A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

Abstract

After about a decade of intense research, spurred by both economic and operational considerations, and by en- vironmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribu- tion to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.

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A Survey of Energy-Efficient Techniques for 5G
Networks and Challenges Ahead
Stefano Buzzi, Senior Member, IEEE, Chih-Lin I, Senior Member, IEEE, Thierry E. Klein, Member, IEEE, H.
Vincent Poor, Fellow, IEEE, Chenyang Yang, Senior Member, IEEE, and Alessio Zappone, Member, IEEE
Abstract—After about a decade of intense research, spurred
by both economic and operational considerations, and by en-
vironmental concerns, energy efficiency has now become a key
pillar in the design of communication networks. With the advent
of the fifth generation of wireless networks, with millions more
base stations and billions of connected devices, the need for
energy-efficient system design and operation will be even more
compelling. This survey provides an overview of energy-efficient
wireless communications, reviews seminal and recent contribu-
tion to the state-of-the-art, including the papers published in this
special issue, and discusses the most relevant research challenges
to be addressed in the future.
Index Terms—Energy efficiency, 5G, resource allocation, dense
networks, massive MIMO, small cells, mmWaves, visible-light
communications, cloud RAN, energy harvesting, wireless power
transfer.
I. INTRODUCTION
ENERGY consumption has become a primary concern
in the design and operation of wireless communication
systems. Indeed, while for more than a century communication
networks have been mainly designed with the aim of optimiz-
ing performance metrics such as the data-rate, throughput, la-
tency, etc., in the last decade energy efficiency has emerged as
a new prominent figure of merit, due to economic, operational,
and environmental concerns. The design of the next genera-
tion (5G) of wireless networks will thus necessarily have to
consider energy efficiency as one of its key pillars. Indeed,
5G systems will serve an unprecedented number of devices,
providing ubiquitous connectivity as well as innovative and
rate-demanding services. It is forecast that by 2020 there will
be more than 50 billion connected devices [1], i.e. more that
6 connected devices per person, including not only human-
type communications, but also machine-type communications.
The vision is to have a connected society in which sensors,
S. Buzzi is with the Department of Electrical and Information Engineer-
ing at the Universit`
a di Cassino e del Lazio Meridionale, Cassino, Italy
(buzzi@unicas.it).
C.-L. I is with the Green Communication Research Center, China Mobile
Research Institute, Beijing 100053, China (e-mail: icl@chinamobile.com).
T. E. Klein is with Nokia Innovation Steering, Nokia, Murray Hill, NJ
07974 USA (thierry.klein@nokia.com).
C. Yang is with the School of Electronics and Information Engineering at
Beihang University, Beijing, China (cyyang@buaa.edu.cn).
H. V. Poor is with the Department of Electrical Engineering, Princeton
University, Princeton, NJ 08544 USA (poor@princeton.edu).
A. Zappone is with the Communication Department of the Technische
Universit¨
at Dresden, Dresden, Germany (Alessio.Zappone@tu-dresden.de).
This research was supported in part by the U. S. National Science
Foundation under Grants ECCS-1343210 and ECCS-1549881, and also by
the German Research Foundation (DFG) project CEMRIN, under grant ZA
747/1-3.
cars, drones, medical and wearable devices will all use cellular
networks to connect with one another, interacting with human
end-users to provide a series of innovative services such
as smart homes, smart cities, smart cars, telesurgery, and
advanced security. Clearly, in order to serve such a massive
number of terminals, future networks will have to dramatically
increase the provided capacity compared to present standards.
It is estimated that the traffic volume in 5G networks will reach
tens of Exabytes (10006Bytes) per month. This requires the
capacity provided by 5G networks to be 1000 times higher
than in present cellular systems [2]. Trying to achieve this
ambitious goal relying on the paradigms and architectures of
present networks is not sustainable, since it will inevitably lead
to an energy crunch with serious economic and environmental
concerns.
Economic concerns. Current networks are designed to max-
imize the capacity by scaling up the transmit powers. However,
given the dramatic growth of the number of connected devices,
such an approach is not sustainable. Using more and more
energy to increase the communication capacity will result in
unacceptable operating costs. Present wireless communication
techniques are thus simply not able to provide the desired
capacity increase by merely scaling up the transmit powers.
Environmental concerns. Current wireless communication
systems are mainly powered by traditional carbon-based en-
ergy sources. At present, information and communication tech-
nology (ICT) systems are responsible for 5% of the world’s
CO2emissions [3], [4], but this percentage is increasing as
rapidly as the number of connected devices. Moreover, it is
foreseen that 75% of the ICT sector will be wireless by 2020
[5], thus implying that wireless communications will become
the critical sector to address as far as reducing ICT-related
CO2emissions is concerned.
A. Averting the energy crunch
In order to avert the energy crunch, new approaches to
wireless network design and operation are needed. The key
point on which there is general consensus in the wireless
academic and industry communities, is that the 1000×capac-
ity increase must be achieved at a similar or lower power
consumption as today’s networks [6], [7]. This means that
the efficiency with which each Joule of energy is used to
transmit information must increase by a factor 1000 or more.
Increasing the network energy efficiency has been the goal of
the GreenTouch consortium [8], which was founded in 2010 as
an open global pre-competitive research consortium with the

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Energy-Efficient
& Green 5G
Resource
Allocation
Deployment
& Planning
Hardware
Solutions
& Transfer
Energy Harvesting
Figure 1. Energy-efficient 5G technologies.
focus to improve network energy efficiency by a factor 1000
with respect to the 2010 state of the art reference network. The
consortium published a technology roadmap and announced its
final results in its “Green Meter” research study [9].
Additionally, the Groupe Speciale Mobile Association
(GSMA) demands, by 2020, a reduction of CO2emissions
per connection of more than 40%. These fundamental facts
have led to introducing the notion of bit-per-Joule energy
efficiency, which is defined as the amount of information that
can be reliably transmitted per Joule of consumed energy, and
which is a key performance indicator for 5G networks [6],
[7] (see also [10]–[12] as some of the first papers introducing
the notion of bit-per-Joule energy efficiency). As illustrated in
Fig. 1, most of the approach useful for increasing the energy
efficiency of wireless networks can be grouped under four
broad categories as follows.
a) Resource allocation. The first technique to increase the
energy efficiency of a wireless communication system is to
allocate the system radio resources in order to maximize the
energy efficiency rather than the throughput. This approach
has been shown to provide substantial energy efficiency gains
at the price of a moderate throughput reduction [13].
b) Network planning and deployment. The second tech-
nique is to deploy infrastructure nodes in order to maximize
the covered area per consumed energy, rather than just the
covered area. In addition, the use of base station (BS) switch-
on/switch-off algorithms and antenna muting techniques to
adapt to the traffic conditions, can further reduce energy
consumptions [14], [15].
c) Energy harvesting and transfer. The third technique is
to operate communication systems by harvesting energy from
the environment. This applies to both renewable and clean
energy sources like sun or wind energy, and to the radio signals
present over the air.
d) Hardware solutions The fourth technique is to design
the hardware for wireless communications systems explicitly
accounting for its energy consumption [16], and to adopt major
architectural changes, such as the cloud-based implementation
of the radio access network [17].
In the following, a survey of the state-of-the-art relative to
the above cited four categories is given, with a special focus
on the papers published in this issue.
II. RESOURCE ALLOCATION
As energy efficiency has emerged as a key performance
indicator for future 5G networks, a paradigm shift from
throughput-optimized to energy-efficiency-optimized commu-
nications has begun. A communication system’s radio re-
sources should no longer be solely optimized to maximize
the amount of information that is reliably transmitted, but
rather the amount of information that is reliably transmitted per
Joule of consumed energy. Compared to traditional resource
allocation schemes, this requires the use of novel mathematical
tools specifically tailored to energy efficiency maximization.
A survey of this topic is provided in [13].
From a physical standpoint, the efficiency with which a
system uses a given resource, is the ratio between the benefit
obtained by using the resource, and the corresponding incurred
cost. Applying this general definition to communication over
a wireless link, the cost is represented by the amount of
consumed energy, which includes the radiated energy, the
energy loss due to the use of non-ideal power amplifiers, as
well as the static energy dissipated in all other hardware blocks
of the system (e.g. signal up and downconversion, frequency
synthesizer, filtering operations, digital-to-analog and analog-
to-digital conversion, and cooling operations). In the literature
it is usually assumed that the transmit amplifiers operate
in the linear region, and that the static hardware energy is
independent of the radiated energy [4], [13], [18]–[20]. These
two assumptions lead to expressing the consumed energy
during a time interval Tas
E=T(µp +Pc)[Joule] ,(1)
wherein pis the radiated power, µ= 1/η, with ηthe efficiency
of the transmit power amplifier, and Pcincludes the static
power dissipated in all other circuit blocks of the transmitter
and receiver.
On the other hand, the benefit produced after sustaining the
energy cost in (1) is related to the amount of data reliably
transmitted in the time interval T, and several performance
functions have been employed in the literature to measure this
quantity, depending on the particular system under analysis.
Some notable examples are:
-System capacity / Achievable rate. The Shannon ca-
pacity (or the achievable rate in scenarios where the
capacity is not known), often expressed through the well-
known formula involving the log of one plus the Signal-
to-Interference plus Noise-Ratio (SINR), represents the
ultimate rate at which reliable communication is possible,
and therefore captures the needs of having both fast and
reliable communication. This measure has been consid-
ered recently in [18] and [21], which focused mainly
on multi-carrier systems. Subsequent to these works,
the achievable rate has become the dominant choice to
measure the quality of a communication system. Further

3
studies that considered this approach are [22], and [23]
for OFDMA systems, [24]–[26] for MIMO systems,
[27]–[29] for relay-assisted communications, [30] and
[31] for cognitive communications.
-Throughput. The system throughput is a measure that,
differently from capacity, takes into account the actual
rate at which data is transmitted. Its consideration how-
ever requires specifying the system bit error rate, and
thus the particular modulation in use. The throughput
was the first metric to be considered in the context of
energy efficiency, and its use dates back to the seminal
works [11], [12], and [32], in the context of CDMA
networks. These studies spurred the interest for energy-
efficient resource allocation in wireless networks, and
a throughput-based definition of energy efficiency was
used in [33]–[36] proposing game-theoretic approaches
for energy-efficient multiple access networks, in [37]
with reference to cognitive radio systems, in [38] in
conjunction with widely linear receivers, in [39] for ultra-
wideband systems, and in [40] for relay-based systems.
-Outage capacity. The above metrics refer to scenarios in
which perfect channel state information (CSI) is available
at the resource allocator. Also, they can be replaced by
their ergodic counterparts in fast-fading scenarios, or in
general whenever only statistical CSI is available at the
resource allocator [41]. Instead, in slow-fading scenarios,
outage events become the major impairments of the
communication and the outage capacity becomes the most
suitable metric to measure the benefit obtained from the
system. This approach is somewhat less popular than
the ones discussed above. Nevertheless, it has attracted
significant interest and recent contributions in this area
can be found in [42] and [43].
A common feature of all the above metrics is that they are
measured in [bits/s] and depend on the signal-to-noise ratio
(SNR) (or SINR) of the communication, denoted by γ. Thus,
we can generally express the system benefit by a function
f(γ), with fto be specified according to the particular system
to optimize.
Finally, we can define the energy efficiency of a communi-
cation link as
EE =T f (γ)
T(µp +Pc)=f(γ)
µp +Pc
[bits/Joule] .(2)
It can be seen that (2) is measured in [bits/Joule], thus naturally
representing the efficiency with which each Joule of energy is
used to transmit information. Fig. 2 shows the typical shape of
the energy efficiency versus the transmit power, for different
values of the static power consumption Pc.
Two important observations can be made from Fig. 2.
1) First, the energy efficiency is not monotone in the trans-
mit power and is maximized by a finite power level.
This is a fundamental difference compared to traditional
performance metrics, which instead are monotonically
increasing in the transmit power. While the maximization
of the numerator of the energy efficiency leads to trans-
mitting with a power level equal to the maximum feasible
p[dBm]
-20 -10 0 10 20 30 40
EE [Mb/J]
0
0.5
1
1.5
2
2.5
Pc= 10 dBm
Pc= 20 dBm
Pc= 30 dBm
Figure 2. Typical shape of the energy efficiency function.
transmit power, maximizing the energy efficiency yields
a power level that is in general lower.
2) Increasing the static power term causes the maximizer of
the energy efficiency to increase. In the limit Pc>> p,
the denominator becomes approximately a constant, and
energy efficiency maximization reduces to the maximiza-
tion of the numerator.
The energy efficiency in (2) refers to a single communica-
tion link. In a communication network the expression becomes
more involved, depending on the benefits and costs incurred
by each individual link of the network and several network-
wide performance functions have been proposed. Two main
approaches can be identified.
-Network benefit-cost ratio. The network benefit-cost
ratio is given by the ratio between the sum of all indi-
vidual benefits of the different links, and the total power
consumed in the network. This metric is called Global
Energy Efficiency (GEE) and is the network-wide energy
efficiency measure with the strongest physical meaning.
A considerable number of contributions have provided
schemes for GEE maximization. Among recent examples
we mention [22], [27], [44] and [23] for OFDMA net-
works, and [26], [45], [46] and [47] for MIMO systems.
Among these references, [27], [46] and [47] also consider
the presence of relays.
-Multi-objective approach. One drawback of the GEE
is that it does not allow tuning of the individual energy
efficiencies of the different network nodes. To address
this issue, an alternative approach is to regard each
individual node’s energy efficiency as a different objective
to maximize, thus performing a multi-objective resource
allocation, maximizing a combination of the different en-
ergy efficiencies {EEk}K
k=1 according to some increasing
function φ(EE1,...,EEK). Several combining functions
φhave been proposed:
1) Weighted Sum Energy Efficiency (WSEE). The function
φis the weighted sum of the individual energy efficien-

4
cies. Contributions using this approach are [23], [48]
and [49].
2) Weighted Product Energy Efficiency (WPEE). The
function φis the weighted product of the individual
energy efficiencies. This approach is related to the
proportional fairness criterion, and studies that have
considered this metric are [23] and [50].
3) Weighted Minimum Energy Efficiency (WMEE). The
function φis the weighted minimum of the individual
energy efficiencies. This approach corresponds to a
worst-case design, and has been considered in [47] and
[51].
All three combining functions are able to describe (at
least) parts of the energy-efficient Pareto boundary of the
system, by varying the choice of the weights. However,
only the WMEE is able to describe the complete Pareto
boundary, by sweeping the weights [13].
Energy efficiency maximization can be also carried out subject
to all practical constraints that are typically enforced in com-
munication systems. Besides the widely considered maximum
power constraint, more recently quality of service (QoS) con-
straints have started being enforced. This includes minimum
rate guarantees [47], [52], minimum delay constraints [53],
[54], maximal delay bound constraints [55], and interference
temperature constraints, typically enforced in underlay systems
[30], [56].
It should also be mentioned that an alternative, yet less
powerful approach for energy saving is, rather than maximiz-
ing the energy efficiency, to minimize the energy consumption
(see [57] and [58] for recent contributions in this direction).
Although the two approaches might seem equivalent, they
in general lead to different resource allocations. Of course,
in order to rule out the trivial zero-power solution, the con-
sumed energy minimization problem must be coupled with
some minimum QoS constraints to be guaranteed in terms
of capacity, outage capacity, or throughput. This approach
results in consuming the minimum amount of energy required
to maintain given minimum system performance; however, it
does not account for the system benefit-cost ratio, and there-
fore might be overly pessimistic, being able only to provide
minimum acceptable performance. Also, power minimization
is a particular case of energy efficiency maximization subject
to QoS constraints, which corresponds to minimizing the
denominator of the energy efficiency, with a constraint on the
numerator.
In this special issue, energy-efficient resource allocation
is studied in [59]–[64]. In [59], weighted sum energy effi-
ciency maximization is considered. A distributed algorithm
is provided, by means of pricing and fractional optimization
techniques. In [60] energy-efficient licensed-assisted access for
LTE systems has been proposed to use unlicensed bands. The
energy efficiency of the system is studied and the energy-
efficient Pareto region is characterized. A Lyapunov optimiza-
tion technique is employed in [61] to optimize the energy
efficiency of WiFi networks with respect to network selection,
sub-channel assignment, and transmit power. Energy efficiency
optimization in MIMO-OFDM networks is performed in [62],
considering the practical scenario in which the propagation
channels vary dynamically over time (for example due to user
mobility, fluctuations in the wireless medium, and changes in
the users’ loads). Learning tools are combined with fractional
programming to develop an online optimization algorithm. The
energy efficiency maximization of a massive MIMO system
operating in the mmWave range is studied in [63], while the
paper [64] focuses on a cognitive scenario wherein a secondary
network co-exists, using the same frequency band, with a
primary cellular network.
III. NET WORK PLANNING AND DEPLOYMENT
In order to cope with the sheer number of connected devices,
several potentially disruptive technologies have been proposed
for the planning, deployment, and operation of 5G networks.
A. Dense networks
The idea of dense networks is to deal with the explosively
increasing number of devices to serve by increasing the
amount of deployed infrastructure equipment. Two main kinds
of network densification are gaining momentum and appear as
very strong candidates for the implementation of 5G networks.
1) Dense Heterogeneous Networks: Unlike present net-
work deployments which uniformly split a macro-cell into
a relatively low number of smaller areas each covered by a
light base-station, dense heterogeneous networks drastically
increase the number of infrastructure nodes per unit of area
[7], [65]. A very large number of heterogeneous infrastructure
nodes ranging from macro BSs to femto-cells and relays are
opportunistically deployed and activated in a demand-based
fashion, thus leading to an irregularly-shaped network layout
such as that shown in Fig. 3.
-10123456
-2
-1
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
Figure 3. Evolution of cellular network layout. Traditional layout on the left;
5G layout on the right.
One critical challenge when dealing with dense heteroge-
neous networks is the modeling of the positions of the nodes
in the network, which is typically difficult to predict determin-
istically. Instead, the nodes’ locations are modeled as random
variables with a given spatial distribution, and in this context
the most widely used tool is the theory of stochastic geometry
[66], [67]. Employing this tool, most research effort on dense
networks has been focused on the analysis of traditional, non-
energy-efficient performance measures [68], [69].
Fewer results are available as far as energy efficiency
is concerned. From an energy-efficient point-of-view, node
densification reduces the (electrical and/or physical) distances

5
between communicating terminals, thus leading to higher data-
rates at lower transmit powers. However, it also creates addi-
tional interference which might degrade the network energy
efficiency. This trade-off has been analyzed in [70], where it is
shown that densification has a beneficial impact on energy effi-
ciency, but the gain saturates as the density of the infrastructure
nodes increases, thus indicating that an optimal density level
exists. The optimal network densification level is investigated
also in [71], where a threshold value on the operating cost of
a small BS is determined. If below the threshold, micro BSs
are beneficial, otherwise they should be switched off. In [72],
fractional programming is employed to develop a spectrum
allocation algorithm in OFDMA heterogeneous networks, so
as to minimize the energy expenditure per transmitted bit. The
paper [73], in this special issue, shows, using a game-theoretic
approach, that infrastructure sharing between different mobile
network operators (MNOs) may bring substantial energy sav-
ings by increasing the percentage of BSs in sleep mode. In
particular, the paper considers two different MNOs coexisting
in the same area, which are aggregated as a single group to
make the day-ahead and real-time energy purchase, while their
BSs share the wireless traffic to maximally turn lightly-loaded
BSs into sleep mode.
2) Massive MIMO: If the idea of dense networks is to
densify the number of infrastructure nodes, the idea of massive
MIMO is to densify the number of deployed antennas. In
massive MIMO, conventional arrays with only a few antennas
fed by bulky and expensive hardware are replaced by hundreds
of small antennas fed by low-cost amplifiers and circuitry. The
research interest in such a technology has been spurred by
[74], which observed how, owing to the law of large numbers,
large antenna arrays can average out multi-user interference.
This happens provided the so-called favorable propagation
condition holds, which has been experimentally validated in
the overview works [75] and [76].
However, massive MIMO systems also come with several
challenges and impairments. First of all, deploying a very large
number of antennas points in the direction of very large sys-
tems, for which a microscopic analysis is usually too complex.
Instead, system analysis and design must be performed based
on the limiting behavior of the network, a task which is usually
accomplished by means of random matrix theory [77], [78].
In addition, massive MIMO systems are characterized by a
more difficult channel estimation task, due to a more severe
pilot contamination effect, and to more significant hardware
impairments. Contributions to address these challenges have
mainly focused on traditional performance measures (see for
example [79] and [80]), and results on the energy efficiency
of massive MIMO systems have started appearing only very
recently.
As far as energy efficiency is concerned, massive MIMO
has been shown to reduce the radiated power by a factor
proportional to the square root of the number of deployed
antennas, while keeping the information rate unaltered [81].
However, this result applies to an ideal, single-cell massive
MIMO system only, and without taking into account the
hardware-consumed power. In [82], the aggregate effect of
hardware impairments in massive MIMO systems is accounted
for and the energy efficiency is analyzed. In [47] and [83] the
hardware power is included in the analysis, and it is shown
that the network energy efficiency is maximized for a finite
number of deployed antennas.
Contributions [84]–[88] in this special issue address the
topic of energy efficiency in dense networks. Contribution
[84] presents a framework for self-organizing cells that au-
tonomously activate or deactivate in response to traffic de-
mands. Network deployment in response to traffic conditions
is also analyzed in [85]. There, it is shown that in the
presence of heavy traffic conditions, deploying indoor small
cells is more energy-efficient than traditional network layouts.
In [86] an optimization framework for energy-efficient radio
resource management in heterogeneous networks is developed,
assuming stochastic traffic arrivals. Reference [87] merges the
heterogeneous approach and the massive MIMO technology,
studying the problem of determining the optimal BS density,
transmit power levels, and number of deployed antennas for
maximal energy efficiency. Alternatively, [88] considers a two-
way relay channel, in which multiple pairs of full-duplex
users exchange information through a full-duplex amplify-
and-forward relay with massive antennas. Different transceiver
strategies are analyzed, which are shown to achieve energy
efficiency gains.
B. Offloading techniques
Offloading techniques are another key 5G strategy instru-
mental to boost the capacity and energy efficiency of fu-
ture networks. Currently available user devices are already
equipped with multiple radio access technologies (RATs) –
e.g., cellular, Bluetooth, WiFi –, so that, whenever alternative
connection technologies are available (e.g., as often happens
in indoor scenarios), cellular traffic can be offloaded and
additional cellular resources can be provided to those users that
cannot offload their traffic. Future networks will vastly rely on
offloading techniques, and these will not only be based on Wi-
Fi. In particular, the following offloading methods/strategies
can be envisioned:
-Device-to-device (D2D) communications. While in a
conventional network user devices are not allowed to
directly communicate, D2D communications [89], [90]
refer instead to the scenario in which several co-located
(or in close proximity) devices can communicate directly
using a cellular frequency and being instructed to do
so by the BS. D2D techniques have a profound impact
on the system energy efficiency since direct transmis-
sion between nearby devices may happen at a much
lower transmit power than that needed for communication
through a BS that can be far away. Additionally, they are
a powerful offloading strategy since they permit releasing
resources at the BS that, through proper interference
management, can be used for supporting other users. The
impact of D2D communications on the energy efficiency
of future wireless networks has been studied in [91]–[94].
-Visible light communications (VLC). VLC, also known
as LiFi or optical wireless communication (OWC), is
a technology that can serve indoor communications in

6
future wireless systems. While being basically a short
range technology, it has some remarkable advantages,
such as very high energy-efficiency, availability of large
bandwidths and thus the capability to support large data-
rates [95]. The use of the visible light spectrum for data
communication is enabled by inexpensive and off-the-
shelf available light emitting diodes (LEDs). Individual
LEDs can be modulated at very high speeds, and indeed
3.5Gbit/s@2m distance has been demonstrated as well as
1.1Gbit/s@10m, both with a total optical output power of
5 mW [96], [97].
-Local caching. Wireless networks are subject to time-
varying traffic loads, and light load periods can be lever-
aged by using the redundant capacity to download and
store in the BS’s cache popular content that is likely
to be requested by several users. This strategy, named
“local caching”, basically trades off storage capacity
(a quite inexpensive resource) at the BS with network
throughput. By reducing the load on the backhaul link,
content caching strategies can potentially increase the
energy efficiency of the core network by avoiding multi-
ple transmissions of the same content for different users.
Local caching is a relatively new offloading technique,
which however has been attracting a growing interest
[98]–[101].
-mmWave cellular. The use of frequency bands above
10GHz, a.k.a. mmWaves [102], while increasing the
available network bandwidth, is considered in this section
as a strategy to offload traffic from the sub-6GHz cellular
frequencies for short-range (up to 100-200 m) communi-
cations in densely crowded areas. Future wireless technol-
ogy will need to harness the massively unused mmWave
spectrum to meet the projected acceleration in mobile
traffic demand. Today, the available range of mmWave-
based solutions is already represented by IEEE 802.11ad
(WiGig), IEEE 802.15.3c, WirelessHD, and ECMA-387
standards, with more to come in the following years. In
Section V we will comment on the hardware challenges
that the use of mmWave poses. Studies on the impact of
mmWaves on the energy efficiency of future 5G can be
found in [103]–[105].
In this special issue, offloading approaches for energy
efficiency are investigated in the papers [106]–[109] and [110].
The paper [106] targets energy-aware D2D communications
underlaying a cellular system, and investigates several funda-
mental problems, including the potential energy savings of
D2D communications, the underlying reasons for the savings
and the tradeoff between energy consumption and other net-
work parameters such as available bandwidth, buffer size and
service delay in large scale D2D communication networks. By
formulating a mixed integer linear programming problem that
minimizes the energy consumption for data transmission from
the cellular BSs to the end-user devices through any possible
ways of transmissions, a theoretical performance lower bound
of system energy consumption is obtained, showing the energy
savings granted by D2D communications.
Papers [107] and [108] focus on VLC. The former
manuscript investigates the energy efficiency benefits of in-
tegrating VLC with RF-based networks in a heterogeneous
wireless environment, by formulating the problem of power
and bandwidth allocation for energy efficiency maximization
of a heterogeneous network composed of a VLC system and an
RF communication system. The latter study, instead, designs
an energy efficient indoor VLC system from a radically new
perspective based on an amorphous user-to-network associa-
tion structure, and shows that the proposed amorphous cells
are capable of achieving a much higher energy efficiency per
user compared to that of the conventional cell formation, for
a range of practical field-of-view angles.
The paper [109] focuses on the energy efficiency aspects of
local caching, and derives an approximate expression for the
energy efficiency of a cache-enabled wireless network. The
study shows that caching at the BSs can improve the network
energy efficiency when power efficient cache hardware is used;
additionally, the caching energy efficiency gain is large when
the backhaul capacity is stringent, the interference level is low,
content popularity is skewed, and when caching takes place at
pico BSs instead of macro BSs.
Finally, the paper [110] investigates the use of mmWaves
for wearable personal networks in crowded environments.
An energy efficiency assessment of mmWave-based “high-
end” wearables that employ advanced antenna beamforming
techniques is proposed; first analytical results on the underly-
ing scaling laws for the interacting mmWave-based networks
based on IEEE 802.11ad are given, and then the impact of
beamforming quality on the system energy efficiency under
various conditions is quantified.
IV. ENERGY HARVESTING AND TRANSFER
Harvesting energy from the environment and converting it
to electrical power is emerging as an appealing possibility
to operate wireless communication systems. Indeed, although
this approach does not directly reduce the amount of energy
required to operate the system, it enables wireless networks
to be powered by renewable and clean energy sources [111].
Two main kinds of energy harvesting have emerged so far in
the context of wireless communications.
-Environmental energy harvesting. This technique refers
to harvesting clean energy from natural sources, such as
sun and wind. Comprehensive surveys on this approach
are [112] and [113].
-Radio-frequency energy harvesting. This technique
refers to harvesting energy from the radio signals over
the air, thus enabling the recycling of energy that would
otherwise be wasted. In this context, interference signals
provide a natural source of electromagnetic-based power.
Surveys on this approach are [114] and [115].
The main challenge in the design of communication systems
powered by energy harvesting is the random amount of energy
available at any given time. This is due to the fact that the
availability of environmental energy sources (e.g. sun or wind)
is inherently a stochastic process, and poses the problem
of energy outages. Unlike traditionally-powered networks,
communication systems powered by energy harvesting must
comply with the so-called energy causality constraint, i.e. the

7
energy used at time tcannot exceed the energy harvested up
to time t.
Early works on environmental energy harvesting dealt with
this problem by taking a so-called off-line approach, assuming
that the amount of energy harvested at a given point in time is
known in advance. Although difficult to meet in practice, this
approach provides insight as to the ultimate performance of
energy-harvesting systems. In [116] an offline power allocation
algorithm termed directional waterfilling is proposed, while
[117] addresses a similar problem but assuming a system
in which the data to be transmitted is available at random
times. In [118] and [119], the results of [117] are extended
to the more realistic case of a battery with finite capacity,
while the impact of energy leakages due to non-ideal batteries
is considered in [120]. Previous results have been extended
to multi-user networks in [121] and [122], to relay-assisted
communications in [123], and to multiple-antenna systems
[124].
More recently, research efforts have been aimed at overcom-
ing the off-line approach, developing on-line design policies,
which do not assume any knowledge about the amount of
energy harvested at specific times. Two main approaches have
emerged in this context. Tools from stochastic optimization are
used to develop design protocols assuming that the statistics
of the energy process are known [125]–[127]. Alternatively,
approaches based on learning theory provide the means to
design energy harvesting systems by having the users adapt to
the environment based on past observations [128], [129].
The issue of energy randomness is also present as far as
radio-frequency energy harvesting is concerned, because in
general the amount of electromagnetic power available in the
air is not known in advance. Indeed, several schemes have
appeared in the literature in which a node opportunistically
exploits the electromagnetic radiation over the air. In [130]
an OFDMA system is considered, in which a hybrid BS is
considered, which is partly powered by radio frequency energy
harvesting. In [131] and [132] a relay-assisted network is con-
sidered, wherein the relay is powered by drawing power from
the received signals. A cognitive radio system is considered
in [133], in which the secondary network draws energy from
the signals received from the primary network.
However, radio-frequency energy harvesting offers an in-
triguing possibility, which also helps to reduce the randomness
of wireless power sources. The idea is to combine energy
harvesting with wireless power transfer techniques, thereby
enabling network nodes to share energy with one another
[134]. This has a two-fold advantage. First, it makes it possible
to redistribute the network total energy, prolonging the lifetime
of nodes that are low on battery energy [135], [136]. Second, it
is possible to deploy dedicated beacons in the network, which
act as wireless energy sources, thereby eliminating or reducing
the randomness of the radio-frequency energy source. This
approach can be taken even further, superimposing the energy
signals on regular communication signals, resulting in the so-
called simultaneous wireless information and power transfer
(SWIPT) [137]–[139].
Several contributions to wireless power transfer are included
in this special issue [140]–[142]. In [140], SWIPT in non-
orthogonal multiple access networks is considered. The net-
work nodes are assumed to be spatially randomly located over
the covered area and a novel protocol is provided in which
users close to the source act as energy harvesting relays to help
faraway users. In [141], the co-existence of a MISO femtocell
system with a macro-cell system is considered. The femtocell
simultaneously transmits information to some of its users and
energy to the rest of its users, while also suppressing its in-
terference to macro-cell devices. The system energy efficiency
is maximized with respect to the system beamforming vectors
by means of fractional programming theory. In [142], energy
harvesting and wireless power transfer is studied in relay-
assisted systems with distributed beamforming, proposing a
novel power splitting strategy.
V. HARDWARE SOLUTIONS
Energy-efficient hardware solutions refers to a broad cate-
gory of strategies comprising the green design of the RF chain,
the use of simplified transmitter/receiver structures, and, also,
a novel architectural design of the network based on a cloud
implementation of the radio access network (RAN) and on the
use of network function virtualization.
Attention has been given to the energy-efficient design
of power amplifiers [143], [144], both through direct cir-
cuit design and through signal design techniques aimed at
peak-to-average-power ratio reduction. The use of simplified
transmitter and receiver architectures, including the adoption
of coarse signal quantization (e.g. one bit quantization) and
hybrid analog/digital beamformers, is another technique that
is being proposed for increasing hardware energy efficiency,
especially in systems with many antennas such as massive
MIMO systems and mmWave systems. The paper [145], as
an instance, presents an analysis of the spectral efficiency
of single-carrier and OFDM transmission in massive MIMO
systems that use one-bit analog-to-digital converters (ADCs),
while a capacity analysis of one-bit quantized MIMO systems
with transmitter CSI is reported in [146]. One-bit ADCs
coupled with high-resolution ADCs are instead proposed and
analyzed in the paper [147], from this special issue, to simplify
receiver design in massive MIMO systems. The paper shows
that the proposed mixed-ADC architecture with a relatively
small number of high-resolution ADCs is able to achieve
a large fraction of the channel capacity of the conventional
architecture, while reducing the energy consumption consid-
erably even compared with antenna selection strategies, for
both single-user and multi-user scenarios.
For mmWave communications, given the required large
number of antenna elements, the implementation of digital
beamforming poses serious complexity, energy consumption,
and cost issues. Hybrid analog and digital beamforming struc-
tures have been thus proposed as a viable approach to reduce
complexity and, most relevant to us, energy consumption
[104], [148], [149]. The paper [150], in this special issue,
focuses on a mmWave MIMO link with hybrid decoding.
Unlike previous contributions on the subject, which consid-
ered a fully-connected architecture requiring a large number
of phase shifters, a more energy-efficient hybrid precoding

8
with sub-connected architecture is proposed and analyzed in
conjunction with a successive interference cancellation (SIC)
strategy. The paper also shows through simulation results that
the proposed SIC-based hybrid precoding is near-optimal and
enjoys higher energy efficiency than spatially sparse precoding
[151] and fully digital precoding.
Cloud-based implementation of the RAN is another key
technology instrumental to making future 5G networks more
energy-efficient. Spurred by the impressive spread of cloud
computing, cloud-RAN (C-RAN) is based on the idea that
many functions that are currently performed in the BS, can be
actually transferred to a remote data-center and implemented
via software [17], [152], [153]. The most extreme implementa-
tion of C-RAN foresees light BSs wherein only the RF chain
and the baseband-to-RF conversion stages are present; it is
assumed that these light BSs are connected through high-
capacity links to the data-center, wherein all the baseband
processing and the resource allocation algorithms are run. This
enables a great deal of flexibility in the network, thus leading
to substantial savings as far as both deployment costs and
energy consumption are concerned. Mobile-edge computing
[154] is also a recently considered approach that increases
network flexibility potentially leading to considerable energy
savings. The studies [155]–[158] are a sample of the many
recent works that have addressed the energy-efficiency gains
possible with a cloud-based RAN.
In this special issue, paper [159] investigates the role that
cellular traffic dynamics play in efficient network energy
management, and designs a framework for traffic-aware en-
ergy optimization. In particular, using a learning approach,
it is shown that the C-RAN can be made aware of the
near-future traffic, so that inactive or low-load BSs can be
switched off, thus reducing the overall energy consumption.
The proposed approach is also validated on real traffic traces
and energy savings on the order of 25% are achieved. The
paper [160], from this special issue, proposes a holistic sparse
optimization framework to design a green C-RAN by taking
into consideration the power consumption of the fronthaul
links, multicast services, as well as user admission control.
Specifically, the sparsity structures in the solutions of both
the network power minimization and user admission control
problems are identified, which call for adaptive remote radio
head (RRH) selection and user admission, a problem that is
solved through a nonconvex but smoothed `p-minimization
(0< p ≤1) approach to promote sparsity in the multicast
setting. Finally, [161], again from this special issue, studies
the energy efficiency of a downlink C-RAN, focusing on
two different downlink transmission strategies, namely the
data-sharing strategy and the compression strategy. The paper
shows that C-RAN signicantly improves the range of feasible
user data rates in a wireless cellular network, and that both
data-sharing and compression strategies bring much improved
energy efficiency to downlink C-RAN as compared to non-
optimized Coordinated Multipoint (CoMP).
VI. FUTURE RESEARCH CHALLENGES
After having reviewed the state-of-the-art of the main 5G
energy-efficient techniques, a natural question is: what are the
next steps to be taken towards an energy-efficient 5G? We
review some of them in the following.
A. The need for a holistic approach
In our opinion, the main issue concerning the current state-
of-the art is that most research has been directed towards
a separate analysis and use of the different energy-efficient
technologies. Resource allocation, deployment and planning
methods, energy harvesting and transfer, have been mostly
studied separately, but will a single approach be able to
achieve the desired thousand-fold energy efficiency increase
with respect to present networks? Most likely the answer
is negative. A holistic approach is thus necessary, in which
all energy-efficient techniques are combined. Indeed, as pre-
viously discussed, some works in this special issue go in
this direction combining multiple energy-efficient techniques
together. The GreenTouch project [8], [9] has taken an initial
end-to-end perspective for the assessment of the network
energy efficiency and energy consumption. More research in
this direction is needed to understand the relative impact and
the combined benefits of new technologies, architectures and
algorithms being developed.
B. Dealing with interference
Section II has introduced fractional programming as the
most suitable tool to handle energy-efficient resource allo-
cation. However, direct application of fractional program-
ming typically requires a prohibitive complexity to operate
in interference-limited networks. Unfortunately, 5G networks
will be interference-limited, since orthogonal transmission
schemes and/or linear interference neutralization techniques
are not practical due to the massive amount of nodes to
be served. Thus, the potentialities of fractional programming
must be extended. A promising answer is represented by the
framework of sequential fractional programming, which pro-
vides a systematic approach to extend fractional programming
to interference-limited networks with affordable complexity.
Sequential fractional programming has been recently shown to
be effective in optimizing the energy efficiency of a number
of candidate technologies for 5G, such as C-RAN, CoMP, and
multi-cell systems, also with multi-carrier transmissions [23],
[47], multi-cell massive MIMO systems [47], heterogeneous
relay-assisted interference networks [46], full-duplex systems
[45], and device-to-device systems [94], [162].
C. Dealing with randomness
Previous sections have shown how randomness will be a
pervasive feature of future wireless communication systems,
which will affect network topologies, traffic evolution, and
energy availability. The energy-efficient design of networks
with such an unprecedented level of randomness requires the
development of new statistical models, which are able to
capture the average or limiting behavior of randomly evolving
networks. Random matrix theory and stochastic geometry
appear as suitable tools towards this end, but most studies em-
ploying these techniques have been concerned with traditional

9
performance metrics, whereas a thorough investigation of their
impact on the energy efficiency of communication systems is
still missing.
A second approach lies in the use of learning techniques,
which deal with randomness by letting the devices learn
from past observations of their surroundings and respond as
appropriate in a self-organizing fashion. However, also in this
case, very little research effort has been directed towards
understanding the impact of this technique on energy-efficient
network design.
D. Emerging techniques and new energy models
In addition, new emerging technologies can also be used
for energy-efficient purposes. In particular, caching and mo-
bile computing have shown significant potential as far as
reducing energy consumption is concerned. By an intelligent
distribution of frequently accessed content over the network
nodes, caching alleviates the need for backhaul transmissions,
which results in relevant energy consumption reductions. In-
stead, mobile computing does not directly reduce the energy
consumption, but, similarly to wireless power transfer, it can
prolong the lifetime of nodes that are low on battery energy.
Nevertheless, in order to conclusively quantify the impact of
these techniques on energy efficiency it is necessary to develop
new energy consumption models which take into account the
energy consumption associated with overhead transmissions
over the backhaul, to feedback signaling, and to the execution
of computing operations in digital signal processors.
VII. CONCLUSIONS
Wireless communications are undergoing a rapid evolution,
wherein the quest for new services and applications pushes for
the fast introduction of new technologies into the marketplace.
Operators are just now starting to make initial profits from
their deployed LTE networks, and already 5G demos and
prototypes are being announced. Moreover, the wireless com-
munications industry has begun to design for energy efficiency.
As shown in this survey, energy efficiency has gained in the
last decade its own role as a performance measure and design
constraint for communication networks, but many technical,
regulatory, policy, and business challenges still remain to be
addressed before the ambitious 1000-times energy efficiency
improvement goal can be reached. We hope that this paper
and those in this special issue will help to move us forward
along this road.
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Stefano Buzzi (SM’07) is currently an Associate
Professor at the University of Cassino and Lazio
Meridionale, Italy. He received his Ph.D. degree in
Electronic Engineering and Computer Science from
the University of Naples “Federico II” in 1999, and
he has had short-term visiting appointments at the
Dept. of Electrical Engineering, Princeton Univer-
sity, in 1999, 2000, 2001 and 2006. His research
and study interest lie in the wide area of statis-
tical signal processing and resource allocation for
communications, with emphasis on wireless cellular
communications.
Dr. Buzzi is author/co-author of about 60 journal papers and 90 conference
papers; he is currently Associate Editor for the IEEE Transactions on Wireless
Communications, and a former Associate Editor for the IEEE Communications
Letters, and the IEEE Signal Processing Letters. Prof. Buzzi has recently been
the lead guest editor for the special issue on “5G Wireless Communication
Systems,” IEEE Journal on Selected Areas in Communications, June 2014.
Chih-Lin I received her Ph.D. degree in electrical
engineering from Stanford University. She has been
working at multiple world-class companies and re-
search institutes leading the R&D, including AT&T
Bell Labs; Director of AT&T HQ, Director of ITRI
Taiwan, and VPGD of ASTRI Hong Kong. She
received the IEEE Trans. COM Stephen Rice Best
Paper Award, is a winner of the CCCP National 1000
Talent Program, and has won the 2015 Industrial
Innovation Award of IEEE Communication Society
for Leadership and Innovation in Next-Generation
Cellular Wireless Networks. In 2011, she joined China Mobile as its Chief
Scientist of wireless technologies, established the Green Communications
Research Center, and launched the 5G Key Technologies R&D. She is
spearheading major initiatives including 5G, C-RAN, high energy efficiency
system architectures, technologies and devices; and green energy. She was
an Area Editor of IEEE/ACM Trans. NET, an elected Board Member of
IEEE ComSoc, Chair of the ComSoc Meetings and Conferences Board, and
Founding Chair of the IEEE WCNC Steering Committee. She was a Professor
at NCTU, an Adjunct Professor at NTU, and currently an Adjunct Professor at
BUPT. She is the Chair of FuTURE 5G SIG, an Executive Board Member of
GreenTouch, a Network Operator Council Founding Member of ETSI NFV, a
Steering Board Member of WWRF, a member of IEEE ComSoc SDB, SPC,
and CSCN-SC, and a Scientific Advisory Board Member of Singapore NRF.
Her current research interests center around Green, Soft, and Open.
Thierry E. Klein is currently the Head of the Inno-
vation Management Program for Vertical Industries
within the Nokia Innovation Steering organization at
Nokia. Prior to his current role, Thierry was the Pro-
gram Leader for the Network Energy Research Pro-
gram at Bell Labs, Alcatel-Lucent with the mission
to conduct research towards the design, development
and use of sustainable future communications and
data networks. He also served as the Chairman of
the Technical Committee of GreenTouch, a global
consortium dedicated to improve energy efficiency
in networks by a factor 1000x compared to 2010 levels. Since 2014, he is
also a member of the Momentum for Change Advisory Panel of the UN
Framework Convention for Climate Change (UNFCCC). He currently also
serves as the Co-Chair of the IEEE Green ICT Initiative.
He joined Bell Labs Research in Murray Hill, New Jersey as a Member of
Technical Staff in 2001 conducting fundamental and applied research on next-
generation wireless and wireline networks, network architectures, algorithms
and protocols, network management, optimization and control. From 2006
to 2010 he served as the Founder and CTO of an internal start-up focused
on wireless communications for emergency response and disaster recovery
situations within Alcatel-Lucent Ventures.
He earned an MS in Mechanical Engineering and an MS in Electrical
Engineering from the Universit de Nantes and the Ecole Centrale de Nantes
in Nantes, France. Thierry received a PhD in Electrical Engineering and
Computer Science from the Massachusetts Institute of Technology, USA. He
is an author on over 35 peer-reviewed conference and journal publications
and an inventor on 36 patent applications. He is the recipient of a Bell Labss
President Award and two Bell Labs Teamwork Awards. In 2010, he was voted
Technologist of the Year at the Total Telecom World Vendor Awards.
H. Vincent Poor (S72, M77, SM82, F87) received
the Ph.D. degree in EECS from Princeton University
in 1977. From 1977 until 1990, he was on the faculty
of the University of Illinois at Urbana-Champaign.
Since 1990 he has been on the faculty at Princeton,
where he is the Michael Henry Strater University
Professor of Electrical Engineering and Dean of the
School of Engineering and Applied Science. His
research interests are in the areas of information
theory, statistical signal processing and stochastic
analysis, and their applications in wireless networks
and related fields. Among his publications in these areas are the recent
books Principles of Cognitive Radio (Cambridge University Press, 2013)
and Mechanisms and Games for Dynamic Spectrum Allocation (Cambridge
University Press, 2014).
Dr. Poor is a member of the National Academy of Engineering, the National
Academy of Sciences, and is a foreign member of Academia Europaea and
the Royal Society. He is also a fellow of the American Academy of Arts
and Sciences and other national and international academies. He received
the Marconi and Armstrong Awards of the IEEE Communications Society in
2007 and 2009, respectively. Recent recognition of his work includes the 2014
URSI Booker Gold Medal, the 2015 EURASIP Athanasios Papoulis Award,
the 2016 John Fritz Medal, and honorary doctorates from Aalborg University,
Aalto University, the Hong Kong University of Science and Technology, and
the University of Edinburgh.

14
Chenyang Yang received her Ph.D. degree in Elec-
trical Engineering from Beihang University (for-
merly Beijing University of Aeronautics and Astro-
nautics), Beijing, China, in 1997. She has been a
full professor with the School of Electronics and
Information Engineering, Beihang University since
1999. Dr. Yang was nominated as an Outstanding
Young Professor of Beijing in 1995 and was sup-
ported by the 1st Teaching and Research Award
Program for Outstanding Young Teachers of Higher
Education Institutions by Ministry of Education of
China during 1999-2004. Dr. Yang was the chair of the IEEE Communications
Society Beijing chapter during 2008-2012. She has served as Technical
Program Committee Member for numerous IEEE conferences. She has been
an associate editor or guest editor of several IEEE journals. Her recent research
interests include green radio, local caching, and other emerging techniques for
next generation wireless networks.
Alessio Zappone (S’08 - M’11) is a research
associate at the Technische Universit¨
at Dresden,
Dresden, Germany. Alessio received his M.Sc. and
Ph.D. both from the University of Cassino and
Southern Lazio. Afterwards, he worked with Con-
sorzio Nazionale Interuniversitario per le Telecomu-
nicazioni (CNIT) in the framework of the FP7 EU-
funded project TREND, which focused on energy
efficiency in communication networks. Since 2012,
Alessio is the project leader of the project CEMRIN
on energy-efficient resource allocation in wireless
networks, funded by the German research foundation (DFG).
His research interests lie in the area of communication theory and signal
processing, with main focus on optimization techniques for resource allocation
and energy efficiency maximization. He held several research appointments
at TU Dresden, Politecnico di Torino, Supelec - Alcatel-Lucent Chair on
Flexible Radio, and University of Naples Federico II. He was the recipient
of a Newcom# mobility grant in 2014. Alessio currently serves as associate
editor for the IE EE SI GNA L PROCE SS ING LE TT ERS .