Towards the 1G of Mobile Power Network: RF, Signal and
System Designs to Make Smart Objects Autonomous

Bruno Clerckx1, Alessandra Costanzo2, Apostolos Georgiadis3, and Nuno Borges
Carvalho4
1
Imperial College London, UK, 2University of Bologna, Italy, 3Heriot-Watt University, UK,
4
University of Aveiro, Portugal
Email: b.clerckx@imperial.ac.uk, alessandra.costanzo@unibo.it, A.Georgiadis@hw.ac.uk,
nbcarvalho@ua.pt
Thanks to the quality of the technology and the existence of international standards,
wireless communication networks (based on radio-frequency RF radiation) nowadays
underpin the global functioning of our societies. The pursuit towards higher spectral efficiency
has been around for about 4 decades, with 5G expected in 2020. 5G and beyond will see the
emergence of trillions of low-power autonomous wireless devices for applications such as
ubiquitous sensing through an Internet of Things (IoT).
Wireless is however more than just communications. For very short range, wireless
power via Inductive Power Transfer is a reality with available products and standards (Wireless
Power Consortium, Power Matters Alliance, Alliance for Wireless Power, Rezence). Wireless
Power via RF (as in wireless communication) on the other hand could be used for longer range
via two different ways, commonly referred to as wireless energy harvesting (WEH) and (farfield or radiative) wireless power transfer/transmission (WPT). While WEH assumes RF
transmitters are exclusively designed for communication purposes whose ambient signals can
be harvested, WPT relies on dedicated sources designed exclusively for wireless power
delivery. Wireless Power via RF has long been regarded as a possibility for energising lowpower devices, but it is only recently that it has become recognised as feasible. Indeed,
according to [Hemour:2014], at a fixed computing load, the amount of requested energy falls
by a factor of two every year and a half due to the evolution of the electrical efficiency of
computer technology. This explains why relying on wireless power to perform meaningful
computation tasks at reasonable distances only became feasible in the last few years and
justifies this recent interest in wireless power.
Recent research advocates that the future of wireless networking goes beyond
conventional communication-centric transmission. In the same way as wireless (via RF) has
disrupted mobile communications for the last 40 years, wireless (via RF) will disrupt the
delivery of mobile power. However, current wireless networks have been designed for
communication purposes only. While mobile communication has become a relatively mature
technology, currently evolving towards its fifth generation, the development of mobile power is
in its infancy and has not even reached its first generation. Not a single standard on mobile
power and far-field WPT exists.
Despite being subject to regulations on exposure to electromagnetic fields as wireless
communication, wireless power brings numerous new opportunities. It enables proactive and
controllable energy replenishment of devices for genuine mobility so that they no longer
depend on centralised power sources. Hence, no wires, no contact, no (or at least reduced)
batteries (and therefore smaller, lighter and compact devices), an ecological solution with no
production/maintenance/disposal of trillions of batteries, a prolonged lifetime and a perpetual,
predictable and reliable energy supply as opposed to ambient energy-harvesting technologies
(solar, thermal, vibration). This is very relevant in future networks with ubiquitous and
autonomous low-power and energy limited devices, device-to-device communications and the
Internet-of-Things (IoT) with massive connections.
Interestingly, radio waves carry both energy and information simultaneously.
Nevertheless, traditionally, energy and information have been treated separately and have

evolved as two independent fields in academia and industry, namely wireless power and
wireless communication, respectively. This separation has for consequences that 1) current
wireless networks pump RF energy into the free space (for communication purposes) but do
not make use of it for energizing devices and 2) providing ubiquitous mobile power would
require the deployment of a separate network of dedicated energy transmitters. Imagine
instead a wireless network where information and energy flow together through the wireless
medium. Wireless communication, or Wireless Information Transfer (WIT), and WPT would
refer to two extreme strategies respectively targeting communication-only and power-only. A
unified Wireless Information and Power Transfer (WIPT) design would have the ability to softly
evolve in between those two extremes to make the best use of the RF spectrum/radiations
and network infrastructure to communicate and energize, and hence outperform traditional
systems relying on a separation of communications and power.
This article reviews some recent promising approaches to make the above vision
closer to reality. In contrast with articles commonly published by the microwave community
and the communication/signal processing community that separately emphasize RF, circuit
and antenna solutions for WPT on one hand and communications, signal and system designs
for WPT on the other hand, this review article uniquely bridges RF, signal and system designs
in order to bring those communities closer to each other and get a better understanding of the
fundamental building blocks of an efficient WPT network architecture. We start by reviewing
the engineering requirements and design challenges of making mobile power a reality. We
then review the state-of-the-art in a wide range of areas spanning sensors and devices, RF
design for wireless power and wireless communications. We identify their limitations and make
critical observations before providing some fresh new look and promising avenues on signal
and system designs for WPT.

Engineering Requirements and Design Challenges of the Envisioned Network
The followings are believed to be the engineering requirements and the main design
challenges: 1) Range: Deliver wireless power at distances of 5-100m for indoor/outdoor
charging of low-power devices; 2) Efficiency: Boost the end-to-end power transfer efficiency
(up to a fraction of percent/a few percent); 3) Non-line of sight (NLoS): Support LoS and NLoS
to widen the practical applications of this network; 4) Mobility support: Support mobile
receivers, at least for those at pedestrian speed; 5) Ubiquitous accessibility: Support
ubiquitous power accessibility within the network coverage area; 6) Seamless integration of
wireless communication and wireless power: Interoperate wireless communication and
wireless power via a unified wireless information and power transfer (WIPT); 7) Safety and
health: Resolve the safety and health issues of RF systems and comply with the regulations;
8) Energy consumption: Limit the energy consumption of the energy-constrained RF powered
devices.

Power Requirements and Consumption of Sensors and Devices
The Integrated Circuit industry is moving from the traditional computing power
paradigm towards a power efficiency (lowest joule per operation) paradigm. This ultra-low
power (ULP) electronics has opened the door to numerous applications in sensor networks
and IoT that do not need nm technology with billions of gates. Sensor nodes commonly require
power for the sensor itself, the data processing circuitry and the wireless data link (e.g. a few
bits/s for temperature sensors to a few kbits/s for ECG or blood pressure monitoring). The first
two functions commonly require less power. This can be attributed to the fact that while CMOS
technology scaling has conventionally provided exponential benefits for the size and power
consumption of digital logic systems, analog RF components, necessary for the data link, have
not seen a similar power scaling. In [Ay:2011], a CMOS image sensor consumes only
14.25µW. In [ADMP801], low power microphones consume 17µW and an ADC digitizing the
microphone output consumes 33µW. Popular protocols for sensor networks include Zigbee
and low power Bluetooth whose commercial-of-the-shelf transmitters consume 35mW
[CC2541]. WiFi is more power-hungry. Despite the progresses in the WiFi industry to design

chipsets for IoT applications by e.g. reducing power consumption in the standby mode to
20µW, an active WiFi transmission consumes around 600mW [Gainspan, CC3100MOD].
Nevertheless, in recent years, there has been significant enhancement with integrated ULP
System on Chip (SoC) and duty-cycled radio whose power consumption is nowadays in the
order of 10-100µW using custom protocols supporting 10-200kbps [Zhang:2013, Kim:2011,
Verma:2010, Pandey:2011]. The use of passive WiFi is also an alternative to generate 802.11b
transmission over distances of 10-30m (in line-of-sight and through walls) while only
consuming 10 and 60µW for 1 and 11 Mbps transmissions, respectively (3 to 4 orders of
magnitude lower than existing WiFi chipsets) [Kellogg:2016].
Observation: 10-100µW is enough to power modern wireless sensors and low-power
devices.

WPT RF Design
Since Tesla’s attempt in 1899, all WPT experiments in 1960-2000 were targeting longdistance and high power transmissions with applications such as Solar Powered Satellite and
wireless-powered aircraft [Brown:1984]. More recently, there has been a significant interest in
WPT and WEH for relatively low-power (e.g., from µW to a few W) delivery over moderate
distances (e.g., a few m to hundreds of m) [Falkenstein:2012, Popovic:2013], owing to the
fast-growing need to build reliable and convenient wireless power systems for remotely
charging various low- to medium-power devices, such as RFID tags, wireless sensors,
consumer electronics [Visser:2013, Popovic:2013a]. The interest in far-field wireless power
has spurred the creation of initiatives like COST IC1301 [Carvalho:2014] and a small number
of start-ups in recent years, namely Drayson Technologies, Powercast, Energous, Ossia.

Fig. 1: Block diagram of a conventional far-field WPT architecture.
Fig. 1 shows a generic wireless power delivery system, which consists of an RF
transmitter and an energy harvester made of a rectenna (antenna and rectifier) and a power
management unit (PMU). Since the quasi-totality of electronics requires a DC power source,
a rectifier is required to convert RF to DC. The recovered DC power then either supplies a low
power device directly, or is stored in a battery or a super capacitor for high power low dutycycle operations. It can also be managed by a DC-to-DC converter before being stored.
Referring to Fig 1, the end-to-end power transfer efficiency e can be expressed as
e=

#$%,'(
#$%,()

=

#*+,() #*+,*) #$%,*) #$%,'(
#$%,() #*+,() #*+,*) #$%,*)
,-

,/

,0

.

(1)

,1

In WEH, the transmitter in Fig 1 is an RF communication transmitter, not controllable
and not optimized for power delivery purposes. Given the typical power density between 10-3
and 10-1 µW/cm2 observed indoor and outdoor at distances from 25 to 100m from a GSM900
base station, WEH is thought unlikely sufficient for powering devices with a few cm2 in size
requiring 10-100µW [Visser:2013]. In WPT, the power transmitter of Fig 1 can be fully
optimized. Therefore, WPT offers more control of the design and room for enhancement of e.
We briefly review the techniques used to enhance e2 , e3 , e4 and e5 .
The DC-to-RF conversion efficiency e2 maximization can leverage a rich literature on
power amplifier (PA) design and rely on transmit signals with constrained Peak-to-Average
Power Ratio (PAPR).

The RF-to-RF conversion efficiency e3 is a bottleneck and requires highly directional
transmission. Common approaches in the RF literature rely on real-time reconfiguration of
time-modulated arrays based on localization of the power receivers [Masotti:2016], phasedarrays [Takahashi:2011] or retrodirective arrays [Miyamoto:2002].
The RF-to-DC conversion efficiency e4 maximization relies on the design of efficient
rectennas. A rectenna harvests electromagnetic energy, then rectifies and filters it using a low
pass filter. Its analysis is challenging due to its nonlinearity, which in turn renders its
implementation hard and subject to several losses due to threshold and reverse-breakdown
voltage,
devices
parasitics,
impedance
matching,
harmonic
generation
[Yoo:1992,Strassner:2013,Valenta:2014]. In WPT, the rectenna can be optimized for the
specific operating frequencies and input power level. It is more challenging in WEH since the
rectenna is designed for a broad range of input power densities (from a few nW/cm2 to a few
µW/cm2) and spectrum (TV, WiFi, 2/3/4G) [Costanzo:2016]. In order to address the large
aggregate frequency spectrum of ambient RF signals, multiband [Masotti:2013,Pinuela:2013,
Niotaki:2014,Belo:2016] and broadband [Kimionis:2017,Song:2015,Sakaki:2014] rectifier
designs have been proposed. In the case of multiband designs one may maximize e4 over a
number of narrowband frequency regions, whereas in the case of broadband (ultra-wideband)
designs one may cover a much larger frequency band however sacrificing the obtained
maximum efficiency. Various rectifier technologies exist, including the popular Schottky diodes
[Falkenstein:2012,Hagerty:2004], CMOS [Le:2008], active rectification [Roberg:2012],
spindiode [Hemour:2014], backward tunnel diodes [Lorenz:2015]. Assuming P78,9: =1W, 5dBi Tx/Rx antenna gain, a continuous wave (CW) at 915MHz, e4 of state-of-the-art rectifiers
is 50% at 1m, 25% at 10m and about 5% at 30m [Hemour:2014]. This severely limits the range
of WPT. Moreover, with the current rectifier technologies, e4 drops from 80% at 10mW to 40%
at 100µW, 20% at 10µW and 2% at 1µW [Valenta:2014, Hemour:2014]. This is due to the
diode not being easily turned on at low input power. Enhancements for the very low power
regime (below 1 µW) rely on spindiodes [Hemour:2014] and backward tunnel diodes
[Lorenz:2015]. For typical input power between 1 µW and 1mW, low barrier Schottky diodes
remain the most competitive and popular technology [Hemour:2014,Costanzo:2016,
Valenta:2014]. e4 also decreases as the frequency increases due to parasitic losses
[Valenta:2014]. The rectifier topology also impacts e4 . A single diode is preferred at low power
(1-500µW) and multiple diodes (voltage doubler/diode bridge/charge pump) favoured above
500µW [Costanzo:2016, Boaventura:2013]. The efficiency is also dependent on the input
power level and the output load variations. One possibility to minimize sensitivity to output
load variation is to use a resistance compression network [Niotaki:2014], while topologies
using multiple rectifying devices each one optimized for a different range of input power levels
can enlarge the operating range versus input power variations and avoid, within the power
range of interest, the saturation effect (that creates a sharp decrease in e4 ) induced by the
diode breakdown [Sun:2013]. This can be achieved using e.g. a single-diode rectifier at low
input power and multiple diodes rectifier at higher power.
Interestingly the rectenna design is not the only factor influencing e4 . Due to the rectifier
nonlinearity, the input waveform (power and shape) also influences e4 in the low input power
regime
(1µW-1mW)
[Trotter:2009,
Boaventura:2011,
Valenta:2013,Valenta:2015,
Collado:2014]. A 20dB gain (in terms of P;<,7: ) of a multisine over a CW excitation at an
average input power of -15dBm was shown in [Valenta:2013]. It is to be noted though that the
output filter is also important in relation to the tone separation in order to boost the performance
of multisine waveform [Boaventura:2014,Pan:2015]. High PAPR signals were also shown
beneficial in [Collado:2014]. It was nevertheless argued in [Blanco:2016] that the
instantaneous power variance is more accurate than PAPR to characterise the effect of
modulation on the rectifier efficiency. Suitable signals and waveforms therefore exploit the
nonlinearity to boost e4 at low input powers and extend WPT range [Boaventura:2013].
Modulation also has an impact on e4 . In [Vera:2010], QPSK modulation was shown to be
beneficial to e4 compared to a CW in the low power regime -20dBm to 0dBm.
[Fukuda:2014,Sakaki:2014] reported somewhat contradicting behaviors in the higher input

power regime of 0-20dBm. In [Fukuda:2014], PSK and QAM modulations were shown
beneficial to e4 compared to a CW, while they were shown detrimental in [Sakaki:2014].
Finally, [Bolos:2016] argues that one may or may not get an advantage from using multisines
or other modulated signals depending on the load and input power.
The DC-to-DC conversion efficiency e5 is enhanced by dynamically tracking the rectifier
optimum load, e.g. dc-to-dc switching converters dynamically track the maximum power point
(MPP) condition [Dolgov:2010,Costanzo:2012]. Due to the variable load on the rectenna, the
changes in diode impedance with power level and the rectifier nonlinearity, the input
impedance of the rectifier becomes highly variable, which renders the matching hard, not to
mention a joint optimization of the matching and load for multisine signals [Bolos:2016].
Interestingly, the maximization of e is not achieved by maximizing e2 , e3 , e4 , e5
independently from each other, and therefore simply concatenating the above techniques. This
is because e2 , e3 , e4 , e5 are coupled with each other due to the rectifier nonlinearity, especially
at input power range 1 µW -1 mW. Indeed, e4 is a function of the input signal shape and power
to the rectifier and therefore a function of 1) the transmit signal (beamformer, waveform, power
allocation) and 2) the wireless channel state. Similarly, e3 depends on the transmit signal and
the channel state and so is e2 , since it is a function of the transmit signal PAPR.
Some recent approaches optimize the system using numerical software tools based on
a combination of full-wave analysis and nonlinear harmonic balance techniques in order to
account for nonlinearities and electromagnetic couplings [Masotti:2016,Costanzo:2014]. This
approach would provide very high accuracy but has the drawback to hold only for an offline
optimization of the system, not for an adaptive WPT whose transmit signal is adapted every
few ms as a function of the channel state, not to mention for an entire WPT network with
multiple transmitters and receivers.
Observations:
First, the majority of the technical efforts in the wireless power literature has been devoted to
the design of the energy harvester but much less emphasis has been put on signals design
for WPT.
Second, the emphasis has remained on point-to-point (single user) transmission.
Third, research has recognized the importance of non-linearity of the rectenna in WPT system
design but has focused to a great extent on decoupling the WPT design by optimizing the
transmitter and the energy harvester independently from each other.
Fourth, multipath and fast fading, critical in NLoS, have been ignored despite playing a key
role in wireless transmissions and having a huge impact on the signal shape and power at the
rectenna input. Recall indeed that multipath has for consequences that transmit and received
(at the input of the rectenna) waveforms are completely different.
Fifth, WPT design has remained very much centered around an open-loop approach with
waveform being static and beamforming relying on tags localization, not on the channel state.
Sixth, the design of the transmit signals is heuristic (with conclusions exclusively based on
observations from measurements using various predefined and standard waveforms) and
there exist no systematic approach and performance bounds to design and evaluate them.
Waveform and beamformer have been studied independently, despite being part of the same
transmit signal.
To tackle the listed challenges, we need:
First, a closed-loop and adaptive WPT architecture with a reverse communication link from
the receiver to the transmitter that is used to support various functions such as channel
feedback/training, energy feedback, charging control, etc. The transmitter should be able to
flexibly adjust the transmission strategy jointly optimized across space and frequency (through
beamforming and waveform) in accordance with the channel status (commonly called Channel
State Information - CSI), and thus, renders state-of-the-art Multi-Input Multi-Output (MIMO)
processing an indispensable part of WPT. An example of a closed-loop and adaptive WPT
architecture is displayed in Fig. 2.

Fig. 2: Block diagram of a closed-loop and adaptive WPT architecture.
Second, a systematic approach to design and optimize, as a function of the channel, the
signal at the transmitter (encompassing beamforming and waveform) so as to maximize e3 ×e4
subject to transmit power and PAPR constraints. This requires to capture the rectifier
nonlinearity as part of the signal design and optimization. Such a systematic design
methodology will lead to the implementation of efficient strategies as part of the “transmission
optimization” module of Fig. 2.
Third, a link and system design approach that takes wireless power from a rectenna paradigm
to a network paradigm with multiple transmitters and/or receivers. Instances of such network
architectures may be a deployment of co-located transmit antennas delivering power to
multiple receivers or the dense and distributed deployment of well-coordinated
antennas/transmitters, as illustrated in Fig. 3.

Fig. 3: Illustration of a WPT network with co-located/distributed transmit antennas and
multiple receivers (P T/R: Power Transmitter/Receiver).

Leveraging Ideas from Wireless Communications
The fundamental limits of a communication network design lie in information and
communication theories that derive the capacity of wireless channels (point-to-point,
broadcast, multiple access, interference channel with single and multiple antennas) and
identify transmission and reception strategies to achieve it, most commonly under the
assumption of a linear communication channel with additive white Gaussian noise (AWGN)
[68-70]. In the 70s till early 2000s, the research emphasis was on a link optimization, i.e.
maximizing the point-to-point spectral efficiency (bits/s/Hz) with advances in modulation and
waveforms, coding, MIMO, Channel State Information (CSI) feedback and link adaptation,
communication over (multipath) fading channel. CSI feedback enables to dynamically adapt
the transmission strategies as a function of the channel state. It leads to a drastic increase in
rate and complexity reduction in receiver design. The emphasis in 4G design shifted towards
a system optimization, with a more interference-centric system design. MIMO evolved into a
multi-link/user/cell MIMO. Multiple users are scheduled in the same time-frequency resource
onto (ideally) non-interfering spatial beams. This led to significant features such as multi-user
MIMO, multi-user fairness and scheduling and multi-point cooperation. The availability of
accurate CSI at the Transmitter (CSIT) is also crucial for multi-user multi-antenna wireless
communication networks, for beamforming and interference management purposes. Some
promising technologies consist in densifying the network by adding more antennas either in a

distributed or in a co-localized manner. The distributed deployment leads to dense network
(with a high capacity backbone) requiring interference mitigation techniques, commonly
denoted as Coordinated Multi-Point transmission and reception (CoMP) in 3GPP, and
classified into joint processing (or Network MIMO) and coordinated scheduling, beamforming
and power control. Co-localized deployment leads to Massive/Large-Scale MIMO where a
base station designs pencil beams (with large beamforming gain) serving its own users, using
per-cell design rules, while simultaneously avoiding inter-cell interference. The reader is
invited to consult [Clerckx:2013] for fundamentals and designs of state-of-the-art MIMO
wireless communication networks.
Observations:
First, wireless power and communication systems share the same medium and techniques
inspired by communications, such as MIMO, closed-loop operation, CSI acquisition and
transmitter coordination are expected to be useful to WPT.
Second, existing techniques developed for wireless communications cannot be directly
applied to wireless power, due to their distinct design objectives (rate vs energy), practical
limitations (hardware and power constraints), receiver sensitivities (e.g. -30dBm for rectenna
vs -60dBm for information receivers), interference (beneficial in terms of energy harvesting vs
detrimental in communications) and models (linear wireless communication channel vs
nonlinear wireless power channel due to the rectifier).

WPT Signal and System Design
Aside the traditional WPT RF design, a new and complementary line of research on
communications and signal design for WPT has emerged recently in the communication
literature [Zeng:2017] and is briefly reviewed in the sequel. This includes among others the
design of efficient transmit signals (including waveform, beamforming and power allocation),
CSI acquisition strategies, multiuser transmission strategies, integration with communications
and system prototyping. Importantly, the nonlinearity of the rectifier has to be captured as part
of the signal and system design and optimization as it induces coupling among the various
efficiencies.
Let us first consider a point-to-point scenario with a single transmitter and receiver. The
first systematic approach towards signal design in adaptive closed-loop WPT was proposed in
[Clerckx:2015,Clerckx:2016], where the transmit signal, accounting jointly for multisine
waveform, beamforming and power allocation, is optimized as a function of the CSI to maximize
𝑒3 ×𝑒4 subject to optional transmit PAPR constraints. Uniquely, such a signal design resolves
some limitations of the WPT literature by optimally exploiting a beamforming gain, a frequency
diversity gain and the rectifier nonlinearity. The rectifier nonlinearity was modelled using a Taylor
expansion of the diode characteristic, which is a popular model in the RF literature
[Boaventura:2011,Boaventura:2013]. The phases of the optimized waveform can be computed
in closed-form while the magnitudes result from a non-convex optimization problem that can be
solved using convex optimization techniques, so-called Reversed Geometric Program (GP).
Multiple observations were made in [Clerckx:2015,Clerckx:2016]. First, it was observed that the
derived adaptive and optimized signals designed accounting for the nonlinearity are more
efficient than non-adaptive and non-optimized multisine signals (as used in [Trotter:2009,
Boaventura:2011, Valenta:2013, Valenta:2015]). Second, the rectifier nonlinearity was shown
essential to design efficient wireless power signals and ignoring it leads to inefficient signal
design in the low power regime. Third, the optimized waveform design favours a power allocation
over multiple frequencies and those with stronger frequency-domain channel gains are allocated
more power. Fourth, multipath and frequency-selective channels were shown to have significant
impact of DC output power and waveform design. Though multipath is detrimental to performance
with non-adaptive waveforms, it is beneficial with channel-adaptive waveform and leads to a
frequency diversity gain.
As an illustration, Fig. 4 (top) displays the magnitude of the frequency response of a given
realization of the wireless channel over a 10MHz-bandwidth. We consider a multisine waveform

with 16 sinewaves uniformly spread within the 10MHz. Assuming this channel has been acquired
to the transmitter, the magnitudes of the optimized waveform on the 16 frequencies can be
computed and are displayed on Fig. 4 (bottom). Interestingly, in contrast with the waveforms
commonly used in the RF literature [Trotter:2009, Boaventura:2011, Valenta:2013,
Valenta:2015, Collado:2014] that are non-adaptive to the channel state, the optimized adaptive
waveform has a tendency to allocate more power to frequencies exhibiting larger channel gains.

Fig. 4: Frequency response of the wireless channel and WPT waveform magnitudes (N = 16)
for 10 MHz bandwidth [Clerckx:2016].
The performance benefits of those optimized channel-adaptive multisine waveforms
over the non-adaptive design (in-phase multisine with uniform power allocation) of
[Trotter:2009, Boaventura:2011, Valenta:2013, Valenta:2015] has been validated using ADS
and PSpice simulations with a single series rectifier in a WiFi-like environment at 5.18GHz for
an average input power of about -12dBm in [Clerckx:2015] and -20dBm in [Clerckx:2016,
Clerckx:2017]. As illustrated in Fig. 5, for a single transmit antenna and a single series rectifier
subject to an average input power of -12dBm and multipath fading, the gains in terms of
harvested DC power are very significant with over 100% gains for 4 sinewaves and about
200% gain for 8 sinewaves over the non-adaptive design. Significant performance gains have
also been validated in [Clerckx:2016] at -20dBm average input power for various bandwidths
and in the presence of multiple transmit antennas where waveform and beamforming are
jointly designed. Moreover, it was interestingly shown in [Clerckx:2017] that the systematic
signal design approach of [Clerckx:2016] actually is applicable to and provides gains (100%200%) in a wide range of rectifier topologies, e.g. single series, voltage doubler, diode bridge.
Details on circuit design and simulation assumptions can be found in [Clerckx:2016,
Clerckx:2017].

Fig. 5: DC power vs. number of sinewaves N for adaptive and non-adaptive waveforms.
Importantly, systematic and optimized signal designs of [Clerckx:2016] also show that
contrary to what is claimed in [Valenta:2015,Collado:2014], maximizing PAPR is not always
the right approach to design efficient wireless power signal. High PAPR is a valid metric in
WPT with multisine waveforms if the channel is frequency flat, not in the presence of multipath
and frequency selectivity. This can be inferred from Fig 5 where the non-adaptive multisine
waveform leads to a much lower DC power despite exhibiting a significantly higher transmit
PAPR compared to the adaptive waveform. Recall indeed that the adaptive waveform will
unlikely allocate power uniformly across all sinewaves, as it emphasizes the ones
corresponding to strong frequency domain channel. This leads to waveforms whose PAPR is
lower than the non-adaptive in-phase multisine waveform with uniform power allocation.
Results in [Clerckx:2016] also highlighted the potential of a large scale multi-sine multiantenna closed-loop WPT architecture. In [Huang:2016,Huang:2017], such a promising
architecture was designed and shown to be an essential technique in enhancing 𝑒 and
increasing the range of WPT for low-power devices. It enables highly efficient very far-field
wireless charging by jointly optimizing transmit signals over a large number of frequency
components and transmit antennas, therefore combining the benefits of pencil beams and
waveform design to exploit the large beamforming gain of the transmit antenna array and the
non-linearity of the rectifier at long distances. The challenge is on the large dimensional problem,
which calls for a reformulation of the optimization problem. The new design enables orders of
magnitude complexity reduction in signal design compared to the Reverse GP approach.
Another low-complexity adaptive waveform design approach expressed in closed-form (hence,
suitable for practical implementation) has been proposed in [Clerckx:2017] and shown to
perform close to the optimal design. Fig 6 illustrates how the rectifier output voltage decreases
with the range for several values of the number of sinewaves N and transmit antennas M in the
multisine transmit waveform. By increasing both N and M, the range is expanded thanks to the
optimized channel-adaptive multisine waveforms that jointly exploit a beamforming gain, a
frequency diversity gain and the rectifier nonlinearity.

Fig. 6: Rectifier average output voltage as a function of the Tx-Rx distance [Huang:2017].
Note that in those recent progress on signal design, despite the presence of many transmit
antennas and sinewaves, a single receive antenna and rectifier per terminal has been assumed.
It would be interesting to understand how to extend the signal design to multiple receive
antennas. This brings the problem of RF or dc combining or mixed RF-dc combining
[Popovic:2014, Gutmann:1979, Shinohara:1998].
Discussions so far assumed deterministic multisine waveforms. It is of significant interest
to understand how modulated waveforms perform in comparison to deterministic waveforms
and how modulation could be tailored specifically for WPT to boost the end-to-end power
transfer efficiency. This would also open the way to understanding how to design unified and
efficient signals for the simultaneous transmission of information and power. A modulated
waveform exhibits randomness and this randomness has an impact on the amount of harvested
DC power. Interestingly, it was shown in [Clerckx:2017b] that for single-carrier transmission,
modulation using circularly symmetric complex Gaussian (CSCG) inputs is beneficial to the
performance compared to an unmodulated continuous wave. This gain comes from the large
fourth order moment offered by CSCG inputs which is exploited by the rectifier nonlinearity. Even
further gain can be obtained using asymmetric Gaussian inputs [Varasteh:2017]. On the other
hand, for multi-carrier transmission, modulation using CSCG inputs was shown in
[Clerckx:2017b] to be less efficient than a multisine because of the independent randomness
across carriers, which leads to random fluctuations. This contrasts with the periodic behavior of
deterministic multisine waveforms that are more suitable to turn on and off the rectifier
periodically. Interestingly, it was noted in [Clerckx:2017b] that PAPR is not an appropriate metric
to assess the suitability of a general modulated waveform for WPT. Nevertheless, despite all
those recent progress on signal design for WPT, the optimum input distribution remains unknown
and so is the optimum waveform.
We now understand that a systematic design of waveform design (including modulation,
beamforming, power allocation) is a key technique to jointly exploit a beamforming gain, the
channel frequency-selectivity and the rectifier nonlinearity, so as to enhance the end-to-end
power transfer efficiency and the range of WPT. One challenge is that those waveforms have
been designed assuming perfect CSI at the transmitter. In practice, this is not the case and the
transmitter should find ways to acquire the CSI. Various strategies exist, including forward-link
training with CSI feedback, reverse-link training via channel reciprocity, power probing with
limited feedback [Zeng:2017]. The first two are reminiscent of strategies used in modern
communication systems [Clerckx:2013]. The last one is more promising and tailored to WPT as
it is implementable with very low communication and signal processing requirements at the
terminal. It relies on harvested DC power measurement and on a limited number of feedback
bits for waveform selection and refinement [Huang:2017b]. In the waveform selection strategy,
the transmitter transmits over multiple timeslots with every time a different waveform precoder
within a codebook, and the receiver reports the index of the precoder in the codebook that leads
to the largest harvested energy. In the waveform refinement strategy, the transmitter sequentially

transmits two waveforms in each stage, and the receiver reports one feedback bit indicating an
increase/decrease in the harvested energy during this stage. Based on multiple one-bit
feedback, the transmitter successively refines waveform precoders in a tree-structured
codebook over multiple stages.
Wireless power networks are however not limited to a single transmitter and receiver. Let
us now consider the presence of a single transmitter and multiple users/receivers, with each
receiver equipped with one rectenna. In this multi-user deployment, the energy harvested by a
given rectenna in general depends on the energy harvested by the other rectennas. Indeed, a
given waveform may be suitable for a given rectenna but found inefficient for another rectenna.
Hence, there exists a trade-off between the energy harvested by the different rectennas. The
energy region formulates this trade-off by expressing the set of all rectenna harvested energy
that are simultaneously achievable. It is mathematically written as a weighted sum of harvested
energy where by changing the weights we can operate on a different point of the energy region
boundary. Strategies to design WPT waveforms in this multi-user/rectenna deployment were
discussed in [Clerckx:2016, Huang:2017]. Fig 7 illustrates such an energy region for a two-user
scenarios with a multisine waveform spanning 20 transmit antennas and 10 frequencies. The
key message here is that by optimizing the waveform to jointly deliver power to the two users
simultaneously, we get an energy region (‘weighted sum’) that is larger than the one achieved
by doing a timesharing approach, i.e. TDMA, where the transmit waveform is optimized for a
single user at a time and each user is scheduled to receive energy during a fraction of the time.

Fig. 7: 2-user energy region with M = 20 and N = 10 [Huang:2017].
Moving towards an entire network made of many transmitters and receivers, a network
architecture needs to be defined [Zeng:2017]. This may consist in having all transmitters
cooperating to jointly design the transmit signals to multiple receivers or having a local
coordination among the transmitters such that a given receiver is served by a subset of the
transmitters or the simplest scenario where each receiver is served by a single transmitter. This
leads to different resource allocation and charging control strategies (centralized vs distributed)
and requirements in terms of CSI sharing and acquisition at the different transmitters. Results
in [Zeng:2017] show that distributing antennas in a coverage area (as in Fig 3) and enabling
cooperation among them distributes energy more evenly in space and therefore potentially
enhances the ubiquitous accessibility of wireless power, compared to a co-located deployment.
It also avoids creating strong energy beams in the direction of the users, which is desirable from
a health and safety perspective.
Demonstrating the feasibility of the aforementioned signal and system designs through
prototyping and experimentation remains a largely open challenge. It requires the
implementation of a closed-loop WPT architecture with a real-time over-the-air transmission
based on a frame structure switching between a channel acquisition phase and wireless power
transfer phase. The channel acquisition needs to be done at the millisecond level (similarly to
CSI acquisition in communication). Different blocks need to be built, namely channel estimation,

channel-adaptive waveform design and rectenna. The first prototype of a closed-loop WPT
architecture based on channel-adaptive waveform optimization and dynamic channel
acquisition, as illustrated in Fig 8, was recently reported in [Kim:2017] with further enhancements
in [Kim:2017b]. Importantly, all experimental results validate the theory developed in
[Clerckx:2016,Clerckx:2017] and fully confirm the following observations: 1) diode nonlinearity
is beneficial to WPT performance and is to be exploited in systematic waveform design, 2) the
wireless propagation channel has a significant impact on signal design and system
performance, 3) CSI acquisition and channel-adaptive waveforms are essential to boost the
performance in frequency-selective channels (as in NLoS scenarios), 4) larger bandwidths
benefit from a channel frequency diversity gain, 5) PAPR is not an accurate metric to assess
and design waveforms for WPT in general frequency-selective channels. The performance gain
of channel-adaptive multisine waveforms versus non-adaptive multisine waveform in a NLoS
deployment with a single antenna at the transmitter and receiver is illustrated in Fig 9. We note
the significant boost of the average harvested DC power at the rectenna output by 105% over
an open-loop WPT architecture with non-adaptive multisine waveform (with the same number
of sinewaves) and by 170% over a continuous wave.

Fig. 8: Prototype architecture with 3 key modules: signal optimization, channel acquisition and
energy harvester [Kim:2017,Kim:2017b].

Fig. 9: Harvested DC power with the architecture of Fig 8 in an indoor NLoS deployment as a
function of N uniformly spread within a 10MHz bandwidth [Kim:2017b].
Ultimately wireless power and wireless communications will have to be integrated. This
calls for a unified Wireless Information and Power Transfer (WIPT) paradigm. A major challenge
is to characterize the fundamental tradeoff between conveying information and energy wirelessly
[Varshney:2008, Grover:2010, Zhang:2013] and to identify corresponding transmission
strategies. Leveraging the aforementioned wireless power signal designs, it has been shown
that the rectifier nonlinearity has profound impact on the design of WIPT
[Clerckx:2016b,Clerckx:2017b, Varasteh:2017]. In contrast with the classical capacity achieving
CSCG input distribution, rectifier nonlinearity leads to input distribution that are asymmetric
Gaussian in single-carrier transmission over frequency flat channels [Varasteh:2017] and to nonzero mean Gaussian in multi-carrier transmissions [Clerckx:2016b,Clerckx:2017b].
Nevertheless, the optimal input distribution and transmit signal strategy for WIPT remain
unknown. Those refreshing results are in sharp contrast with earlier results of [Varshney:2008,
Grover:2010, Zhang:2013] that ignore the rectifier nonlinearity and therefore rely on the
conventional capacity-achieving CSCG input distribution.
Observations:
First, the above results show the huge potential in a systematic signal and system design and
optimization approach towards efficient WPT and WIPT that accounts for the unique
characteristic of wireless power, namely the non-linearity.
Second, the nonlinearity radically changes the design of WPT and WIPT: it 1) leads to a WIPT
design different from that of conventional wireless communication (whose channel is assumed
linear), 2) favours a different input distribution, signal design, transceiver architecture and use
of the RF spectrum, 3) is beneficial to increase the rectifier output DC power and enlarge the
rate-energy region.
Third, an adaptive signal design approach provides a different paradigm compared to the
traditional WPT design. It leads to an architecture where the rectenna is fixed as much as
possible (e.g. with a fixed load) but the transmit signal is adaptive in contrast with the approach
in the RF literature where the waveform is fixed and the rectenna/PMU is adaptive (e.g.
dynamic load control). Since the wireless channel changes quickly (10ms order), it can be
impractical for energy-constrained devices to dynamically compute and adjust the matching
and the load as a function of the channel. Even though both approaches are complementary,
the adaptive signal approach makes the transmitter smarter and decreases the need for
power-hungry optimization at the devices. Nevertheless, adaptation implies acquiring CSIT,
which is an important challenge to be addressed. Ultimately, it is envisioned that an entire endto-end optimization of the system should be conducted, likely resulting in an architecture
where the transmit signals and the rectennas adapt themselves dynamically as a function of
the channel state.
Conclusions:
An integrated signal and system optimization has been introduced as the strategic approach
to realize the first generation of a mobile power network and to enable energy autonomy of
pervasive devices, such as smart objects, sensors and embedded systems in a wide range of
operating conditions.
It has been shown that the nonlinear nature of this design problem, both for the transmitter
and for the receiver sides, must be accounted for the signal and the circuit- level design. In
this way, a new architecture of the system is foreseen enabling simultaneous WPT and WIPT
while enhancing the power transfer efficiency at ultra-low power levels. Techniques for
dynamic tracking of the channel changes need to be exploited to adaptively modify the
transmitted energy, both in terms of its waveform shape and of its intensity, with the twofold
advantage of reducing the complexity of the rectenna and of the PMU design while keeping
the rectenna itself in its own optimum operating conditions.

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