Ultra-Reliable Low Latency Cellular Networks:
Use Cases, Challenges and Approaches
He Chen, Rana Abbas1, Peng Cheng1, Mahyar Shirvanimoghaddam1, Wibowo Hardjawana1, Wei Bao1,
Yonghui Li, and Branka Vucetic
The University of Sydney, NSW 2006, Australia
Email: firstname.lastname@sydney.edu.au
Abstract‐The fifth-generation cellular mobile networks are expected to support mission critical
ultra-reliable low latency communication (uRLLC) services in addition to the enhanced mobile
broadband applications. This article first introduces three emerging mission critical applications of
uRLLC and identifies their requirements on end-to-end latency and reliability. We then investigate
the various sources of end-to-end delay of current wireless networks by taking the 4G Long Term
Evolution (LTE) as an example. Subsequently, we propose and evaluate several techniques to
reduce the end-to-end latency from the perspectives of error control coding, signal processing, and
radio resource management. We also briefly discuss other network design approaches with the
potential for further latency reduction.

I. Introduction
The growth of wireless data traffic over the past three decades has been relentless. The upcoming
fifth-generation (5G) of wireless cellular networks is expected to carry 1000 times more traffic [1]
while maintaining high reliability. Another critical requirement of 5G is ultra-low latency – the
time required for transmitting a message through the network. The current fourth-generation (4G)
wireless cellular networks have a nominal latency of about 50ms; however, this is currently
unpredictable and can go up to several seconds [2]. Moreover, it is mainly optimized for mobile
broadband traffic with target block error rate (BLER) of 10-1 before re-transmission.
1

These authors contributed equally to this article.

There is a general consensus that the future of many industrial control, traffic safety, medical,
and internet services depends on wireless connectivity with guaranteed consistent latencies of
1ms or less and exceedingly stringent reliability of BLERs as low as 10-9 [3]. While the projected
enormous capacity growth is achievable through conventional methods of moving to higher parts
of the radio spectrum and network densifications, significant reductions in latency, while
guaranteeing an ultra-high reliability, will involve a departure from the underlying theoretical
principles of wireless communications.
II. Emerging uRLLC Applications
In this section, we introduce three emerging mission-critical applications and identify their
latency and reliability requirements.
A. Tele-surgery
The application of uRLLC in tele-surgery has two main use cases [4]: (1) remote surgical
consultations, and (2) remote surgery. The remote surgical consultations can occur during
complex life-saving procedures after serious accidents with patients having health emergency
that cannot wait to be transported to a hospital. In such cases, first-responders at an accident
venue may need to connect to surgeons in hospital to get advice and guidance to conduct
complex medical operations. On the other hand, in a remote surgery scenario, the entire
treatment procedure of patients is executed by a surgeon at a remote site, where hands are
replaced by robotic arms. In these two use cases, the communication networks should be able to
support the timely and reliable delivery of audio and video streaming. Moreover, the haptic
feedback enabled by various sensors located on the surgical equipment is also needed in remote
surgery such that the surgeons can feel what the robotic arms are touching for precise decision-

making. Among these three types of traffic, it is haptic feedback that requires the tightest delay
requirement with the end-to-end round trip times (RTTs) lower than 1ms [4]. In terms of
reliability, rare failures can be tolerated in remote surgical consultations, while the remote
surgery demands an extremely reliable system (BLER down to 10-9) since any noticeable error
can lead to catastrophic outcomes.
B. Intelligent Transportation
The realization of uRLLC can empower several technological transformations in transportation
industry [5], including automated driving, road safety and traffic efficiency services, etc. These
transformations will get cars fully connected such that they can react to increasingly complex
road situations by cooperating with others rather than relying on its local information. These
trends will require information to be disseminated among vehicles reliably within extremely
short time duration. For example, in fully automated driving with no human intervention,
vehicles can benefit by the information received from roadside infrastructure or other vehicles.
The typical use cases of this application are automated overtake, cooperative collision avoidance
and high density platooning, which require an end-to-end latency of 5–10ms and a BLER down
to 106 [5].
C. Industry Automation
uRLLC is one of the enabling technologies in the fourth industrial revolution [6]. In this new
industrial vision, industry control is automated by deploying networks in factories. Typical
industrial automation use cases requiring uRLLC include factory, process, and power system
automation. To enable these applications, an end-to-end latency lower than 0.5ms and an
exceedingly high reliability with BLER of 10-9 should be supported [3]. Traditionally, industrial
control systems are mostly based on wired networks because the existing wireless technologies

cannot meet the industrial latency and reliability requirements. Nevertheless, replacing the
currently used wires with radio links can bring substantial benefits: (1) reduced cost of
manufacturing, installation and maintenance; (2) higher long-term reliability as wired
connections suffer from wear and tear in motion applications; (3) inherent deployment flexibility.
Other possible applications of uRLLC include Tactile Internet, augmented/virtual reality, fault
detection, frequency and voltage control in smart grids.

Fig. 1. Architecture of 4G LTE network with representative mission-critical user equipment. The bottom part lists various
potential measures towards latency reduction in different parts.

III. Latency Sources in Cellular Networks
Cellular networks are complex systems with multiple layers and protocols, as depicted in Fig. 1.
The duration of a data block at the physical layer is a basic delay unit which gets multiplied over
higher layers and thus causes a considerable latency in a single link. On the other hand, protocols
at higher layers and their interactions are significant sources of delay in the whole network.
Latency varies significantly as a function of multiple parameters, including the transmitter–

receiver distance, wireless technology, mobility, network architecture, and the number of active
network users.
TABLE I
VARIOUS DELAY SOURCES OF AN LTE SYSTEM (RELEASE 8) IN THE UPLINK AND DOWNLINK

Delay Component

Description

Time (ms)

A user connected and aligned to a base station will send a Scheduling
Request (SR) when it has data to transmit. The SR can only be sent in an
Grant acquisition

5ms
SR-valid Physical Uplink Control Channel (PRCCH). This component
characterizes the average waiting time for a PRCCH.
This procedure applies to the users not aligned with the base station. To
establish a link, the user initiates an uplink grant acquisition process over the

Random Access

9.5ms
random access channel. This process includes preamble transmissions and
detection, scheduling, and processing at both the user and the base station.

Transmit time interval

The minimum time to transmit each packet of request, grant or data

1ms

The time used for the processing (e.g., encoding and decoding) data and
Signal processing

3ms
control

Packet retransmission

The (uplink) hybrid automatic repeat request process delay for each

in access network

retransmission

8ms
Queueing delay due to congestion, propagation delay, packet retransmission

Vary

delay caused by upper layer (e.g., TCP)

widely

Core network/Internet

The latency components of the LTE networks have been systematically evaluated and quantified
in [7]. Latencies for various radio access network algorithms and protocols in data transmission
from a user to the gateway (i.e., uplink) and back (i.e., downlink) are summarized in Table I. The
two most critical sources of delay in radio access networks are the link establishment (i.e., grant
acquisition or random access) and packet retransmissions caused by channel errors and

congestion. Another elementary delay component is the transmit time interval (TTI), defined as
the minimum data block length, which is involved in each transmission of grant, data, and
retransmission due to errors detected in higher layer protocols.
According to Table I, after a user is aligned with the base station, its total average radio access
delay for an uplink transmission can be up to 17ms excluding any retransmission. The delay for a
downlink transmission is around 7.5ms, which is lower than that of the uplink since no grant
acquisition process is needed in the downlink. The overall end-to-end latency in cellular
networks is dictated not only by the radio access network but also includes delays of the core
network, data center/cloud, Internet server and radio propagation. It increases with the
transmitter-receiver distance and the network load. As shown by the experiment conducted in [8],
at least 39ms is needed to contact the core network gateway, which connects the LTE system to
the Internet, while a minimum of 44ms is required to get response from the Google server. As
the number of users in the network rises, the delay goes up, due to more frequent collisions in
grant acquisition and retransmissions caused by inter-user interference.
In the subsequent sections, we will consider novel approaches that could be implemented at
various cellular network layers (as depicted in the bottom part of Fig. 1) to support ultra-low
latency services.
IV. Short Error Control Codes
In traditional communication systems, very long low-density parity check (LDPC) or turbo codes
are used to achieve near error-free transmissions, as long as the data rate is below the Shannon
channel capacity. Since the network latency is significantly affected by the size of data blocks,
short codes are a prerequisite for low delays; but the Shannon theoretical model breaks down for

short codes. A recent Polyansky-Poor-Verdu (PPV) analysis of channel capacity with finite
block lengths [9] has provided the tradeoffs between delays, throughput, and reliability on
Gaussian channels and fixed rate block codes, by introducing a new fundamental parameter
called ‘channel dispersion’; this analysis shows that there is a severe capacity loss at short blocklengths.
There are no known codes that achieve the PPV limit. Low-density parity check (LDPC) codes
and polar codes have been reported to achieve almost 95% of the PPV bound at block error rates
as low as 10-7 for block lengths of a few hundred symbols [10]. However, their main drawback is
the large decoding latency. On the other hand, convolutional codes provide fast decoding as a
block can be decoded as it is being received and can achieve BLERs as low as 10-9. Note that as
the signal-to-noise ratio (SNR) in wireless channels varies over time and frequency due to fading,
these low BELRs can only be achieved at very high SNRs (as high as 90 dB) over point to point
channels. To address this issue, these error control codes need to be augmented by some form of
diversity such as implementing multi-antenna techniques.
As long fixed rate codes achieve the Shannon capacity limit for one signal-to-noise ratio (SNR)
only, today’s wireless networks use adaptive schemes, which select a code from a large number
of fixed rate codes, to transmit data at the highest possible rate for a specified reliability and
estimated channel state information (CSI). The problem is the inevitable latency increase due to
complex encoding and decoding algorithms, the time required to estimate the CSI at the receiver,
the feedback of CSI back to the transmitter, code rate and modulation selection process in the
transmitter, and block length.
In this context, self-adaptive codes appear as a promising solution to uRLLC. Self-adaptive
codes, also known as rateless codes, can adapt the code rate to the channel variations by sending

an exact amount of coded symbols needed for successful decoding. This self-adaptation does not
require any channel state information at the transmitter side, thus eliminating the channel
estimation overhead and delay. While there are some research results on rateless codes for the
short block length regime, they are all on binary codes, and their extension to the real domain is
not straight-forward.

Fig. 2. AFC with a 0.95-rate Protograph-based LDPC precoder are used to encode a message of length 192 bits for a block error
rate of 10-4 over a wide range of SNRs for the AWGN channel.

Recently, an analog fountain code (AFC) [11] was proposed as a capacity-approaching rateless
code over a wide range of SNRs for asymptotically long codewords. AFC can be represented by
a single sparse non-binary generator matrix such that the optimization of the coding and
modulation can be performed jointly via specialized EXIT charts. The resulting performance is
seamless over a large range of SNRs with only linear encoding and decoding complexity with
respect to the block length. In Fig. 2, we show that AFC, even in the current sub-optimal design
for short codes, has a small gap to the PPV bound in the high SNR region. Moreover, we expect
that a much lower latency can be achieved when optimizing AFC for shorter block lengths. As

self-adaptive codes do not require any CSI to be available at the transmitter side, the channel
estimation overhead can be eliminated, which has been reported to require 7–8ms in the current
LTE standards. Finally, for the sake of completion, it is worth mentioning that our simulations
over the Rayleigh fading channel showed that AFC can achieve BLERs as low as 10-6 for a wide
range of SNRs with space diversity with only 10 antennas and maximum ratio combining.
V. Ultra-fast Signal Processing
The current LTE systems use system throughput as the main design target and performance
indicator. In contrast, signal processing latency issues has drawn far less attention in the design
process. Similar to Section III, valuable insights into the processing latency bottleneck in the
current LTE systems could be obtained by a breakdown of latencies contributed by each LTE
receiver module. To this end, we investigate the average computational time for the major
receiver modules of an LTE Release 8 system by implementing it on an Intel Core i5 computer.
The computational time, a practical indicator for relative latency, is presented in Table II for
three typical bandwidths. In the simulations, we have 4 transmit and 2 receive antennas, 16QAM, and 0.3691 code rate at signal-to-noise ratio of 10dB. The closed-loop spatial
multiplexing mode was implemented and the average computational time is based on one
subframe. It is clearly shown that MMSE-based channel estimation, MMSE-SIC-based MIMO
detection, and Turbo decoding consume the most computational resources and dominate the
computational time. To lower the processing latency, new ultra-fast signal processing techniques,
especially for the three identified functions, should be developed to strike a favorable tradeoff
between throughput and latency.

TABLE II
A COMPARISON OF COMPUTATIONAL TIME FOR DIFFERENT FUNCTION MODULES AT THE RECEIVER, WHEREIN ALL NUMBERS WITHOUT A UNIT ARE
IN SECONDS.

Receive Modules

B = 1.4MHz

B = 5MHz

B = 10MHz

CFO Compensation

0.0010

0.0023

0.0037

FFT

2.9004e-04

6.2917e-04

8.3004e-04

Disassemble Reference Signal

1.2523e-04

2.2708e-04

3.1685e-04

Channel Estimation (MMSE)

0.0015

0.0141

0.0878

Disassemble Symbols

0.0013

0.0045

0.0087

MIMO Detection (MMSE-SIC)

0.0028

0.0242

0.0760

SINR Calculation

2.4947e-04

6.6754e-04

0.0012

Layer Demapping

4.3253e-05

1.0988e-04

3.8987e-04

Turbo Decoding

0.0129

0.0498

0.1048

Obtained Throughput

2.2739Mbps 10.073Mbps

20.41Mbps

In our simulation, we propose and implement an improved channel estimation approach to
reduce the channel estimation latency. The basic idea is to use the least square estimation to
extract the CSI associated with the reference symbols, and then employ an advanced lowcomplexity 2-D biharmonic interpolation method to obtain the CSI for the entire resource block.
Typically, the resulting curves from the biharmonic interpolation method are much smoother
than the linear and nearest neighbor methods. Our simulation results show that the proposed
channel estimation method can reduce around 60% of the computational time relative to the
MMSE-based method at B = 5MHz, while achieving almost the same system throughput.
It is also desirable to develop ultra-fast multilayer interference suppression technologies to
enable fast MIMO detection, especially for a large number of transmit and receive antennas.
Along this direction, a parallel interference cancellation (PIC) with decision statistical combining
(DSC) detection algorithm was developed in [12], which can significantly reduce the detection

latency compared with MMSE-SIC. The PIC detectors are equivalent to a bank of matched
filters, which avoid the time-consuming MMSE matrix inversion. A very small number of
iterations between the decoder and the matched filter are added to achieve the performance of
MMSE receivers. This algorithm was also applied to ICI cancellation for high-mobility MIMOOFDM systems and was shown to achieve a very good performance/complexity tradeoff.
Parallel hardware implementation is another important measure to reduce signal processing
latency. For example, the recently proposed parallel turbo decoder architecture [13] eliminates
the serial data dependencies, realizes full parallel processing, offers a significantly higher
processing throughput, and finally achieves a 50% hardware resource reduction compared with
the original architecture. With uRLLC recently declared as one of the major goals in 5G
networks, we envisage more research activities in developing ultra-fast signal processing
techniques and architectures.
VI. Radio Resource Management
In this section, we will discuss two radio resource management techniques that have great
potential to reduce the latency caused by the medium access process.
A. Non-orthogonal Multiple Access
As shown in Table I, grant acquisition and random access procedures in current standards are
two major sources of delay. This calls for novel approaches and fundamental shifts from current
protocols and standards originally designed for human communication to meet the requirements
for ultra-low latency applications. Though optimal in terms of per user achievable rate,
orthogonal multiple access (OMA) techniques, such as OFDMA in current LTE, are major
causes of the latency associated with the link establishment and random access. More

specifically, in existing wireless systems, radio resources are orthogonally allocated to the users
to deliver their messages. This requires the base station to first identify the users through
contention-based random access. This strategy suffers from severe collisions and high latencies
when the number of users increases.
Non-orthogonal multiple access (NOMA) has recently gained considerable attention as an
effective alternative to conventional OMA. In general, NOMA allows the signals from various
users to overlap by exploiting power, code or interleaver pattern at the expense of receiver
complexity. In the power-domain NOMA, which has been shown to be optimal in terms of
spectral efficiency [14], signals from multiple users are superimposed and successive
interference cancellation (SIC) is used at the receiver to decode the messages. Users do not need
to be identified at the base station beforehand, thus eliminating random access delay which is
significantly high in medium to high load scenarios [14].
Fig. 3 shows a comparison between NOMA and OMA in an uncoordinated scenario, where the
devices randomly choose a subband for their transmission. The number of subbands is denoted
by Ns and the total available bandwidth is assumed to be W = 100MHz. The bandwidth is
assumed to be uniformly divided into Ns subbands, each of W/Ns bandwidth. As can be seen,
when the number of devices is small, OMA slightly outperforms NOMA in terms of delay,
which is expected as the collision probability in this case is small and the devices can achieve
higher spectral efficiency as they are transmitting orthogonally. However, when the number of
devices is large, NOMA outperforms OMA, as it can effectively exploit the interference and
enable the devices to be decoded at the base station. In other words, in high traffic load scenarios,
OMA is mainly dominated by the random access collision which leads to unavoidable high

latencies, while NOMA supports a large number of devices with the desired latency, by
eliminating the random access phase and enabling the users to share the same radio resources.

Fig. 3. Delay versus the number of devices for NOMA and OMA.

The main benefits of NOMA come from the fact that it does not need separate grant acquisition
and random access phase, as the devices can send their data whenever they want to send. This
becomes more beneficial when the number of devices grows large, which is the scenario of
interest for most internet-of-things use cases. NOMA can also be easily combined with AFC
codes [11] to improve the spectral efficiency for each user, therefore providing a cross-layer
solution for reducing the delay. One solution to better satisfy the latency requirements for
different applications is to further divide the radio resources between the different uRLLC
applications. This will be further discussed in the next subsection. In this way, NOMA can be
further tuned to service a larger number of devices with the same requirements.
B. Resource Reservation via Resource Block Slicing
In the current LTE network, the management of radio resource blocks (RBs) for multiple
services is jointly optimized. As such, the latencies of different services are interdependent [15].

A traffic overload generated by one service can negatively impact the latency performance of
other services. To address this issue, we propose to reserve radio resources for each service. The
reservation is done by slicing RBs and allocating a slice to each service based on the traffic
demand. Moreover, if RBs in a slice are not used, they will be shared by other services. This type
of resource reservation method can achieve a high spectral efficiency and eliminate the latency
problem caused by the traffic overload issues coming from other services.
To evaluate the benefit of the proposed RB slicing on a LTE network, we conduct a simulation to
compare its performance with a legacy LTE network by using NS-3. Two types of services with
different data rates and latency requirements, i.e., low latency intelligent transportation systems
(ITS) with average packet sizes of 100 bytes and average packet intervals of 100ms per user, and
smart grid (SG) with average packet sizes of 300 bytes and average packet intervals of 80ms per
user, respectively, are considered in our simulation. The devices for the above services are
distributed in 1 km2 area according to a Poisson Point Process (PPP) with averages of 400 and
600 devices for ITS and SG, respectively, served by 4 LTE base stations, operating with 20MHz
bandwidth. The proportion of traffic load for each slice is approximated based on the ratio of the
number of users in a service over the total number of users in all services. Thus, for the proposed
RB slicing, we allocate 40% of available RBs exclusively for ITS devices transmissions, leaving
the remaining 60% RBs for SG devices transmissions. Note that all available RBs are shared by
ITS and SG equally in the current LTE network.
Fig. 4 shows the cumulative density function (CDF) for the end to end packet latencies under a
legacy LTE network and under the RB slicing regime that isolates the traffic demand of
intelligent transportation systems (ITS) sensor and smart grids (SG) from each other. By
performing RB slicing that reserves resources for each service, the latency is reduced from an

average of 10ms to 5ms and 6ms for ITS and SG devices, respectively, as shown in the small box
in Fig. 3. This simulation confirms the benefit of the proposed approach. The open future
research challenges can be then on how to dynamically optimize the proportion for the resources
reserved by multiple services with varying load as well as heterogeneous reliability and latency
requirements.

Fig. 4. The cumulative distribution function (CDF) of the end‐to‐end delay without and with radio resource block slicing2.

VII. Other Potential Techniques
In addition to the measures introduced in previous sections, there are other techniques that have
great potential to reduce the end-to-end latency of cellular systems. In what follows, we briefly
discuss the principles of four potential technologies and explain how they can reduce latency.
A. Cross-layer Error Control
Automatic Repeat reQuest (ARQ) is a commonly-used error control method for detecting packet
losses by using acknowledgements and timeouts. ARQ has been widely adopted in many
2

The authors would like to thank Zhouyou Gu for his assistance in simulating this figure.

communication networks with Transport Control Protocol (TCP). However, it introduces high
and unpredictable delays in wireless networks due to the time varying channel and user
contention over a common radio link. On the other hand, User Datagram Protocols (UDP), with
no ARQ retransmissions and lower overheads than TCP, have been used for delay sensitive
applications with no stringent requirements for low error probabilities, such as Voice over
Internet Protocol (VoIP), Video on Demand (VoD), Internet Protocol Television (IPTV) etc.
For emerging mission-critical applications over wireless networks, lower overheads are desirable
to reduce overall end to end latency. However, in order for UDP to be suitable for uRLLC, its
reliability needs to be substantially improved. Research on this has focused on the design of error
control schemes with minimal error protection at the physical layer and rateless coding for
erasure channels in the application layer. The research problems have been in optimizing the
redundancy split between the physical and application layers to have reliable transmission. This
approach involves a significant loss in the decoding error performance due to hard decision
decoding at the application layer and weak codes at the physical layer. A promising solution to
resolve this is to use short AFC codes in both the physical and the network layer and form a
concatenated code with soft output decoding at the physical and soft input decoding at the
network layer. Furthermore, the decoding of both AFC codes can be highly parallelized for a low
decoding delay.
B. Device-to-Device Communication
Device-to-device (D2D) communication refers to a radio technology that enables direct
communication between two physically close terminals. D2D has recently been considered as a
key solution for ultra-low latency applications, as it provides a direct link between traffic

participants, without going through the network infrastructure. D2D communication is a good fit
for vehicle-to-vehicle (V2V) communications to enable real-time safety systems, cooperative
collision avoidance and automated overtake. However, it may be not applicable to many other
mission-critical services, such as power systems or remote surgery with communication nodes
separated at large distances. Due to the global spectrum shortage, D2D links are expected to
operate within the same spectrum used by existing infrastructure-based communication systems
(e.g., cellular systems). This calls for highly efficient interference management techniques to
ensure the harmonious coexistence between D2D links and conventional links. Otherwise, the
latency gain introduced by D2D communication can easily disappear.
C. Mobile Edge Computing
Mobile edge computing (MEC) is a promising approach to promptly process computationally
intensive jobs offloaded from mobile devices. Edge computing modules can be installed at base
stations which are closer to sensing devices than data servers/clouds. To decrease job-processing
delays, edge computing modules are operated in a Software as a Service (SaaS) fashion. In other
words, a set of data processing software is in an always-on status, ready to process offloaded jobs
from sensing devices. The offloaded jobs can be processed immediately without waiting for
computing resource allocation, software initiation, and environment parameter configuration.
The data transfer between the sensing device and the computing module in the base station relies
on the existing air interface. A multiplexer/de-multiplexer at the base station can distinguish if
transmitted data are for computation offloading purpose. If so, the data is redirected to the edge
computing modules instead of the mobile core network. In fact, the implementation of edge
computing technologies is not mature in cellular networks. The key barrier stems from the
incompatibility of computing services and the existing LTE protocol stack. Modifying the

existing stack to accommodate computing services may cause substantial network reconstruction
and reconfiguration. Therefore, smoothly merging edge computing into the protocol stack is a
key future research direction.
D. Mobile Caching for Content Delivery
Smart mobile caching schemes are also effective solutions for improving the delay performance
of data intensive applications, e.g., multimedia, augment reality (AR) applications etc. Mobile
caching enables content reuse, which leads to drastic delay reductions and backhaul efficiency
improvements. The mobile cache can be installed at each base station. Whenever a mobile
device’s request “hits” a cached content, the base station intercepts the request and directly
returns the cached content without resorting to a remote server. Each base station determines the
cached contents through learning their popularities. Caching policies such as geo-based caching
and least frequently used eviction, etc. can be employed.

The selected contents are then

downloaded from remote servers. Downloading cached files is not a delay-sensitive task; hence,
it can be operated in a separate network without competing for network bandwidth with other
delay-sensitive data traffic. Despite the potential benefits of caching, it is still challenging to
realize these benefits in practice. This is because the cache size at the base station is limited, but
the number of possible contents can be unlimited. Thus, it is essential to determine how to wisely
cache a set of popular contents to maximize the hit rate.
VIII. Summary
This article has introduced the emerging applications, design challenges, and potential
approaches in the design of ultra-reliable low latency communications (uRLLC). We described
potential use cases of uRLLC in tele-surgery, smart transportation and industry automation and

presented the latency and reliability requirements for these applications. To pinpoint major
latency bottlenecks in current cellular networks, we showed a breakdown of the various delay
sources in an LTE system and found that a few orders of end-to-end latency reduction is required
to support the mission critical applications. To achieve this, each latency component needs to be
reduced significantly. Our initial results showed that short analog fountain codes, ultra-fast signal
processing, non-orthogonal multiple access and resource reservation via resource block slicing
are essential to reduce latency in the physical and multiple access layers. Furthermore, other
potential latency reduction measures, including cross-layer error control, device-to-device
communication, mobile edge computing and mobile caching, were briefly discussed. We hope
this article can encourage more research efforts toward the realization of uRLLC.
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