1

Millimeter Wave Communications for Future
Mobile Networks

arXiv:1705.06072v1 [] 17 May 2017

Ming Xiao, Senior Member, IEEE, Shahid Mumtaz, Senior Member, IEEE,
Yongming Huang, Senior Member, IEEE, Linglong Dai, Senior Member, IEEE, Yonghui Li, Senior Member, IEEE,
Michail Matthaiou, Senior Member, IEEE, George K. Karagiannidis, Fellow, IEEE, Emil Björnson, Member, IEEE,
Kai Yang, Senior Member, IEEE, Chih-Lin I., Senior Member, IEEE, Amitava Ghosh, Fellow, IEEE

Abstract—Millimeter wave (mmWave) communications have
recently attracted large research interest, since the huge available
bandwidth can potentially lead to rates of multiple Gbps (gigabit
per second) per user. Though mmWave can be readily used
in stationary scenarios such as indoor hotspots or backhaul, it
is challenging to use mmWave in mobile networks, where the
transmitting/receiving nodes may be moving, channels may have
a complicated structure, and the coordination among multiple
nodes is difficult. To fully exploit the high potential rates of
mmWave in mobile networks, lots of technical problems must
be addressed. This paper presents a comprehensive survey of
mmWave communications for future mobile networks (5G and
beyond). We first summarize the recent channel measurement
campaigns and modeling results. Then, we discuss in detail recent
progresses in multiple input multiple output (MIMO) transceiver
design for mmWave communications. After that, we provide an
overview of the solution for multiple access and backhauling,
followed by analysis of coverage and connectivity. Finally, the
progresses in the standardization and deployment of mmWave
for mobile networks are discussed.
Index Terms—Millimeter wave communications, mobile networks, channel model, MIMO beamforming, multiple access,
standardization.

W

I. I NTRODUCTION AND BACKGROUND

ITH the fast development of electronic devices and
computer science, various emerging applications (e.g.,
virtual reality, augmented reality, big data analytics, artificial
intelligence, three-dimensional (3D) media, ultra-high definition transmission video, etc.) have entered our society and
created a significant growth in the data volume of wireless networks. Meanwhile, mobile networks have become
indispensable to our society as a key service for personalcomputing devices. One of the main characteristics of future mobile networks (5G and beyond) is the unprecedented
M. Xiao is with the department of information science and engineering,
School of Electrical Engineering, Royal Institute of Technology, Sweden.
Email: mingx@kth.se; Shahid Mumtaz, is with Campus Universitario de
Santiago, Portugal, Email: smumtaz@av.it.pt; Yongming Huang, is with
Southeast University, China, Email: huangym@seu.edu.cn; Linglong Dai is
with Tsinghua University, China, Email: daill@tsinghua.edu.cn; Yonghui
Li, University of Sydney, Australia, Email: yonghui.li@sydney.edu.au
Michail Matthaiou is with Queen’s University Belfast, UK, Email:
m.matthaiou@qub.ac.uk; George K. Karagiannidis is with Aristotle University
of Thessaloniki, Greece, Email: geokarag@auth.gr; Emil Björnson is with
Linköping University, Sweden, Email: emil.bjornson@liu.se; Kai Yang is with
TongJi University, China, kaiyang@tongji.edu.cn; Chih-Lin I., China Mobile,
China, Email: icl@chinamobile.com; Amitava Ghosh, Nokia, USA, Email:
amitava.ghosh@nokia.com
Ming Xiao would like to thank Mr. Zhengquan Zhang for valuable
discussion and drawing some of the tables.

traffic volumes, with huge area spectral efficiency (hundreds
of bit/s/Hz/km2 ) and the very high throughput per device
(multiple Gbps). For instance, it is predicted that the world
monthly traffic of smartphones will be about 50 petabytes in
2021 [1], which is about 12 times of the traffic in 2016.
In order to meet these requirements, the research and
deployment for the future mobile networks [2]–[4] have
already been launched. Since 2013, the national-level 5G
research organizations and projects (including European Union
(EU) 5GPPP/METIS, China IMT-2020 (5G) Promotion Group,
Korea 5G Forum, and Japan ARIB) have been set up one
after the other to achieve the 2020 technical targets. In
2015, ITU-R officially named 5G systems as IMT-2020,
and released recommendation on its framework and overall
objectives. Currently, Phase-1 of 5G is being standardized
in 3GPP (http://www.3gpp.org/news-events/3gpp-news). Fig. 1
illustrates potential usage scenarios and capabilities of IMT2020 [4]. Note that 5G is envisaged not only to expand and
support diverse usage scenarios and applications that will
continue beyond the current networks, but also to support a
broad variety of new application scenarios, including:
1) Enhanced Mobile BroadBand (eMBB);
2) Massive machine type communications (mMTC);
3) Ultra Reliable Low Latency Communication (URLLC).
It is expected that IMT-2020 can provide the following
eight key performance indicators (KPIs) [4]: greater than
10 Gbit/s peak data rate, 100 Mbit/s user-experienced data
rate, 3x spectrum efficiency, greater than 100 Mbps cell edge
rates, 10 Mbit/s/km2 area traffic capacity, 100x network energy
efficiency, 1 ms over-the-air latency, support for 500 km/h mobility, and 106 km2 connection density [2]. The multiplicative
improvements are measured with respect to IMT-Advanced.
Recently, EU has also launched Beyond 5G research within the
H2020 framework (ICT 2017-09 Call), where a key technology
foundation is to use the millimeter wave (mmWave) bands
from 30 GHz to 300 GHz, and also THz frequency bands.
To achieve the magnificent objectives and visions listed
above, several key enabling technologies have been identified,
such as mmWave communications, massive multiple-input
and multiple-output (MIMO), small cell deployment, fullduplex relaying, D2D communications, interference management techniques, dynamic TDD with self-backhauling and
novel access technologies. Many of these technologies have
complementary benefits and need to be combined to achieve all

2

Enhanced Mobile Broadband
Peak data rate
(Gbit/s)
20

100
IMT-2020
10
1

Area traffic capacity
(Mbit/s/m²) 10
1

100x
Network energy
efficiency

User experienced
data rate (Mbit/s)

10x

0.1

1x

3x

Spectrum
efficiency

1x
350400

Mobility
500 (km/h)

IMT-Advanced
10
105
106
Massive Machine Connectivity density
(devices/km²)
Type
Communications

1
Latency
(ms)

Ultra-Reliable
and Low Latency
Communications

Fig. 1. 5G usage scenarios and key capabilities of IMT-2020, as compared
to IMT-Advanced [2].

the key capabilities of 5G. For example, mmWave communications [4], [5], [7], [9], [12], [13] is widely considered the most
important technologies to achieve 10 Gbit/s peak data rates.
This is because there are a large amount of bandwidth available
in the mmWave bands, and expanding the bandwidth is an
efficient approach to enhance system capacity. In particular,
the channel capacity of an additive white Gaussian noise
channel operating over B Hz is
!
P
C = B log2 1 +
,
(1)
N0 B

where P is the signal power and N0 is the noise power
spectral density [15]. Hence, the capacity increases linearly
with the bandwidth B, if we also let P grow proportionally
to B. Since P is limited by regulations in practice, mmWave
communication is particularly well-suited for scenarios with
good channel conditions, such as short-range small cell access
and line-of-sight backhauling in mobile networks [16]–[19].
Self-bcakhaul, where the same wireless spectrum is shared
between access and backhaul [20], [23], can provide flexible and cost-efficient solutions to overcome the difficulty of
deploying dedicated backhaul, especially in an ultra dense
network (UDN) [24]–[26]. It refers to a set of solutions where
small base stations (BSs) without dedicated backhaul connect
to other BSs that have dedicated backhaul link, by utilizing a
similar radio access technology as the one used by the user
equipments (UEs) to access the network.
The study of mmWave can trace back to more than 100
years ago. For instance, the experiments at wavelengths
as short as 5 and 6 mm were performed by Bose and
Lebedew in the 1890s [27]. As for its application in radio
communications, the mmWave mobile communications
were originally invented in the 1990s and early 2000s,
including system design and channel measurements [28]–
[30]. Due to the fact that the spectral resource below
6 GHz is becoming scaring while abundant bandwidth
is availabe at mmWave band, recently large efforts have

been devoted to mmWave communications research and
mmWave wireless local area networks (WLAN); for example,
IEEE 802.11ad technology operating at 60 GHz, is already
available. The more challenging development of mmWave
mobile communications is ongoing and is what this paper
focuses on. Samsung first achieved 1 Gb/s data transmission
at 28 GHz in May 2013. Google also put substantial
research efforts into mmWave communications. Verizon
has submitted applications to the Federal Communications
Commission (FCC) to obtain special temporary authorization
(STA) to test mmWave communications technology at
28 GHz. T-Mobile is also expected to obtain STA at
28 GHz and 39 GHz. Nokia in collaboration with National
Instruments achieved a peak rate of 15 Gbps using their
proof-of-concept system at 73GHz band in April 2015.
To further promote the development of mmWave mobile
communications, Millimetre-wave evolution for backhaul and
access (MiWEBA) (http://www.miweba.eu/, a joint EU-Japan
project), Beyond 2020 heterogeneous wireless networks
with mmWave small cell access and backhauling (MiWaves)
(http://www.miwaves.eu/), and mmWave based mobile radio
access network for fifth generation integrated communications
(mmMAGIC) (https://5g-mmmagic.eu/) projects have been
initiated by EU. In Japan, to prepare for 2020 Tokyo
Olympic, DOCOMO and Ericsson tested at 15GHz to
reach the rates of 4.5Gbps in outdoor environments and at
70GHz to reach the rates of 2Gbps in indoor environments
(https://www.nttdocomo.co.jp/english/info/media_center/pr/201
5/030203.html). In China, the minister of science and
technology (MOST) has supported a few projects (through
project 863) in mmWave in mobile networks and the
RF chips of 60GHz and 42-48GHz have been produced
(http://www.most.gov.cn/kjbgz/201609/t20160923_127867.htm).
Huawei and China mobile also demonstrated the Ka-band
(26.5–40GHz) mobile access with 20Gbps rates in the Mobile
World Congress 2017.
The mmWave communication has a rich history. Recently
the main research interest on mmWave is shifting from local
area networks to mobile networks. The main focus of this
paper is on development of mmWave mobile communications.
We acknowledge the initial contribution in this field in the
1990s and 2000s including channel measurement and system
design, but we particularly concentrate on its new research
contributions for future mmWave mobile networks. Note that
though there have been many research progresses in the
area, the existing literature mainly focuses on addressing
specific technical challenges. A comprehensive survey, which
organically combines these numerous but disjointed works and
provides the summary of latest research progress, is basically
missing.
Note that references [7], [12]–[14], [16] gave excellent
overviews of some aspects of mmWave mobile networks from
different aspects. Reference [7] is a pioneering overview paper
on the mmWave for mobile networks. Yet, as published in
2013, [7] did not contain the technology development of
very recent years. Moreover, some topics, such as, mmWave
MIMO, multiple-access, backhual and standardization are not
included in [7]. Reference [12] is also an overview paper

3

networks.
10

2

A. Main Technical Challenges
Specific Attenuation (dB/km)

101

100

10-1

10-2

Despite the theoretical potentials for extremely high data
rates, there are several key technical challenges for using
mmWave in mobile networks, including severe pathloss, high
penetration loss, high power consumption, blockage due to
shadowing, hardware impairment, etc. In what follows, we
will give a brief introduction to these topics.
Pathloss: In free-space transmission, the power of the
received signal (outside the Kirchhoff area) can be determined
by Friis transmission formula [7]:
!2
λ
d −n,
(2)
Pr (d) = Pt Gt Gr
4π

where Pt is the transmit power and Gt and Gr are the
antenna gains of the transmitter and receiver, respectively.
10-3
50
100
150
200
250
300Moreover, λ is wavelength, d is the transmission distance,
and the pathloss exponent n equals to 2 in free space. The
Frequency (GHz)
formula in (2) can be also used to, approximately, describe
Fig. 2. Atmospheric and molecular absorption in different frequency bands
the power of the received signal in non-free-space propagation
[35].
as well, by making channel measurements and then finding a
suitable value of n that approximately describes the pathloss
on mmWave 5G paper published in early 2014, which did measurements. The value of n is usually in the range from 2
not include recent progress of the topic. Moreover, topics to 6. There are also refined models, e.g., for cellular networks
of mmWave MIMO and standardization are not included in and n can in some scenarios be smaller than 2 [31].
The wavelength of mmWave signals is much shorter than
[12]. Reference [16] gives a good brief overview of mmWave
for 5G. However, the introduction in [16] does not include conventional microwave communication signals, operating
technical details as a magazine paper. [14] is a recent overview at carrier frequency below 6 GHz. Hence, the pathloss of
paper on signal processing for mmWave MIMO. Yet, reference mmWave signals is much higher than that of microwave
[14] is focused in mmWave MIMO and other topics e.g., signals, if all other conditions including the antenna gains are
recent developments of channel measure/modeling, multiple the same. Although the pathloss of mmWave is generally quite
access, standardization, field test are missing. Reference [13], high, it is feasible to communicate over the distances that are
published in 2014, discussed the mmWave mobile in channel common in urban mobile networks, such as a few hundreds of
modeling, modulation, network architecture. Yet, [13] did meters [7] or even a few kilometers [32]. By using directive
not discuss the topics of mmWave MIMO, standardization, antennas, it has been demonstrated that 10 km communication
multiple access. Different from these papers, we provide such ranges are possible under clean air conditions [32]. If the air
a systematic survey on the main progress and technical content is not clean, the rain attenuation and atmospheric/molecular
of mmWave communications for mobile networks, and further absorption increase the pathloss and limit the communication
discuss some related research issues and challenges. Especially range [33], [34]. The impact of these factors varies with the
we include the recent technical development of related topics, carrier frequency; for instance, the atmospheric and molecular
including the accepted papers of this IEEE JSAC special issue. absorption are shown in Fig. 2 and the rain attenuation is
The rest of the paper is organized as follows. We first give shown in Fig. 3.
Penetration loss: The pathloss discussion assumes line-ofan overview of the key challenges and technical potentials of
mmWave communications in future mobile networks in Sec- sight (LoS) communications, but the high penetration loss is
tion II. Then, we discuss mmWave propagation characteristics compounded in non-line-of-sight (NLoS) scenarios. In indoor
and channel modeling in Section III, and then present MIMO environments, although the penetration losses for clear glass
design for mmWave communications in Section IV. Then, we and dry walls are relatively low for 28 GHz signals (compadiscuss the multiple access technologies, in-band backhauling, rable to microwave bands), the penetration losses for brick
and the coverage performance in Section V. We present the and tinted glass are high for 28 GHz signals (about 28 dB and
progress of the standardization and deployment of mmWave 40 dB), which is much higher than at microwave bands [37],
mobile networks in Section VI. The conclusions are finally [112]. The penetration losses are typically larger at higher
frequencies. Hence, it is difficult to cover inside with mmWave
given in Section VII.
nodes deployed outside and vice-versa due to high penetration
II. K EY C HALLENGES AND T ECHNICAL P OTENTIALS
loss.
High power consumption: In addition to the challenges
Next, we will describe some important challenges and potential gains from using mmWave communications in mobile imposed by high pathloss, (1) shows that the transmit power

4

elsewhere. This idealized radiation pattern, often referred as
the “flat-top model”, was used in [22], [38]–[40] for systemlevel performance analysis. However, in practice, the radiation
patterns are more complicated and implementation-dependent;
the main-lobe gain is not constant and the side-lobe radiation
is non-zero. The effect of side-lobe radiation and the gradual
reduction of main-lobe gain caused by beam misalignment
cannot be ignored. The maximum beamforming gain, which
can be achieved only if the main-lobes of the transmitter
and receiver are perfectly aligned, is rare due to practical
implementation constraints. In 3GPP, a more practical twodimensional directional antenna pattern [41] is adopted, where
the antenna gain G(θ), with respect to the relative angle θ to
its boresight, is given by

Rain Attenuation (dB/km)

102

101

100
0.25 mm/h
2.5 mm/h
12.5 mm/h
25 mm/h
50 mm/h
100 mm/h
150 mm/h
200 mm/hr

10-1

10-2

50

100

150

200

250

300

Frequency (GHz)

Fig. 3. Rain attenuation in different frequency bands [35].

needs to increase with the bandwidth if the signal-to-noise
ratio (SNR) should remain intact [14]. Alternatively, directive
antennas or MIMO technology can be used to direct the
signal power spatially, which leads to an array gain and
latter also provides the flexibility of spatial multiplexing
[36]. MIMO/beamforming is considered essential for mmWave
communications, particularly since the short wavelength at
mmWave frequencies makes it possible to fit many halfwavelength spaced antennas into a small area. MIMO arrays
are normally fully digital for sub 6 GHz systems, where
each antenna requires a dedicated radio-frequency (RF) chain,
including power amplifier (PA), low noise amplifier (LNA),
data converter (ADC/DACs), mixer, etc. However, realizing a
fully digital MIMO implementation at mmWave frequencies
is a non-trivial task, using current circuit design technology
[14]. Having hundreds (or even thousands) of antennas, each
supported by a separate RF chain, requires a very compact
circuit implementation. Moreover, due to the high bandwidth,
the PAs and data converters are expensive and power consuming. Hence, it appears that fully digital mmWave MIMO
implementations are currently infeasible from a cost-efficiency
perspective. This is likely to change in the future, but, in
the meantime, alternative low RF-complexity architectures
have received much attention from the research community.
In particular, hybrid analog/digital architectures are being
considered, where the corresponding signal processing techniques must be redesigned to enable channel estimation and
a good tradeoff between the spectral efficiency and energy
consumption/hardware cost [37].
Narrow beamwidth and side-lobes: To increase the transmission distance for mmWave, an array gain can be obtained
by using directional antennas, MIMO, and beamforming.
Consequently, the beamwidth of mmWave signals is normally
narrow. When modeling the directivity, the radiation patterns
are usually modeled in an idealized fashion, e.g., a constant
large antenna gain within the narrow main-lobe and zero

3



G m 10− 10


G (θ) = 



 G s,





2θ 2
ω

,

|θ| ≤

θm
,
2

θm
≤ |θ| ≤ π,
2

(3)

where ω denotes the half-power (3 dB) beamwidth and θ m is
the main-lobe beamwidth. G m and G s represent the maximum
main-lobe gain and averaged side-lobe gain, respectively.
Eq. (3) models how the signal changes with the beamwidth
and receiving angles. The narrow beamwidth has a two-fold
impact. While the pros will be discussed later, for the cons,
narrow beamwidth leads to higher sensitivity to misalignment
between the transmitter and the receiver, especially in mobile
networks that should support high mobility. The reason for
beam misalignment can be coarsely divided into two categories: 1) Imperfection of existing antenna and beamforming
techniques [42]–[44], such as the analog beamforming impairments, array perturbations, oscillator locking-range based
phase error, and the direction-of-arrival (DoA) estimation errors. Moreover, limited feedback may also cause the transmitter having only partial channel information and thus beaming
misalignment [45]. 2) Mobility of communication UE [46],
[47], which invokes tracking error and system reaction delay.
Hardware impairments and design challenges: In addition to above challenges, practical transceiver hardware are
impaired by phase noise (PN), non-linear PAs, I/Q imbalance,
and limited ADC resolution [48]. These effects limit the channel capacity [49], particularly when high spectral efficiency
is envisioned. On the other hand, it was proved in [50] that
MIMO communication links are less affected by hardware
impairments than single-antenna links.
In mmWave communication systems, mixers are applied for
signal up-conversion at the transmitter and down-conversion at
the receiver using local oscillators to generate carrier signals
operating at the desired carrier frequency. However, due to the
random deviation of the output signal frequency around the
carrier, it is infeasible that both oscillators at the transmitter
and the receiver operate exactly at the same carrier frequency.
Such a mismatch can be described by PN since the frequency
offset yields a random phase difference for the time domain
samples. Due to the high carrier frequency, mmWave communication systems are more sensitive to PN than conventional
ones.

5

Another important hardware impairment in mmWave is
the nonlinear PAs, since it is challenging to provide linear
amplification to a signal with very wide bandwidth. In practice,
each amplifier has a non-linear behavior, e.g., input signals
of large amplitude are clipped and different frequencies are
amplified differently. For the modeling of such non-linear
characteristics, the modified Rapp model [51], is commonly
used to describe the input-output relationship. Denoting Vi
the input voltage level of the PA, the output voltage level
Vo is described by the amplitude modulation to amplitude
modulation (AM-AM),
Vo =

rVi
(1 + (|rVi |/Vs ) 2p )

1
2p

,

(4)

where r is the small signal gain, Vs is the limiting output
amplitude, and p controls the smoothness of the transition from
linear operation to saturated operation. There are also other
models, such as the Saleh model [52].
Moreover, the higher and larger frequency bands of
mmWave communication systems cause many technical challenges in the design of circuit components and antennas.
In [53], authors discussed in depth the challenges against
the design of mmWave CMOS Radios, ranging from the
device-level challenges to the architecture-level challenges. In
particular, phase noise and IQ imbalance also cause severe
technical challenges against realizing mmWave RF circuits
[54]–[56]. In [57], authors have discussed and summarized
the latest research progress on integrated circuits for mmWave
communication systems. Authors have specifically summarized in details a few key technologies, including RF power
amplifiers , mixers, high speed analog-to digital converters, onchip and in-package antennas and 60 GHz voltage-controlled
oscillators.
B. Technical Potentials
Having listed the main challenges, we should not forget the
main reasons for using mmWave communications.
Large continuous unused bandwidth: Compared to microwave communications, one of the major benefits of
mmWave communications is the availability of large bandwidth, though wider bandwidth does not always lead to
higher rates in the noise-limited region [8]. Currently, the
available bandwidth for mobile networks (2G, 3G, 4G and
LTE-Advanced spectrum) is globally smaller than 780 MHz
and each major wireless provider has only a total of about
200 MHz spectrum [7]. This bandwidth is not sufficient for
providing rates of Gbps to multiple devices, since a huge
per-device spectral efficiency would be required. However, in
mmWave bands, there are large chunks of bandwidth available
for future mobile networks. As shown in Fig. 4, in the
mmWave bands, the potentially available bandwidth can be
more than 150 GHz [5], even excluding unfavorable bands
such as the 60 GHz oxygen absorption band (57–64 GHz) and
the water vapor (H2 O) absorption band (164–200 GHz). With
150 GHz of spectrum, a low spectral efficiency of 1 b/s/Hz is
sufficient to deliver a rate of 150 Gbps. Having a low spectral
efficiency simplifies the implementation and makes the end

Water
vapor
absorption
band

24.25GHz

42.5

64
57

76 86
71 81

109.5

164

200

300 GHz

102.2

Oxygen
absorption bands

27.5GHz

28.35

29.1 29.25

Bands proposed
for mobile use

31 31.3 GHz

37 GHz

Potential bands
for mobile use

38.6

Other bands
raised in NOI

40

42 42.5 GHz

Absorption bands

Fig. 4. Spectrum usage in mmWave bands.

.
performance less affected by hardware impairments. The large
unused frequency bands have therefore already attracted lots
of interest. For instance, in October 2003, the FCC announced
that the 71–76 GHz, 81–86 GHz, and 92–95 GHz frequency
bands (collectively referred as the E-band) will be available for
ultra-high-speed data communication including point-to-point
WLAN, mobile backhaul, and broadband Internet access. A
total of 12.9 GHz bandwidth is available in the E-band (6090 GHz). More recently, in July 2016, FCC dedicated large
bandwidths in mmWave bands for future cut-edge wireless
communications, namely, 64-71 GHz unlicensed bands (plus
previous 57-64 GHz) and 27.5-28.35 and 37-40 GHz licensed
bands.
Short wavelength and narrow beamwidth: Contrary to
signals at sub-6 GHz bands, the mmWave signal has much
shorter wavelength, which facilitates packing a large number
of antennas into an array of compact size [6], [7]. This
greatly expands the application range of large-scale antenna
communications in the future mobile networks [9], [10]. At
the same time, when having many antenna elements, the
beamwidth is narrow [11]. The positive side of this property is
the higher security against eavesdropping and jamming, and
a larger resilience against co-user interference. This implies
that the spectrum can be reused frequently in space, so many
interfering point-to-point MIMO systems (or multiuser MIMO
systems) can be deployed in a limited spatial region.
III. C HANNEL M EASUREMENTS AND M ODELING
A. Millimeter Wave Measurement Campaigns
Since the wavelength of mmWave bands is far shorter than
in microwave bands below 6 GHz, the parameters for radio
channel models will be quite different. Thus, understanding
the mmWave propagation characteristics is the first task to
design and develop mmWave communication systems. In
general, parameters such as pathloss, delay spread, shadowing, and angular spread are used to characterize the radio
propagation, which can be obtained through analyzing the data
collected by various channel measurement campaigns in different environments. Extensive channel measurement campaigns

6

TABLE I
S UMMARY OF MM WAVE MEASUREMENT CAMPAIGNS
Frequency
(GHz)
10
11
15

16
17
26
28

37
38
40
41
55
57
60

72
73
81-86
82
10
11, 16, 28, 38
73
26

Scenario

Site

Parameters

Ref.

Street canyon
Outdoor to indoor
Urban outdoor
Street canyon
Street canyon
Office
Airport
Outdoor to indoor
Urban outdoor
Outdoor to indoor
Urban steet and Sidewalk
Street canyon
Street canyon
Open square
Airport
Indoor (lab) and outdoor urban
Dense urban
Indoor and outdoor to indoor
Dense urban
Outdoor to indoor
Office
Laboratory
Station and airport
Urban
Urban steet and Sidewalk
Urban (Campus)
Urban (Campus)
Urban (Campus)
Indoor (lab)
Street canyon
Urban (street)
Urban (street)
Street canyon
Street canyon
Office
Shopping mall
Outdoor to indoor
Indoor (lab)
Urban (Campus)
Hospital
Outdoor courtyard and in vehicle
Street canyon
Street canyon
Urban
Office
Dense urban
Dense urban
Office
Roof-to-street
and street canyon
Street canyon
Office
Indoor hotspot
Indoor
rural
Indoor

Berlin
Belfort
Ishigaki
Helsinki
Stockholm

Large-scale parameters, time variance, and frequency dependence
Penetration losses, delay spread, and time variation
Pathloss, RMS delay spread, shadow, and power delay profile
Pathloss, delay spread, and angular spread
Pathloss and delay spread
Pathloss, directional spread, and delay spread
Pathloss, delay spread, and angular spread
Penetration loss and RMS delay spread
Pathloss
Penetration losses, delay spread, and time variation
Pathloss
Delay spread, angular spread, and pathloss
Large-scale parameters, time variance, and frequency dependence
Pathloss, delay spread, and angular spread
Pathloss, delay spread, and angular spread
Penetration loss, pathloss, reflectivity, and AoA
Pathloss, RMS delay spread, shadow, PDP, AoA, and AoD
Penetration loss and reflection coefficients
Outage
Excess loss, received power
Pathloss, RMS delay spread, and shadow
RMS delay spread and PDP
Pathloss and shadow
Pathloss, RMS delay spread, angle spread, AoA, and AoD
Pathloss
Pathloss, RMS delay spread, and PDP
Outage statistics
Pathloss, RMS delay spread, shadow, PDP, AOA, outage statistics
Penetration loss, reflectivity
Large-scale parameters, time variance, and frequency dependence
Pathloss, fading envelope, coherence bandwidth
Mean delay, delay spread, delay interval and delay window
Delay spread, angular spread, and pathloss
Pathloss and delay spread
Pathloss, directional spread, and delay spread
Direction
Penetration loss and RMS delay spread
Penetration loss
Pathloss, RMS delay spread, and PDP
Pathloss, delay spread, and PDP
Pathloss, RMS delay spread, PDP, and AoA
Pathloss
Pathloss and delay spread
Angular, RSS
Penetration loss, delay spread, PDP
Pathloss, delay spread, shadow, PDP, AoA, and AoD
Outage
Pathloss, RMS delay spread and shadow
Frequency response, impulse responses, and delay

[58]
[58]
[59]
[58]
[58]
[58]
[58]
[58]
[60]
[58]
[61]
[58]
[58]
[58]
[58]
[62]
[63]–[65]
[66]
[67]
[68]
[69], [70]
[71]
[72]
[73], [74]
[61]
[75]
[76]
[77]
[62]
[58]
[78]
[79]
[58]
[58]
[58]
[58]
[58]
[62]
[75]
[80]
[81]
[82]
[83]
[84]
[85]
[65], [86]
[67]
[69], [70]
[87]

Large-scale parameters, time variance, and frequency dependence
Delay and angular spread
Large-scale parameters, pathloss model
Power delay, azimuth, elevation profile
pathloss model
pathloss, shadow fading, and coherence bandwidth

[58]
[58]
[88]
[89]
[90]
[91]

Helsinki
Stockholm
Tokyo
Belfort
Tokyo
Helsinki
Berlin
Helsinki
Helsinki
Dallas
New York
New York
New York
Göteborg
New York
Seoul
Daejeon
Tokyo
Austin
Austin
Austin
Berlin
London
Oslo
Helsinki
Stockholm
University of Bristol
Stockholm
Austin
Japan
Berlin
Berlin
Aachen
New York
New York
New York
New York
Otaniemi
and Kaisaniemi (Helsinki)
Berlin
CEA-Leti
Oulu
Jinan, China
Virginia
BeiJing

7

TABLE II
S UMMARY OF MM WAVE MEASUREMENT RESULTS ON DIRECTIONAL (D) AND OMNIDIRECTIONAL (O) PATHLOSS , DELAY SPREAD AND SHADOWING
Freq.
(GHz)
11
11
11
28

Environment

Scenario

Site

Macro cellular
Micro cellular
Outdoor hotspot
Outdoor cellular

LoS/NLoS
LoS
LoS
LoS/NLoS

28

Dense urban

LoS/NLoS

28

Office

LoS/NLoS

28

Office

LoS/NLoS

28

Office

LoS/NLoS

28

Office

LoS/NLoS

28
28
28
28
28

Station
Airport
Urban
Urban street
Urban street

LoS/NLoS
LoS/NLoS
NLoS
LoS/NLoS
LoS/NLoS

28

Urban street

LoS/NLoS

38
38
38
38
38
38
38
38
55
60
60
60
60
72

Outdoor Cellular
Outdoor Cellular
Outdoor Cellular
Outdoor Cellular
Outdoor Cellular
Outdoor Cellular
Outdoor Cellular
Outdoor Cellular
Urban (street)
Hospital
Hospital
Hospital
Street canyon
Indoor

LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS
LoS
LoS
LoS/NLoS
NLoS

73

Office

LoS/NLoS

73

Office

LoS/NLoS

73

Office

LoS/NLoS

73

Office

LoS/NLoS

73
73
73
73
73

Outdoor cellular
Outdoor cellular
Outdoor cellular
Outdoor cellular
Dense urban

LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS
LoS/NLoS

73

Dense urban

LoS/NLoS

Indoor
Indoor
rural
Hall

LoS/NLoS

Ishigaki
Ishigaki
Ishigaki
New York
(Manhattan)
New York
(Manhattan)
New York
(Brooklyn)
New York
(Brooklyn)
New York
(Brooklyn)
New York
(Brooklyn)
Seoul
Seoul
Daejeon
Daejeon
New York
(Manhattan)
New York
(Manhattan)
Austin
Austin
Austin
Austin
Austin
Austin
Austin
Austin
London
Japan
Japan
Japan
Berlin
New York
(Brooklyn)
New York
(Brooklyn)
New York
(Brooklyn)
New York
(Brooklyn)
New York
(Brooklyn)
New York
New York
New York
New York
New York
(Manhattan)
New York
(Manhattan)
Oulu
JiNan
Virginia
BeiJing

10
11/16/28/38
73
26

LoS/NLoS
LoS

Tx/Rx Antenna
(Height (m) etc.)
(O), Tx: 28, Rx: 3
(O), Tx: 8, Rx: 3
(O), Tx: 3, Rx: 3
(D), 24.5-dBi Tx/Rx

PL exp.
2.6/3.4
2.7
2.2
2.55/5.76

(O), Tx:7; 17, Rx: 1.5

2.1/3.4

(D), V-V, 15-dBi, Tx: 2.5, Rx: 1.5

1.7/4.5

(O), V-V, 15-dBi, Tx: 2.5, Rx: 1.5

1.1/2.7

(D), V-H, 15-dBi, Tx: 2.5, Rx: 1.5

4.1/5.1

(O), V-H, 15-dBi, Tx: 2.5, Rx: 1.5

Shadowing
(dB)
4.4/6.7
2.5
2.5
8.66/9.02

[59]
[59]
[59]
[7], [63]

3.6/9.7

[65]

2.6/11.6

[69]

1.7/9.6

[69]

8.0/10.9

[69]

2.5/3.6

3.0/9.4

[69]

(D), Tx: 8, Rx: 1.5
(D), Tx: 8, Rx: 1.5
(O), 24.5-dBi Tx:15
(O), Tx:15, Rx: 1.6
(O), Tx:15, Rx: 1.6

2.15/4.06
2.17/3.55
3.53
1.90/3.15
1.81/3.03

1.19/10.67
1.33/7.61
6.69
0.63/22.09
2.05/17.99

[72]
[72]
[73]
[74]
[74]

(O), Tx:15, Rx: 1.6

1.87/2.97

1.74/15.92

[74]

(D), 25-dBi TX and 13.3-dBi RX
(D), 25-dBi TX and 13.3-dBi RX
(D), 25-dBi TX and 13.3-dBi RX
(D), 25-dBi TX and 13.3-dBi RX
(D), 25-dBi TX and 25-dBi RX
(D), 25-dBi TX and 25-dBi RX
(D), 25-dBi TX and 25-dBi RX
(D), 25-dBi TX and 25-dBi RX
(D), Tx: 10, Rx: 2
Tx (O): 1.31, Rx (D): 2.04
(O), Tx : 2.30, Rx: 2.04
(O), Tx: 0.75, Rx: 1.58/2.29
(O)
20-dBi Tx/Rx: 1.61

2.13/2.54
2.16/2.52
2.03/2.40
2.74/2.97
2.25/3.29
2.38/3.20
2.01/2.70
2.99/4.17
3.6/10.4
1.34
1.86
2.23
2.02/3.06

10.1
17.3
4.8
17.5
13.5
15.1
5.3
16.5

8.14/7.74
8.78/7.82
5.31/5.27
12.46/11.16
6.51/11.63
14.12/8.97
6.56/5.54
13.92/8.90

[77]
[77]
[77]
[77]
[77]
[77]
[77]
[77]
[78]
[80]
[80]
[80]
[83]
[85]

(D), V-V, 15-dBi, Tx: 2.5, Rx: 1.5

1.7/5.3

3.3/13.3

2.1/15.6

[69]

(O), V-V, 15-dBi, Tx: 2.5, Rx: 1.5

1.3/3.2

1.9/11.3

[69]

(D), V-H, 15-dBi, Tx: 2.5, Rx: 1.5

4.7/6.4

9.0/15.8

[69]

(O), V-H, 15-dBi, Tx: 2.5, Rx: 1.5

3.5/4.6

6.3/9.7

[69]

(D), Tx: 7, Rx: 4
(D), Tx: 7, Rx: 2
(D), Tx: 17, Rx: 4
(D), Tx: 17, Rx: 2
(O), Tx:7; 17, Rx: 2

2.68/4.72
2.77/4.64
2.52/3.90
2.32/3.76
2.0/3.3

6.79/9.52
5.33/8.32
4.00/10.81
2.71/8.81
5.2/7.6

[86]
[86]
[86]
[86]
[65]

(O), Tx:7; 17, Rx: 4.06

2.0/3.5

4.2/7.9

[65]

Tx: 2, Rx: 1.6
Tx: 2.6, Rx: 1.45
Tx:110, Rx: 1.6-2
Tx: 2.5, Rx: 2

1.4/3.3

1.4/2.6

[88]
[89]
[90]
[91]

2.16/2.75

Delay spread
(ns)

4.1/18.4

12.8/18.7

22.29

6.7
6.3
3.8

21.2/10.3

17.4/30.4
9.1/10.2/10.6/8.5
16.08

1.7/6.7
0.098

Ref.

8

[58]–[87], covering potential mmWave mobile communication
bands, like 10, 28, 38, 60, and 82 GHz, have already been
performed. Table I summarizes the performed measurement
campaigns, where in particular NYU WIRELESS and the EU
mmMAGIC project make contributions.1 The researchers from
NYU WIRELESS have performed many measurements at 28,
38, 60, 72 and 73 GHz since year 2011, and obtained abundant
measurement results. Based on various measurement results
(including those from NYU WIRELESS) and ray tracing
analysis, 3GPP [21], [99] also provides new modeling features
like: i) dynamic LoS/non-LoS (NLoS) blockage 2 , spatial
consistency and penetration modeling, ii) extension of power
delay/angle profiles, and iii) the pathloss model based on onemeter reference distance. The EU mmMAGIC project plans
more than 60 single-frequency measurement campaigns, covering eight frequency bands from 6 to 100 GHz, under typical
environments and scenarios, including urban micro-cellular
(UMi) (street canyon, open square), indoor (office, shopping
mall, airport), outdoor-to-indoor (O2I) and two scenarios with
very high user densities (stadium and metro station) [58].
1) Pathloss and Shadowing: Pathloss and shadowing are
the two most important large-scale characteristics of the radio
channel, which have been reported for various environments
both in LoS and NLoS cases. It is common to determine
the pathloss exponent that best fits the measurements to Friis
transmission formula in (2), while the shadowing parameters
are used to model the random deviations from this model.
Table II summarizes the main mmWave measurement results
for directional and omnidirectional pathloss and shadowing.
2) Power Delay Profile and Delay Spread: The shape of
power delay profile (PDP) in measurements is a single or superposition of multiple exponentially decaying spectrums. The
delay spread denotes an extent of the multipath power spread
over the PDP, which is an important parameter that determines
the inter-symbol interference in single-carrier transmission
and the frequency-flatness of the subcarriers in multi-carrier
transmission. In Table II, we also summarize the delay spread
of various measurement results.
B. Millimeter Wave Channel Modeling
Channel models are important for evaluating and analyzing
the performance in system-level simulations. For mmWave,
some recent channel modeling results are reported in [92]–
[98]. For more general mobile and wireless networks, several
channel models have proposed continuously, which are illustrated in Fig. 5. To give a historical perspective, we give a
brief introduction as follows (even though some models are
not for mmWave).
1) 3GPP Spatial Channel Model and SCM-Extended: The
3GPP Spatial Channel Model (SCM) [41] was proposed in
2003 and supports six delay paths with 5 MHz bandwidth in
the 2 GHz frequency band under three scenarios: Suburban
1 Note that there are a lot of excellent measurement campaigns and results.
For space limits, we can only list a part of the existed literatures here. The
results, especially before 2012, are mostly omitted.
2 COST 259 also include the LoS/NLoS blockage probability model.

2003

3GPP-SCM

2004

2005

SCM-E

WINNER-I

COST 273

2006
WINNER-II

2007

2008
IMT-Advanced

2009

IEEE 802.11ad

WINNER+

2010

2011
COST 2100

2012

2013
QuaDRiGa

2014
METIS

3GPP 3D

2015

2016

MiWEBA

mmMAGIC

2017
Year

Fig. 5. Available channel models for mobile and wireless networks [58].

Macro (SMa), Urban Macro (UMa), and UMi. The SCMExtended model [99] further extended the SCM, by supporting
bandwidths of up to 100 MHz.
2) WINNER I/II/+ Model: As the EU flagship mobile
technological projects for 4G, the WINNER I/II/+ projects
developed several channel models for mobile networks. The
WINNER I model [100] is an antenna-independent model, in
which different antenna configurations and different element
patterns can be used. In order to support more scenarios, such
as outdoor-to-indoor and indoor-to-outdoor, and elevation in
indoor scenarios, the WINNER II model [101] was developed. It includes scenario-dependent polarization modeling,
and thus improves the accuracy for cross-polarized MIMO
antennas. The parameter tables were reviewed and additional
measurements were done in order to cover the complete 1–
6 GHz frequency range. As a major upgrade of the WINNER
II model, the WINNER+ model [102] supports 3D propagation
effects. WINNER II and WINNER+ models are also antenna
independent since they are based on double-directional channel
representations.
3) 3GPP 3D Model [103], [104]: This model is defined
in the 2 GHz band at a relatively small bandwidth of 10 MHz.
It has consolidated parameters for the two most commonly
used scenarios: UMa and UMi, which are further split into
LoS, NLoS and O2I propagation. The core part of this model,
i.e., the small-scale fading (SSF) model, is identical to the
WINNER+ model. Thus, the same parameters can be used
and similar functionality is provided. More recently, 3GPP
TR38.900 [98] summarizes the recent channel modeling re-

9

sults of 3GPP for the band above 6 GHz and up to 100 GHz.
The models in [98] are comprehensive including street canyon,
open area, rooftop, indoor, backhaul, D2D/V2V and stadium
etc.
4) COST 273/2100 Model: The COST 273 model [105]
was an evolution of the earlier COST 259 channel model
towards mobile broadband by using MIMO technologies. The
model is based on the concept of geometrically located multipath clusters in 2D propagation environment to model the interrelationship between Angle-of-Arrivals (AoAs) and Angleof-Departures (AoDs). This concept is effective to keep spatial
consistency, and can evaluate the performance of MIMO
beamforming and multi-cell transmission more accurately. As
an evolution of the COST 273 model, the COST 2100 model
[106] used visibility regions (VRs) introduced in COST 259 to
model the scenario-variation. These VRs make the evaluation
of multi-cell and heterogeneous transmissions more practical
by considering the VR of BSs from each UE. This model also
extends the multipath clusters to 3D propagation environments.
5) QuaDRiGa Model [107]: As an open source implementation of the 3GPP-3D channel model, the QuaDRiGa
channel model is further extended with the features of spatial
consistency (to accurately evaluate the performance of massive MIMO) and multi-cell transmissions by exploiting the
approach in SCM-E and COST 273.
6) IEEE 802.11ad Model [108]: This model was developed
in 2010 to support indoor short-range communications, such
as in offices and homes using 60 GHz unlicensed band. The
model is quasi-deterministic (QD): specular components, such
as the LoS path and single and double bounce reflections are
modeled deterministically in 3D propagation environments,
while other contributions are modeled stochastically as random
components in the cluster. One of the important features is the
support for blockage.
7) MiWEBA Model [109]: The model is an extension of
the IEEE802.11ad channel model towards outdoor access,
backhaul/fronthaul, and device-to-device (D2D) scenarios. The
approach is QD, where specular components are modeled deterministically, while other components are modeled stochastically. In this way, beamforming and path-blocking models can
be supported. Furthermore, the first 60 GHz pathloss model in
an UMi environment was developed in MiWEBA. The effect
of the ground reflection paths traveling closely in space to the
LoS path has been found to be of high significance.
8) IMT-Advanced Model [110]: This model consists of
a primary module and an extension module (not specific
to mmWave), where the former is based on the WINNER
II channel model, while the latter enhances the support for
variable BS antenna heights, street widths, and city structures.
9) METIS Model [111]: This model consists of a mapbased (deterministic) model, a stochastic model, and a hybrid
model as a combination of both. The stochastic model extends
the WINNER+ and 3GPP-3D models to support 3D shadowing
maps, mmWave parameters, direct sampling of the power
angular spectrum, and frequency dependent pathloss models.
Based on extensive measurement campaigns, lists of channel
parameters for <6 GHz and 50 to 70 GHz bands are available.

10) mmMAGIC Model [58]: This is a statistical channel
model for link- and system-level simulations that is designed
for the entire frequency range from 6 to 100 GHz for a large
variety of scenarios. The model uses the theoretical approach
of the existing 3GPP 3D channel model. However, it extends
it in the following ways: i) the spatial accuracy regarding
path and sub-path distributions is substantially improved, ii)
a realistic non-uniform distribution of sub-path amplitudes is
included, iii) sub-paths can be modeled using spherical waves;
iv) there is consistency over the frequency range (6−100 GHz),
v) there is frequency-dependent antenna models; vi) providing
continuous variations over time, vii) mmWave-specific random
blockage, clustering and scattering objects are being modeled,
and viii) the reflection from the ground or the floor is modeled.
11) 3GPP-like 5G Model [22], [112]: In [112], the outdoor
model is established for the bands from 6 GHz to 100 GHz.
An initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells and urban macrocells,
and 2) a baseline model for incorporating pathloss, shadow
fading, LoS probability, penetration, and blockage models
for the typical scenarios. Various processing methodologies
such as clustering and antenna decoupling algorithms are also
included in [112]. In [21], [22], the indoor model is established
for office and shopping mall environments. The measurement
results show that the smaller wavelengths introduce an increased sensitivity of the propagation models to the scale of
the environment and show some frequency dependence of the
pathloss as well as increased occurrence of blockage.
IV. MIMO D ESIGN FOR MM WAVE C OMMUNICATIONS
While conventional microwave communications, operating
below 6 GHz, can cover many users in wide coverage areas,
mmWave communications mainly provide local-area coverage
and thus feature fewer users. Moreover, there is limited
scattering in outdoor mmWave communications, in contrast
to the rich scattering in conventional microwave communications [7]. Due to these differences, mmWave MIMO systems
have different constraints and requirements, requiring different
transceiver designs. In this section, we first describe hardware
architectures for mmWave MIMO systems, for the use in mobile networks. We discuss how to design the signal processing
techniques, including channel estimation, channel-tracking,
and precoding/combining for millimeter MIMO systems.
A. MIMO Architectures
The conventional MIMO system is fully digital, where all
the signal processing techniques are performed at baseband
as shown in Fig. 6 (a) [36]. As explained in Section II,
for mmWave communication with many antennas and high
bandwidth, the conventional fully digital MIMO architecture
requires many energy-intensive RF chains, leading to an unaffordable energy consumption and hardware cost. Therefore,
although the fully digital architecture most likely will be
available at some point in the future, alternative architectures
are required for emerging mmWave mobile networks.
To reduce the implementation complexity, a fully analog
architecture has been adopted in indoor mmWave communications, such as 60 GHz WLAN [114]. As shown in Fig. 6

10

RF chain

RF chain
Digital signal
processing

RF chain

(a)

Digital signal
processing

RF chain

Analog
circuit

(b)

RF chain
Dimensionreduced digital
signal processing

Analog
circuit
RF chain

(c)
Fig. 6. MIMO architectures: (a) fully digital architecture; (b) fully analog
architecture; (c) hybrid architecture.

(b), only one RF chain is employed to transmit a single
data stream, and the analog circuit (e.g., realized by analog
phase-shifters) is utilized to partially adjust the signals (e.g.,
phases of signals) to achieve an array gain. The advantage
of the fully analog architecture is that it only requires one
RF chain, leading to quite low hardware cost and energy
consumption [114]. However, since the analog circuit can
only partly adjust the signals, it is hard to adjust the beam
to the channel conditions and this leads to a considerable
performance loss [14], particularly for mobile users. In addition, fully analog architecture can only support single-stream
transmission, which cannot achieve the multiplexing gain to
improve spectral efficiency [14].
New MIMO architectures need to be designed to balance
between the benefits of fully digital and fully analog architectures. The hybrid analog-digital architecture is the key
solution [122]–[124]; the hybrid architecture was agreed to be
deployed in future 5G systems at the 3GPP RAN1 meeting
in June 2016 [125], as described later. The hybrid architecture
can be considered an extension of the fully analog architecture

to the multi-stream scenario. As shown in Fig. 6 (c), the key
idea is to divide the conventional digital signal processing
(e.g., precoding and combining) of large size into two parts:
a large-size analog signal processing (realized by analog
circuits) and a dimension-reduced digital signal processing
(requiring a small number of RF chains). Since there is
often only a small number of effective scatterers at mmWave
frequencies, each user has a MIMO channel matrix with
low rank [7]. Hence, the optimal number of data streams is
generally much smaller than the number of antennas. Since the
number of streams determines the minimum required number
of RF chains, this number can be significantly reduced by
the hybrid architecture, leading to reduced cost and energy
consumption.
The analog circuits of the hybrid architecture can be implemented by different circuit networks, leading to different
hardware constraints [14], [115], [116]. Next, we describe
the three typical implementation networks that are illustrated
in Fig. 7. The choice of architecture affects not only the
signal processing design but also the performance of mmWave
MIMO systems.
N1) Fully-connected network with phase-shifters: In this
network, each RF chain is connected to all antennas via phaseshifters, as shown in Fig. 7 (a) [117]. Hence, a highly directive
signal can be achieved by adjusting the phases of transmitted
signals on all antennas [117]. By employing such a network,
all elements of the analog precoder/combiner have the same
fixed amplitude.3 Difficult factors in the implementation can be
the addition of the analog signals at each antenna and selection
of a collection of phase-shifts that are suitable over the entire
bandwidth.
N2) Sub-connected network with phase-shifters: In this
network, each RF chain is only connected to a subarray
via phase-shifters, as shown in Fig. 7 (b) [113], [121]. Due
to this limitation, for each RF chain, only the transmitted
signals on a subset of antennas can be adjusted. Therefore,
compared with N1, the achieved array gain and directivity
is reduced proportionally to the number of subarrays [113].
However, this network might be preferred in practice, since
there is no need to add analog signals at the antenna inputs
and the number of phase-shifters required in this network
is also significantly reduced. Moreover, it has been shown
to achieve the performance close to that of N1 [121]. This
network imposes two hardware constraints [121]: i) the analog
precoder/combiner should be a block diagonal matrix; and
ii) All the nonzero elements of the analog precoder/combiner
have the same fixed amplitude.
N3) Lens antenna array: An alternative quite different from
the networks discussed above is to utilize a lens antenna array,
as shown in Fig. 7 (c) [126]. The lens antenna array (a feed
antenna array placed beneath the lens) can realize the functions
of signal emitting and phase-shifting simultaneously [126].
3 Here we assume the resolution of phase-shifter is sufficiently high, so that
the phase of transmitting signal can be arbitrarily adjusted. In practice, the
phase-shifters with finite resolution will incur phase noise and degrade the
performance of hybrid precoding and combing. In this case, how to design
the signal processing techniques is an interesting topic of future research, and
some initial works can be found in [118]–[120].

11

Beam

Beam

(a)

(b)

Fig. 8. Beam-training: (a) Wide beamwidth; (b) Narrow beamwidth.
Phase shifters

Phase shifters

(a)

(b)
Lens antenna array

Switches

(c)
Fig. 7. Analog circuit with different networks: (a) Fully-connected network
with phase-shifters (N1); (b) Sub-connected network with phase-shifters (N2);
(c) Lens antenna array (N3).

It can concentrate the signals from different propagation
directions (beams) on different feed antennas. As the scattering
for outdoor mmWave communications is not rich [7], the
number of effective propagation paths is usually limited, and
the channel power will be concentrated on only a small number
of beams. Therefore, the selecting network can be used to
significantly reduce the MIMO dimension as well as the
number of RF chains without major performance loss [126].
With careful design, the lens antenna array can excite several
orthogonal beams spanning the whole space. If the directions
of channel paths coincide with the directions of the orthogonal
beams, an array gain similar to N1 can be achieved4 [126].
Moreover, compared with phase-shifter network (including
a large number of phase-shifters, power splitters/combiners,
and signal/control lines) in N1, the hardware cost and energy
consumption incurred by lens antenna array in N3 is relatively
low [128], [251]. Essentially, the lens antenna array plays
the role of a discrete Fourier transform (DFT).5 Therefore,
in this network, each column of the analog precoder/combiner
restricts to a DFT column [126].
B. Channel Estimation with Hybrid Architecture
Channel state information (CSI) is essential to benefit from
the array gain provided by multiple antennas. Acquiring CSI
is particularly challenging in mobile networks, where the
channels can change rapidly. With a fully digital receiver, the
4 If not, power leakage will happen, leading to some performance loss after
the selecting network [127].
5 It is worth pointing out that mathematically, N3 is equivalent to the
structure based on butter matrix proposed in [129], which aims to improve the
performance of antenna selection for microwave MIMO with high correlation.

use of pilot transmission is the most efficient way to acquire
CSI [6], [130]. The channel estimation is more complicated
in a hybrid mmWave architecture, since we cannot extract the
actual received signals on all antennas simultaneously. We will
now discuss alternative methods for acquiring CSI in a hybrid
architecture.
To exemplify the signal processing, we consider a singleuser multi-stream system. The transmitter employs NT antenT RF chains, the receiver employs N antennas
nas and NRF
(
) R
T , NR
R
and NRF RF chains, and ND ≤ min NRF
RF parallel data
streams are transmitted from the transmitter to the receiver. In
this subsection, we mainly consider narrowband systems. Note
that for broadband systems, the analog circuit is fixed for the
whole bandwidth. As a result, the analog signal processing
(e.g., precoding) cannot be adaptively adjusted according to
different frequencies. This will lead to more challenges in
signal processing design, which requires future investigation,
but some pioneering works can be found in [137], [140]. The
narrowband system model of the hybrid architecture can be
presented as [117]
y = W H HFs + W H n,

(5)

where s and y both of size ND × 1 are the transmitted and
received signal vectors, respectively, H of size NR × NT is the
mmWave MIMO channel, which can be modeled as described
in Section III, n of size NR × 1 is the noise vector. F = FA FD
of size NT × ND is the hybrid precoder, where FA of size
T is the analog precoder and F of size N T × N is
NT × NRF
D
D
RF
the digital precoder. Similarly, W = WA WD of size NR × ND
R is the
is the hybrid combiner, where WA of size NR × NRF
R
analog combiner and WD of size NRF × ND is the digital
combiner.
The precoder and combiner matrices should be selected
based on the current channel realization H, but estimating
this matrix is non-trivial [141]. Firstly, due to the lack of
antenna gain before the establishment of the transmission link,
the SNR for channel estimation can be quite low. Secondly,
the number of RF chains in the hybrid architecture is usually
T  N and
much smaller than the number of antennas (i.e., NRF
T
R  N ), so we cannot simultaneously obtain the sampled
NRF
R
signals on all receive antennas. As a result, the traditional
channel estimation schemes [138], [139] requiring the sampled
signals on all antennas will involve unaffordable pilot overhead
in a hybrid architecture. To solve this problem, two dominant
categories of channel estimation schemes have been proposed
in the mmWave literature.
The key idea of the first category is to reduce the dimension
of channel estimation problem, by dividing it into two steps.

12

In the first step, it performs the beam-training between the
transmitter and receiver to obtain the analog precoder FA and
analog combiner WA . In the second step, the effective channel
matrix WAH HFA in the analog domain is estimated by classical
algorithms, such as least squares (LS) [139]. Note that the size
of WAH HFA is ND × ND , which is much smaller than that of
the original channel matrix (i.e., ND  NT, NR ). Therefore, the
pilot overhead in the second step is relatively low, which is
proportional to ND . The remaining difficulty lies in how to
design the efficient beam-training scheme to find the optimal
FA and WA .
To achieve this goal, two primary approaches have been
proposed. The first one is to extend the traditional single-beam
training schemes standardized in IEEE 802.11ad/802.15.3c
to multi-beam training [47], [142]. For example, the singlebeam training scheme in IEEE 802.11ad consists of three
phases [142]: i) Sector level sweep (SLS): the wide beamwidth
is considered at first, as shown in Fig. 8 (a). The transmitter
and receiver try all possible wide beam pairs. With the channel
feedback to indicate the largest received SNR [143], [144], the
best beam pair (according to some criterion) can be selected
for the next phase; ii) Beam-refinement protocol (BRP): the
narrow beamwidth is considered as shown in Fig. 8 (b), and
the beams are trained in a similar way within the previous
selected wide beam pair; iii) Beam tracking: a periodic beamrefinement is performed for the time-varying channels. Repeating such procedure above for each RF chain to select its
corresponding beams, we can obtain the optimal FA and WA .
This hierarchical search could significantly reduce the training
overhead, but its performance heavily depends on the designed
training beam codebook. For the fully analog architecture,
forming a wide beam usually requires the deactivation of some
antennas and thus reduces the total transmit power. While for
the hybrid architecture, it is possible to design wide beam
without reducing the transmit power [145], [146]. Another
scheme can be found in [164], where a novel codebook-based
beam-training together with the following hybrid precoding
design was proposed. Specifically, an RF codebook, denoted
as FC B , is used to specify the possible set of RF beamforming vectors by considering the practical limitation of phaseshifters. Then, the original hybrid precoding problem is transformed into a joint codeword selection and precoding design
problem, which is essentially a group sparsity constrained
optimization problem and only requires effective channels
HFC B . Based on that, a beam-training procedure is proposed
to obtain effective channels with less signaling feedback by
utilizing the beam-domain sparse property of mmWave channels, and efficient algorithms are developed for maximizing
both the spectral efficiency and the energy efficiency. The
second approach is to employ algorithms developed from
machine learning to realize the beam-training, since it can be
considered as a combinatorial problem with a finite number
of beams to be searched. For example, in [147], a tabu search
(TS)-based beam-training scheme is proposed. It first selects
an initial beam pair, and defines the neighbors of this pair
(e.g., only one RF chain changes its beam and the others keep
fixed). Then, the training procedure is only executed within
the neighbors with much smaller size to find a pair which:

i) enjoys the best performance; ii) is not the tabu beam pair
according to some criteria. After a small number of iterations,
it can obtain FA and WA with satisfying performance. Other
algorithms, such as local search algorithm [148], can be also
used. They can usually significantly reduce the pilot overhead,
but the robustness cannot be guaranteed, since these algorithms
are performed in a random way.
The second category of channel estimation schemes is to
exploit the sparsity of mmWave MIMO channels. Instead of
estimating the effective channel matrix WAH HFA of small size,
it can directly obtain the complete channel matrix H with
low pilot overhead. To explain the basic idea, we consider
the simplest case where both the transmitter and receiver use
one RF chain for channel estimation6 [141]. In time slot m,
the transmitter uses a hybrid precoder fm of size NT × 1 to
transmit the pilot s m to the receiver. The received pilot ym at
the receiver using a hybrid combiner wm of size NR × 1 can
be presented as
√ H
H
Hfm s m + wm
n
ym = ρwm

√ T
H
H
= ρ fm ⊗ wm vec (H) + wm
n.
(6)

Note that the mmWave MIMO channel matrix H can be well
approximated by the extended virtual channel model with
sufficiently quantized AoAs/AoDs [141]. Therefore, vec (H)
can be rewritten as vec (H) = AD hb where hb = vec H̃b of
size G2 × 1 is a sparse channel vector, and G is number of
quantized AoAs/AoDs. The position of each nonzero element
in hb indicates the AoA and AoD of one channel path, and the
value of the nonzero element is the corresponding complex
gain of this path. AD of size NT NR × G2 is the dictionary
matrix of quantified AoAs/AoDs. After receiving M pilots,
we have
 fT ⊗ w H 
1 
 1T
H
√  f2 ⊗ w2 
√
 AD hb + neff = ρΨAD hb + neff, (7)
y = ρ 
..
.


 fT ⊗ w H 
M
M


where y = y1, y2, · · · y M T and neff is the effective noise
vector.
To estimate hb from (7), there are two primary approaches
proposed in the literature. The first one is to combine the idea
of beam-training with sparse signal recovery. For example,
in [141], an adaptive channel estimation scheme is proposed.
This scheme divides the total channel estimation problem
into several subproblems, each of which only considers one
channel path. For each channel path, it first starts with a
coarse AoA/AoD grid, and determines the AoA and AoD of
this path belonging to which angle range by employing OMP
algorithm. Then, the AoA/AoD grid around the determined
angle range is narrowed and the AoA and AoD of this path
are further refined also by the OMP algorithm. In each step,
fm and wm are designed based on the corresponding AD
(which varies due to different AoA/AoD grid in each step)
to make the effective sensing matrix ΨAD have a fixed gain
6 When multiple RF chains are used, the transmitted pilots on different RF
chains can be designed to be orthogonal to simplify the channel estimation
at the receiver.

13

in a specific angle range. This requirement is the same as that
of the hierarchical beam-training introduced above. Therefore,
a good multi-resolution codebook such as [145], [146] can
greatly improve the performance. It has been shown that the
adaptive channel estimation scheme enjoys high accuracy with
reduced pilot overhead. Some improved schemes following
the similar idea can be found in [146], [149]. The second
approach is to regard (7) as a classical sparse signal recovery
problem [150]–[152]. Then, it designs fm and wm to make the
sensing matrix ΨAD enjoy sufficiently low mutual coherence,
which is crucial to achieve high signal recovery accuracy in
CS theory [153]. In [154], several design methods have been
proposed to achieve this goal. Finally, (7) can be solved by lots
of efficient CS algorithms, such as OMP and LASSO [153],
and the structural sparsity of mmWave MIMO channels (e.g.,
common support or partial common support) can be also
exploited to further reduce the pilot overhead and improve
the recovery accuracy [155].

the sparse structure of mmWave MIMO channels, it utilizes
the obtained channels in the previous time slots to predict
the prior information, i.e., the support of the channel, in the
following time slot without channel estimation. Finally, with
the known supports, the time-varying channels can be tracked
with a low pilot overhead. The related schemes can be found
in [160], [161]. Such schemes can perform well for the LoS
path. For the NLoS paths caused by complicated scattering,
it is difficult to analyze the geometrical relationship. As a
result, a more promising solution in practice is to utilize the
idea of the second category to track the LoS path. Then, by
eliminating the influence of this path, the NLoS paths can be
tracked following the idea of classical Kalman filters [162].
To realize mmWave mobile networks, the efficiency of
the beam-tracking schemes must be carefully tested in real
environments, to understand which mobility speeds that can be
supported and which channel characteristics that can reliably
imposed to aid the tracking procedure.

C. Channel Tracking

D. Hybrid Precoding and Combining

Since the mmWave channel varies over time in mobile networks, the conventional real-time channel estimation
should be executed frequently. This has two important consequences [156]: i) In mmWave bands, the user mobility leads
to huge Doppler effects and very limited channel coherence
time. This means that the mmWave MIMO channels will
vary quickly, even if we consider the short symbol duration
associated with the wide bandwidth; ii) In hybrid mmWave
implementations, there is no enough time to continuously redo
the beam-training from scratch. Hence, channel-tracking exploiting the temporal correlation of the time-varying channels
is preferable [156]. In this subsection, we describe two main
categories of channel-tracking schemes.
The first channel-tracking approach is an improved version
of beam-training. The key idea is to select several candidate
beam pairs instead of only the optimal beam pair, during each
beam-training procedure [142], [157]. For example, when the
beam pair with the highest SNR is selected, the beam pairs
achieving the second and third highest SNRs are also retained.
When the channel varies, only the candidate beam pairs are
tested and switched on to keep the SNR above a certain
threshold. If all the candidate beam pairs fail, the complete
beam-training will be executed again. This idea involves quite
low complexity, which makes it easy to implement. Thus, it
has been applied in current commercial mmWave communications, such as WLAN (IEEE 802.11ad) [114]. However, this
idea is only efficient for single-stream data transmission. For
multi-stream data transmission, the beam training itself will
incur high pilot overhead, not to mention the search among
all candidate beams for all data streams.
An alternative solution to track the mmWave MIMO channel
is to utilize the geometric relationship between the transmitter
and receiver to track the LoS path of the channel. Specifically,
in [156], a priori aided channel-tracking scheme was proposed.
By considering a motion model, this scheme first excavates
a temporal variation law of the AoA and AoD of the LoS
path. After that, by combining the temporal variation law with

After the CSI has been acquired, we can design the precoding and combining in the hybrid architecture to achieve
the multiplexing gain and array gain offered by MIMO.
However, the precoder/combiner design is considerably different from the precoding/combining optimizations in [163]
for the conventional fully digital MIMO systems, due to the
special hardware constraints in hybrid architecture of mmWave
systems. In this subsection, we discuss how to design the
hybrid precoder F = FA FD and combiner W = WA WD for N1N3 architectures discussed in Section III-A with perfect CSI,
which helps understand the fundamental limits of the mmWave
MIMO with a hybrid architecture. The practically more relevant case of imperfect CSI is largely open and deserves much
attention in the upcoming years; in particular, because it is
well-known from conventional MIMO communications that
precoding/combining schemes that work well under perfect
CSI can be widely different from the schemes that work well
in practice.
Let us for simplicity consider a non-fading channel H.
Specifically, we focus on the optimization problem of hybrid
precoding and combining with the aim of maximizing the
achievable rate, given by [117]


H
H H
Fopt, Wopt = arg max log2 I+ ρR−1
n W HFF H W ,
F,W

s.t. FA ∈ F , WA ∈ W, kFk 2F ≤ ND,

(8)

where Rn is the noise/interference covariance matrix, while
F and W are the sets with all possible analog precoders
and combiners satisfying the hardware constraints (which
are different for N1-N3 as discussed in Section IV-A), respectively. Generally, obtaining the optimal solution to (8)
is a non-trivial task, since F and W are much different
from conventional fully digital communication. To address
(8), several hybrid precoding/combining schemes have been
proposed in the literature to achieve feasible solutions.
We first discuss the hybrid precoding and combining
schemes for the architecture N1. One effective approach is

14

to decompose the original optimization problem into several
subproblems, and each subproblem is approximated as a
convex one and then solved by standard convex optimization
algorithms. Particularly, a spatially sparse precoding scheme
based on the orthogonal pursuit matching (OMP) has been
proposed in [117], which can fully exploit the sparsity of
mmWave MIMO channel and achieve the near-optimal performance. Using a similar idea, there are some other advanced
schemes proposed for N1 [164], [166], [167].
For the subconnected architecture N2, a few hybrid precoding solutions have been developed for maximizing the
achievable rate or the spectral efficiency [121], [168]. More
recently, the energy efficiency optimization was studied and
a solution was proposed in [165]. Since the subconnected
architecture adopts a subarray structure, it is natural to solve
the complicated hybrid precoding problem by decomposing it
into several subproblems and optimizing them in an alternating
manner. In particular, a successive interference cancelation
(SIC)-based precoding scheme was proposed in [121], which
enjoys higher energy efficiency than the spatially sparse precoding scheme. The hybrid precoding problem with energy
efficiency as the objective is more complicated. In [165],
this optimization problem is solved by jointly exploiting the
interference alignment and fractional programming. First, the
analog precoder and combiner are optimized via the alternating
direction optimization method. Then, the digital precoder and
combiner without hardware constraints are obtained based on
the effective channel matrix WAH HFA .
Finally, for the hybrid precoding/combining schemes for
N3, the main difficulty lies in the designs of FA and WA ,
as FD and WD can be easily obtained based on WAH HFA .
Therefore, it is essential to design an appropriate selecting
matrix to select DFT columns (beams) to form FA and WA .
Following this idea, a magnitude maximization (MM) beamselection scheme was proposed in [169], where several beams
with large power are selected. Alternatively, an interferenceaware beam-selection scheme was proposed in [127]. The key
idea is to classify all users into two user groups according
to the potential inter-beam interference. For users with low
inter-beam interference, it directly selects the beams with large
power, while for users with severe inter-beam interference,
a low-complexity incremental algorithm was proposed. More
beam-selection schemes for N3 can be found in [170], [171].
It worth pointing out that although we have only discussed
the single-user multi-stream scenario, the schemes designed
for N3 can be extended to multi-user multi-stream scenario.
In principle, we can replace the digital SVD precoder by the
classical zero-forcing (ZF) precoder to suppress multi-user
interference. However, such an extension is not straightforward
for the schemes designed for N1 and N2. To tackle this
problem, in [172], a two-stage precoding scheme was proposed
for architecture N1. In the first stage, FA is searched from a
predefined codebook to maximize the desired signal power
of each user. In the second stage, a digital precoder similar
to ZF precoder is designed to cancel multi-user interference.
The multi-user hybrid precoding scheme for N2, however, is
still an open problem requiring further research efforts.

Re ( ×)
RF chain

Im ( ×)
Re ( ×)

RF chain

Im ( ×)

Re ( ×)
RF chain

Im ( ×)

1-bit ADC

1-bit ADC
Digital signal
processing

1-bit ADC

Fig. 9. 1-bit ADC based architecture at the receiver.

E. Low-Resolution ADC Based Architecture
Besides the promising hybrid analog-digital architecture
discussed above, some other advanced architectures have also
been proposed to reduce the RF complexity of mmWave
MIMO systems. The low-resolution (e.g., 1-bit) ADC based
architecture7 is one typical example as shown in Fig. 9 [131].
Different from hybrid analog-digital architecture which aims
to reduce the number of RF chains, the key idea of the lowresolution ADC based architecture is to replace the highresolution (e.g., 15-bit) ADCs by low-resolution ADCs to
reduce the energy consumption and hardware cost, while the
total number of RF chains or ADCs is still the same as that
in the fully digital architecture [131].
The main advantage of low-resolution ADC based architecture is that it can significantly reduce the energy consumption
and hardware cost since it only requires a small number of
compactor in ADC [132]. Moreover, this architecture can
also simplify other circuit modules such as the automatic
gain control (AGC) [132]. It has been proved that lowresolution ADC based architecture can achieve the capacityapproaching performance in the low and medium SNR region [133]. However, due to the severe quantization effect
of low-resolution ADCs, the capacity of this architecture is
usually limited in the high SNR region [133] In addition, the
nonlinearity of the quantization also imposes new challenges
on the signal processing designs. Take the 1-bit ADC for
example, the sampled signal in the digital domain can only
be one of the two discrete values instead of one continuous
value. To solve this problem, some promising solutions have
been proposed. For example, in [134], a channel estimation
method using expectation-maximization (EM) algorithm is
proposed for low-resolution ADC based architecture to find the
maximum a posteriori probability estimate. In [135], a sumproduct-algorithm (SPA) based signal detector is designed by
utilizing the concept of clustered factor graph. In [136], the
authors develop an efficient algorithm for optimal Bayesian
data detection in the mmWave OFDM system with lowresolution ADCs. A power allocation (PA) scheme is also
proposed to minimize the average symbol error rate in [136].
Nevertheless, besides these works above, there are still
7 Note that the DACs at the transmitter usually consume less power than
ADCs for mmWave MIMO systems. Therefore, employing low-resolution
ADCs instead of DACs is more promising to reduce the energy consumption
and hardware cost [131].

15

some open issues for the low-resolution ADC based architecture [131], e.g., how to design signal processing for broadband
mmWave MIMO channels, which require future investigation.
F. Recent Progress
In the current special issue of IEEE JSAC, the signal
processing schemes on mmWave MIMO are investigated
in [174]–[189]. Specifically, in [174], a receive antenna selection (RAS)-aid spatial modulation scheme is proposed to reduce the RF complexity, and an iterative algorithm is proposed
to search the antenna index with low complexity. In [175],
a channel estimation scheme is proposed by utilizing the
structured sparsity of mmWave MIMO channel, and a training
sequence (TS) is designed by the genetic algorithm. In [176],
a low complexity non-iterative interference alignment (IA)
scheme for multi-cell mmWave MIMO systems is proposed.
In [177], a CANDECOMP/PARAFAC decomposition-based
channel estimation scheme is proposed for wideband mmWave
MIMO by utilizing the tensor theory. The Cramer-Rao bounds
of the estimated channel parameters are also derived to show
the advantages of the proposed scheme. In [178], a new
antipodal curvedly tapered slot antenna is designed to generate
circularly polarized field. It can achieve high gains with low
hardware complexity in E-band and W-band. [179] proposes a
single-user multi-beam transmission scheme in the beamspace,
and the corresponding multi-beam selection, cooperative beam
tracking, multi-beam power allocation, and synchronization
are investigated. [180] develops a low-complexity channel estimation for hybrid mmWave MIMO systems, and investigates
the achievable sum-rate of zero forcing with the estimated
channel under the consideration of system imperfection. [181]
investigates the hybrid precoding for wideband mmWave
MIMO, and proposes a unified heuristic design for two different hybrid precoding architectures, i.e., the fully-connected
and the partially-connected architectures, to maximize the
overall spectral efficiency. [182] proposes a novel unified
hybrid precoding design for fully- and partially-connected
hybrid architectures from the view of energy efficiency instead
of spectral efficiency. [183] characterizes the gains of pilot
precoding and combining in terms of channel estimation quality and achievable data rate. [184] considers the sub-28 GHz
communications that do not exhibit enough directivity and
selectively, and tackles the sum-rate maximization problem
based on the concept of difference of log and trace (DLT)
bound. [185] analyzes the achievable rate and energy efficiency
of hybrid precoding receivers with low resolution ADC, and
shows it robustness to small automatic gain control imperfections. [186] investigates the hybrid precoding design with antenna selection, and decomposes the whole problem into three
sub-problems which are solved via an alternating optimization
method. [187] proposes a hybrid precoding scheme based on
Kronecker decomposition for multi-cell multi-user mmWave
MIMO systems. [188] investigates beamforming training for
partially-connected hybrid architectures, and proposes two
multi-resolution time-delay codebooks. In [188], fundamental
limits of beam alignments are studied under different search
approaches (exhaustive or hierarchical).

V. ACCESS , BACKHAULING , AND C OVERAGE
A. Multiple-access Technologies
Multiple-access technologies are necessary for supporting
multiple users in mobile networks, and they have been widely
investigated in the lower frequency bands. Different multiple
access technologies have been utilized in practical systems,
including frequency division multiple access (FDMA), time
division multiple access (TDMA), code division multiple
access (CDMA), and orthogonal frequency division multiple
access (OFDMA). These multiple-access technologies are also
applicable to mmWave, but in different flavors than in lower
frequency bands due to the increased complexity caused by
the greatly increased bandwidth, and the different channel
characteristics in mmWave bands, e.g., highly directional
transmissions. More importantly, as described in Section IV,
the shorter wavelengths at mmWave frequencies make MIMO
technology suitable for mmWave, since it is possible to use
more antennas in the same physical space. One of the key
advantages of MIMO is the spatial resolution and beamforming capability that it provides. In this subsection, major nonorthogonal multiple-access technologies that exploit the spatial
or power domains will be discussed in the context of mmWave.
This includes spatial division multiple access (SDMA), nonorthogonal multiple access (NOMA), and finally random access.
1) SDMA: In addition to the orders-of-magnitude larger
bandwidths, as compared to conventional systems operating
below 6 GHz, a multiplexing gain can be achieved by multiplexing of users in the spatial domain. This technology is
known as SDMA was introduced in 1990s [192], and Massive
MIMO is the latest branch of this tree [36]. 802.11ac was
the first major wireless standard that integrated SDMA [193].
SDMA can ideally increase the sum rate proportionally to
the number of multiplexed users, provided that the BS is
equipped with at least as many RF transceiver chains as
all the users have in total. Due to the highly directional
transmissions in mmWave communication systems, users from
different directions may be well separated using different
spatial beams, which is also known as favorable propagation
[191]. The design of transmit precoding/beamforming and
receive combining for SDMA is generally a mature topic
[194], [195], but the special channel estimation and channel
characteristics of hybrid systems require some further work on
this topic [196], [197].
One key difference between systems operating in mmWave
band and at lower frequencies is the coverage area, which
is substantially smaller in mmWave. Hence, there is generally
fewer users to multiplex by SDMA in mmWave. Nevertheless,
one of the critical challenges of SDMA is how to serve
multiple users when the number of users is larger than the
number of antennas. It is necessary to group users so that users
from different groups may access the BS at the same time,
while not causing significant interference to each other. After
realizing the user grouping, user scheduling should also be
considered to select the users from different groups to access
the BS at the same time. Users in the same group can be
served orthogonally (or semi-orthogonal) in time, frequency,

16

or code domain. A few effective scheduling algorithms have
been proposed in [202]–[210] to enhance throughput.
2) NOMA: An alternative to the non-orthogonal spatial
multiplexing provided by SDMA is to perform non-orthogonal
multiple access (NOMA) in the power domain. This approach
is considered as one of the candidates for improving the
spectral efficiency and connectivity density in 5G [223]–
[227]. At the BS, different signals intended for different
users are superimposed on each other after classic channel
coding and modulation. Multiple users share the same timefrequency resources, and are then detected at the receivers
by successive interference cancellation (SIC). In this way,
the spectral efficiency can be enhanced at the cost of an
increased receiver complexity compared to conventional orthogonal multiple access (OMA), where the potential interference is treated as noise. NOMA has been considered in
mmWave communications in recent literatures. Specifically,
the performance of NOMA in mmWave communications was
evaluated in [228] and [229], and simulation results have
shown that NOMA can achieve better channel capacity than
OMA in both uplink and downlink mmWave communication
systems. Furthermore, the capacity performance of NOMAmmWave-massive-MIMO systems was investigated in [230],
and simulation results indicated that enormous capacity improvement can be achieved compared to the existing LTE
systems. In addition, sum rate and outage probabilities of
mmWave-NOMA systems with random beamforming were
analyzed in [231], where two users can be served by NOMA
in each beam. Furthermore, a transmission scheme that uses
NOMA in mmWave beamspace MIMO has been proposed
in [232]. By using intra-beam superposition coding and SIC
under the framework of NOMA in the proposed beamspace
MIMO-NOMA system, more than one user can be simultaneously supported in each beam, which is different from
conventional SDMA, where only the signal intended for one
user is transmitted in each beam. Consequently, the number of
served users can be significantly increased for a given number
of beams and RF chains, and the system achievable sum
rate can be also improved. Besides, beam division multiple
access (BDMA) with per-beam synchronization (PBS) in time
and frequency for wideband massive MIMO transmission
over mmWave/Terahertz (THz) bands has been proposed in
[233], beam scheduling for both UL and DL BDMA and a
greedy beam scheduling algorithm has also been developed.
Additionally, the design challenges of NOMA in mmWave
due to beamforming was investigated in [234]. Note that
with the enhanced pathloss in mmWave communications, the
interference experienced by the users in NOMA can be significantly reduced [228]. Considering this harmony between the
mmWave channel characteristics and the principle of NOMA,
the use of NOMA for mmWave communications is a research
direction deserves further investigation.
3) Random access: This is primarily used for initial access
and handover, which is very important in system design.
Since random access cannot fully benefit from beamforming
due to the lack of information on the best transmit-receive
beam pair, the design of the random access channel becomes
more challenging in mmWave communication systems [211]–

[213]. An overview of random access in mmWave mobile
networks has been presented in [211], where the important
issues for the design of a random access channel with respect
to initial access, handover, uplink-downlink configuration, and
scheduling have been discussed in detail.
More recently, there are some recent works emphasize on
lower frequencies in ad hoc wireless network scenarios or,
more recently, on the 60 GHz IEEE 802.11ad WLAN and
WPAN scenarios [214]. While the authors in [215] considered an exhaustive method which sequentially scans the 360
degree angular space, the authors in [216] proposed the use
of directional cell discovery procedure where the angular
space is scanned in time-varying random directions using
synchronization signals periodically transmitted by the base
stations. In [217], different scanning and signaling methods
were compared, with respect to the initial access design
options, in order to assess the respective access delays and
system overheads. The analysis shows that low-resolution fully
digital architectures have significant benefits when compared
with single-stream analog beamforming. In order to reduce
the delay due to exhaustive search procedures, the authors
in [211], [217], [218] implemented a faster user discovery
method which employs a two-stage hierarchical procedure,
while the use of context information about user and/or BS
positions, provided by a separate control plane, was considered
in [214], [219]. In a refinement to the method in [219], the
procedure to capture the effects of position inaccuracy and
obstacles was implemented in [220]. In addition, the use
of booster cells which operate at mm-Waves and under the
coverage of a microwave-based anchor cell was proposed by
the authors in [221]. In this arrangement, the booster BS gets
information about user locations from the anchor BS, enabling
it to directly steer the transmit beams towards the user position.
Finally, the authors in [222] showed that the performance of
analog beamforming degrades in presence of errors in the
available context information provided during the initial cell
search procedures.
B. Backhauling
To meet the aggressive 5G KPIs discussed in Section I,
an UDN deployment is considered as a promising system
architecture to enable Gbps user experience and seamless
coverage in mobile networks [235], [236]. In other words,
many small-cell BSs are densely deployed as hotspots (e.g., in
office buildings, shopping malls, residential apartments) that
would greatly offload the macro cells. Hence, the backhaul
between the macro-cell BS and the associated small-cell BSs
should provide large bandwidth with reliable link transmission.
Besides, energy efficiency and deployment cost are also key
considerations for operators. It has been demonstrated that
backhaul links with 1–10 Gbps is required to effectively
support UDN [235]. Conventional optical fiber supports large
data rates and reliable link transmission, but its application
to UDN as backhaul may not be an economical choice for
operators due to the restriction of deployment and installation
[237]. Hence, wireless backhaul is more attractive to overcome
the geographical constraints. Microwave backhaul in sub-6

17

GHz and 20 GHz bands have been successfully deployed
in current mobile networks. However, the available licensed
spectrum in these bands are limited, and insufficient to meet
the demand of 5G backhauling, and thus mmWave with wider
bandwidth is preferred.
Using mmWave bands for backhauling in UDN is desirable
due to the following reasons [237].
• Large bandwidth: The large amount of underutilized
mmWave including unlicensed V-band (57–67 GHz) and
lightly licensed E-band (71–76 GHz and 81–86 GHz) (the
specific regulation may vary from country to country)
can provide potential GHz transmission bandwidth. For
example, more than 1 Gbps backhaul capacity can be
supported over a 250 MHz channel in the E-band [238].
• Reduced interference: The coverage distance for E-band
is up to several km due to rain attenuation, while that for
V-band is about 50–700 m due to both the rain and oxygen
attenuation. Due to high pathloss, mmWave is suitable
for UDN, where improved frequency reuse and reduced
inter-cell interference are expected. It should be pointed
out that rain attenuation is not a big issue for mmWave
used for backhaul in UDN. For example, if we consider
very heavy rainfall of 25 mm/h, the rain attenuation is
only around 2 dB in the E-band for a backhaul link of
200 m [238].
• In-band backhauling: The backhaul and user access
links are conventionally carried out in different frequency
bands. Multiplexing backhaul and access on the same
mmWave frequency band, also named in-band backhaul
[235], has obvious cost benefits from the hardware and
frequency reuse perspective. However, as in any inband backhauling scenario, the interference between the
backhaul and the user access links must be controlled;
for example, by beamforming and signal processing.
C. Coverage and Connectivity
Since mmWave signals in general have high penetration
loss, they are very sensitive to the blockage by walls and other
objects. In [242], a mathematical framework for modeling the
random blockages that occur when users are moving around
was developed by leveraging concepts from random shape
theory. In this model, both BSs and users are assumed to form
a homogeneous Poisson point process (PPP) on the plane.
Random buildings are modeled as rectangles with random
sizes and orientations whose centers form a PPP on the plane.
Let K denote the total number of blockages crossing a link. As
shown in [242], K follows a Poisson distribution with the mean
of βR + p, where β = 2λ(E[W ] + E[L])/π, p = λE[L]E[W ],
E[L] and E[W ] represent the average length and width of a
random blockage building, respectively. Then, the probability
that link of length R is free from blockage has an exponential
distribution, given by
P(K = 0) = e−(βR+p) .

(9)

It should be noted that in practical mmWave systems the
parameters β and p in (9) need to be obtained through
experimental fitting.

A visible LoS region of location X is defined as the set of
locations which can be connected to X with a LoS link. If
we consider the case where the blockages are impenetrable,
the average size of a visible region in a cellular network can
be shown to be 2πe−p / β 2 [242] and the average number
of BSs which has a LoS link with a user is 2π µe−p / β 2 ,
where µ is the density of the BS PPP. Therefore, in order
to achieve acceptable coverage, we can either increase the
density of the BS deployment or reduce the physical length of
the communication links between two nodes in the network by
deploying more intermediate relays. The paper [173] systematically studies the blockage problem of mmWave networks.
The impact of reflection and relaying are also investigated. It is
shown that if the LoS signals are blocked, the reflection signals
may be useful in regions, while for some regions relaying
is preferred. Reference [173] also studies the optimal routing
problem in multiple relay mmWave networks. Recently, an
optimal opportunistic strategy for access point deployment is
proposed in [247] to well balance the overhead and capacity
under random blockage.
[243] takes a stochastic geometry approach to the connectivity of mmWave networks with multi-hop relaying. It is
shown that multi-hop relaying can greatly improve the connectivity compared to the single-hop mmWave transmission.
The results also show that to obtain near-optimal connectivity
the relaying route window should be about the size of the
obstacle buildings. The coverage probability is an important
performance metric in a mmWave cellular network. It is
defined as the probability that the destination is able to receive
a signal with a certain threshold SNR T:
Pc (T ) = Pr(SNR > T ).

(10)

To further improve the coverage, [244] studies the possibility
of BS cooperation in the downlink of mmWave networks in a
stochastic geometry framework. It is shown that cooperation
among randomly located BSs can effectively increase the
coverage probability.
By using tools from stochastic geometry, a general and
tractable framework for coverage analysis with arbitrary distributions for interference power and arbitrary antenna patterns
was developed and applied to mmWave ad hoc networks
exploiting directional antenna arrays in [245]. It is shown
that the coverage probabilities of mmWave ad hoc networks
increase as a non-decreasing concave function with the antenna
array size. Numerical results also show that largescale antenna
arrays are required for satisfactory coverage in mm-wave
networks. To further enhance the connectivity and session
continuity, multi-connectivity strategies were developed to
leverage multiple simultaneous small cell connections in ultradense urban deployments of mmWave networks in [246].
The benefits of multi-connectivity strategies were investigated
by taking into account: (i) the intricacies of mmWave radio
propagation in realistic urban environments; (ii) the dynamic
mmWave link blockage due to human mobility; and (iii)
the multi-connectivity network behavior to preserve session
continuity. Results show that even simpler multi-connectivity
schemes bring notable improvements to session-level mmWave
operation in realistic environments.

18

To achieve a robust coverage and connectivity of mmWave
networks, a heterogeneous mm-wave network architecture
consisting of small-cell BSs operating at mmWave bands and
macro-cell BSs, operating at microwave frequencies, should be
considered in practice. The macro-cells will be used as a signaling network for cell discovery and signaling transmission to
guarantee full coverage and provide reliable control channels.
The small-cell BSs form a data subnetwork that provides high
data rates using mmWave bands for the users that they cover.
VI. S TANDARDIZATION AND D EPLOYMENT
Despite of the huge potential and great number of benefits
in using mmWave in mobile networks, there are still a lot
of skepticisms, particularly from investors, as for whether
the technology is suitable for cellular coverage and mobility
scenarios. Due to the great potential of mmWave communications as described in previous sections, 3GPP is working
towards standardizing mmWave for in the 5G New Radio (NR)
interface. More specifically, 3GPP is working on Release 14
(will be finalized around June 2017), which will include the
channel modeling for radio above 6GHz. From the second
half of 2017, 3GPP will work on release 15, which will
deliver the first set of 5G standards. Commercial use of the
unlicensed mmWave band is not a new territory, but mmWave
bands have been used since the 1980s. However, the existing
standards and applications have only been for static ultrahigh-definition multimedia data transfer. Specifically, IEEE
802.11ad/aj for wireless local area network (WLAN), IEEE
802.15.3c and ECMA-387 for wireless personal area network
(WPAN), WirelessHD for video area networking (VAN) are
standards at unlicensed 60GHz and 45GHz for providing
short-range point-to-point (P2P) communications.
In this section, we will introduce the key features of the
aforementioned existing and in-development mmWave standards, followed by an overview of the plan for commercial
cellular deployment.
A. 3GPP’s New Radio at mmWave Band
1) Vision and use cases: 5G is envisioned to provide
orders-of-magnitude improvements in the peak data rate,
network capacity, latency, availability, and reliability over
legacy networks. Meanwhile, the deployment should cooperate
seamlessly with legacy networks and provide fundamental
shifts in cost and energy efficiency [120]. As discussed in
Section I, 3GPP has defined three use cases for 5G NR since
2016; namely, eMBB, mMTC and URLLC. EMBB is targeted
for mobile broadband services that require extraordinary data
rates; mMTC is the basis for connectivity in Internet of Things
(IoT); and URLLC is needed for applications which have
stringent latency and reliability requirements. 3GPP has also
identified its interest in the frequency bands above 6 GHz
and up to 100 GHz for NR. The channel model for above
6 GHz is defined in 3GPP Specification 38.900/38.901 [21],
[98]. Network operators around the world are exploring the
possibility of using parts of this spectrum for licensed mobile communications to meet diversified services from their
customers. mmWave is naturally envisioned to provide eMBB

services where high-speed data transmission is needed. This is
particularly important in small cells and dense urban scenarios.
Other applications are backhaul for last-mile fiber replacement,
wireless fronthauls, etc. However, the research has not been
very clear on how mmWave affects the latency and reliability
aspects.
2) mmWave and massive MIMO: The 5G vision is pushing
for a fundamental change in the mobile networks, starting with
the lowest physical layer (PHY). The two core PHY technologies, that will set 5G NR apart from previous radio access
technologies (RATs), are mmWave and massive MIMO. Both
put forth different paradigms that make a break with many
current understandings in the wireless propagation, signal
processing, device manufacturing and eventual network design
[113]. Moreover, there is a clear trend that mmWave and
massive MIMO will work in cooperation to ensure a successful
5G operation. The great SDMA capability of massive MIMO
is ideal in the frequency bands below 6 GHz, where a large
area is covered and many users with NLoS channels need
to be served simultaneously. The high capacity of mmWave
communications is ideal for hotspots that need not guarantee
coverage or support mobility, but can provide great service to
the LoS users that are static or moving at pedestrian speed. In
3GPP, the maximum number of the RF chains for the NR BS
and UE is determined as 32 and 8 respectively. The maximum
number of antenna elements can go up to 1024 at the BS and
64 at the UE (for 70 GHz), respectively.
3) Hybrid beamforming Architecture: At the PHY layer,
the hybrid analog-digital beamforming technique was agreed
to be used in 5G systems at the 3GPP RAN1 meeting in 2016
[125]. As discussed in Section IV, the hybrid architecture may
be used by both the BSs and UEs. It is worth noting that
the hybrid beamforming would not only be used to boost the
data rate for data transmission, but also be used at the control
channel to enhance cell coverage. Thereafter, it is deemed necessary to have Physical Uplink Control Channel (PUCCH) to
include CSI that is related to analog beamforming information.
As for the Physical Downlink Control Channel (PDCCH), it is
desirable to have a common design for high and low frequency
bands. Both uniform antenna arrays (i.e., antenna elements
with the same polarization from multiple panels are uniformly
distributed in horizontal and vertical dimensions, respectively)
and non-uniform antenna arrays [250] (i.e., antenna elements
with same polarization from multiple panels are not uniformly
distributed in horizontal or vertical dimension) should be
supported in 5G systems. This means that a flexible design
of the hybrid beamforming is needed, which is not limited to
calibrated arrays of a particular geometry. Spatial beams can be
generated from separate antenna panels (or different sections
of the antenna array) to serve different users. Meanwhile, it
is possible to allocate different transmission tasks (i.e., data
channel and control channel) to each subarray. Nevertheless,
fully digital beamforming is not disregarded by industry, and
low-resolution beamforming could work at the lower end of
the mmWave spectrum with manageable complexity.
4) PHY layer design: Some common understandings on
the way forward for the standardization of the PHY layer
for mmWave communications have been agreed upon. More

19

TABLE III
S UMMARY OF MM WAVE PERFORMANCE MEASUREMENT CAMPAIGNS

Freq band
Architecture
Peak Throughput (Gbps)

Huawei

QUALCOMM

Ericsson

Samsung

Nokia

28 GHz [252]
73 GHz [253]
Digital (@28 G)
Analog (@73 G)
35(@73 G)

28 GHz [254]

28 GHz [257]

28 GHz [256]

Hybrid

Hybrid

Hybrid

10

14

7.5

28 GHz
73 GHz [255]
Hybrid(@28GHz)
RF(@73GHz)
11

detailed discussion and specifications are still open and are
expected to be finalized in the 3GPP Release 15 in 2018.
Research should focus on, but not be limited to, the following areas: i) Beam-management, which includes the beamsweeping procedure, beam-selection based on CSI feedback or
beam-reciprocity assumptions, beam-tracking and recovery for
mobility support, etc.; ii) the corresponding Reference Signal
(RS) design for beam-management; and iii) Control channel
design.
Beam-sweeping is a main method for the analog part of
the hybrid beamforming, and its procedure design has a
big impact on the system implementation. To name a few,
how to design an efficient and reliable sweeping procedure
during the initial access stage, how to perform beam-tacking
for a UE as the propagation channel changes (due to fast
fading or mobility), and how to maintain a connection (i.e.,
beam-recovery) in the case of link failure and/or blockage.
A well designed beam-selection mechanism can ensure good
signal strength to reap the benefits of data transmission at the
mmWave frequencies with a wide bandwidth. The accuracy of
the selection is dependent on the analog beam-feedback, the
digital precoding matrix feedback and the Channel Quality
Index (CQI) feedback. The common understanding is that the
UE will report measurement results on different BS transmit
beams to aid beam-selection at the BS.
B. Prototypes and Deployment plan
There has been tremendous efforts from various organizations (vendors, operators, universities, etc.) in building
hardware platforms for mmWave channel measurement (as
described in Section II) and for proof-of-concept prototyping.
This is the necessary step to test for different frequency
bands, different use cases, and their respective KPIs before
the mass deployment. In the past year of 2016, there have
been many feasibility tests and functionality tests for potential
key techniques proposed for mmWave. A large quantity of
measurement data has been accumulated, which is vital for
clarification on the system architecture and the PHY design, as
well as the subsequent standardization and commercialization
for mmWave communication. Table III lists some known
operational prototypes and their key system parameters.
Current measurements are largely focused on four areas of
interest.
1) Basic throughput performance: the maximum number
of reported spatial streams is two (not counting dualpolarization of antenna elements) and the maximum spectral efficiency is less than 20 bps/Hz.

2) NLoS transmission: metal and concrete walls pose high
penetration loss; heavy foliage poses severe shadowing
loss; reasonable reflection loss but high diffraction loss.
3) Coverage: indoor ≤ 100 m; urban outdoor: ≤ 350 m;
outdoor-to-indoor: ≤ 20 m.
4) Beam-tracking: It can be done for a single user at
pedestrian (15 km/h) or low speed (40 km/h); multi-user
tracking (spatially) is lacking.
The next phase of testing will focus on the feasibility in
more difficult use cases for a single channel; for instance, outdoor BS serving indoor UEs, maximum outdoor coverage test,
high-mobility/vehicular scenarios, beam-tracking for multiple
UEs, dense urban services, multi-cell networking and core
network support. This will require the consumer equipments
to be developed and ready for such tests, so that end-toend performance evaluation can be possible. It is most likely
that mass deployment of mmWave technology for mobile
networks will occur after 2020, since the progress still largely
hinges on the standardization process and the resolving of the
aforementioned challenges.
VII. C ONCLUSIONS
Despite of high potentials of providing multiple Gbps rates,
many technical challenges have to be solved for mmWave
communications to become a mainstream technology in mobile networks. In recent years, large efforts have made to
tackle the various challenges and many excellent results have
been reported. This tutorial paper has summarized the recent
technical progresses in mmWave communications for mobile
networks, including channel measure/modeling, MIMO design, multiple access, performance analysis, standardization,
and deployment. Many directions for future research work
have also been identified. From our point of view, finding
effective solutions for applying mmWave technology in high
mobility environments, enabling enhanced transmission distance, combating hardware impairments, and achieving high
energy-efficiency are very important and interesting challenges
to tackle. From a broader perspective, the RF implementation
of mmWave technology is very important and a long-term goal
should be to obtain a cost-efficient fully digital implementation.
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