Information Processing and Wireless Energy Harvestingin Interference-Aware Public Safety Networks

Daniyal Munir1 • Syed Tariq Shah1 • Kae Won Choi1 •

Min Young Chung1

Published online: 11 June 2018� Springer Science+Business Media, LLC, part of Springer Nature 2018

Abstract In a public safety environment, user equipments (UEs) located within the cov-

erage area of evolved NodeB, relay network services to out-of-coverage UEs. However,

relay UEs in public safety environments are typically energy constrained and cannot

operate indefinitely without recharging. Radio frequency energy harvesting has been

proposed as a solution for recharging wireless UEs. In this paper, we propose a scheme for

extending the lifetime of a public safety network by wirelessly charging relay UEs. In

addition, we propose a relay selection method considering the battery status of relay UEs.

The proposed relay selection is defined as a bipartite graph matching problem and the

optimal relay is obtained through matching games technique. The proposed scheme not

only improves the network lifetime but also extend the network coverage. We also conduct

system level simulations to evaluate the performance of the proposed scheme. Simulation

results show that the overall performance of the system is improved in terms of achievable

throughput and network lifetime.

Keywords Energy harvesting � Public safety � Device-to-device communications �Network lifetime

& Min Young Chungmychung@skku.edu

Daniyal Munirdaniyal@skku.edu

Syed Tariq Shahsyed.tariq@skku.edu

Kae Won Choikaewonchoi@skku.edu

1 College of Information and Communication Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea

123

Wireless Pers Commun (2018) 103:2071–2091https://doi.org/10.1007/s11277-018-5896-x

1 Introduction

In public safety situations, reliable communication services should be supported where

network infrastructure is partially damaged [1]. Third Generation Partnership Project

(3GPP) has been making efforts for evolving a broadband public safety network based on

Long Term Evolution (LTE). Device-to-device (D2D) communications enable LTE net-

works to become one of the potential solutions to provide reliable network services in

public safety environments [2]. Figure 1 shows an example of such network, where in-

coverage user equipments (UEs) relay information between evolved NodeB (eNB) and out-

of-coverage isolated UEs (IUEs) [3].

Employing D2D communications for public safety services poses many challenges,

such as, device discovery, resource allocation, relay UE (RUE) selection, battery power of

these RUEs, and so on [2, 4]. Among them two important problems require primary

attention for improving performance of relay networks. One problem is the limited battery

life of RUEs. Since network life time of D2D-based public safety network highly depends

on the battery power of RUEs, an efficient battery recharge mechanism may significantly

improve the overall network performance. The other problem is the selection of RUEs to

provide reliable network services to IUEs. An efficient relay selection algorithm can

improve the performance of IUEs and extend network coverage [5].

Energy harvesting from radio frequency (RF) signals is an attractive solution to

recharge wireless UEs [6]. In this technique, the RF radiation is captured by the receiving

antennas and converted into direct current (DC) voltage through rectifier circuits [7]. As

RF signals carry information and energy at the same time, simultaneous information

processing and wireless energy harvesting has been studied recently [8–11].

Energy harvesting techniques can be used to prolong the operating time of UEs without

replacing their batteries [12]. Especially, it can be useful for recharging the batteries of

UEs in public safety environments, where infrastructure has been damaged. Huge amount

of researches have been done in this field; see [13] and the references therein. However, its

design for public safety environments has not been investigated. Wireless energy har-

vesting should be given appropriate consideration for extending the battery life of RUEs in

mission critical public safety situations.

On the other hand, D2D communications-based coverage extension has been exten-

sively studied for both legacy cellular networks and public safety environments [14–19].

Fig. 1 Communication support for out-of-coverage isolated user equipments

2072 D. Munir et al.

123

However, these works have not considered any interference for RUE selection process,

unlike real situations. Interference severely effects the desired signal, if multiple RUEs

transmit information signals using the same channel, simultaneously. Therefore, for the

selection of RUEs, interference should be taken into account. Preliminary version of this

work has been presented in a conference, which briefly discusses the selection of energy

harvesting RUEs in an interference free environment [20].

In this paper, we propose an RF energy harvesting-based battery recharging mechanism

for RUEs. To the best of our knowledge, energy harvesting technique for D2D-based relay

network in public safety scenario has not been studied in previous works. Main objective of

our proposed scheme is to prolong the network lifetime of a D2D-based public safety

network. For this purpose, we investigate the use of energy harvesting RUEs, which are

also used to extend the network coverage in a public safety environment. This provides a

more accurate characterization of system performance than our previous work [19], where

RUEs rely solely on their battery power. Furthermore, to exploit the inherent diversity gain

of relaying, we also propose an RUE selection scheme. The proposed selection

scheme considers the battery status of candidate RUE in addition to the eNB-RUE link

capacity. We conduct system level simulations to study the effect of different parameters

on the throughput of the proposed D2D-based public safety network.

The rest of the paper is organized as follows. The next section discusses the related

works. Section 3 introduces a system model and energy harvesting techniques. A proposed

relay selection scheme is presented in Sect. 4. Performance evaluation is provided in

Sect. 5 and the paper is concluded in Sect. 6.

2 Related Work

The concept of relaying information using legacy cellular UEs has been addressed in

previous works. In [15], the authors have used RUEs to extend the coverage of femtocells.

In this RUE assisted heterogeneous cellular network, idle UEs opportunistically offload

from macrocell to femtocell. Nishiyama et al. have proposed a network architecture based

only on smart phones, which can relay messages to multi-hop distance without using

network infrastructure [16]. In [21], authors have proposed a network topology composed

of UEs as virtual infrastructure nodes, providing both capacity gains and enhanced network

coverage. However, these schemes consider an interference-free environment, which is

unrealistic in a wireless communication setup.

The benefits of an efficient relay selection scheme are many folds, such as, increased

throughput and energy efficiency, reduced signaling overhead, less number of relay re-

selection and so on. Three RUE selection schemes are proposed in [22], namely, best relay

selection, relay cooperation and relay ordering. These schemes are based on channel state

information (CSI) feedback to maximize the end-to-end transmission rate given a certain

outage probability. Another relay selection scheme is proposed in [19] for public safety

situations, where the radial velocity of RUEs is also considered along with CSI feedback.

However, these schemes may fail to fully exploit the benefits if the selected RUEs do not

have sufficient battery power to support IUEs. Therefore, sustainable battery power supply

should be available for RUEs in public safety situations.

Recently, energy harvesting from RF signals has become a sustainable solution to

recharge the power constrained devices [8]. An excessive amount of researches have been

carried out in this field. Initially, the research in wireless energy harvesting and information

Information Processing and Wireless Energy Harvesting in... 2073

123

processing has considered one-hop communication systems and studied rate-energy (R-E)

tradeoff [9, 10]. For realization of simultaneous wireless information and power transfer

(SWIPT), a practical receiver architecture has been proposed in [11], where the perfor-

mance of two receiver strategies, time switching (TS) and power splitting (PS) has been

analyzed.

RF energy harvesting has also been investigated for wireless relay networks. Nasir et al.

in [23] have proposed two relaying protocols based on PS and TS schemes, TS-based

relaying (TSR) and PS-based relaying (PSR). The relay switches in time between energy

harvesting and information decoding for TSR protocol. On the other hand, in PSR, the

received power at relay is split in two portions one for energy harvesting and the other for

information decoding. The power constraint relay harvests energy from received RF sig-

nals first and then forwards the source signals to the destination, utilizing the harvested

energy. Authors extend this work to two-way relay network for TSR scheme in [24]. In

[25], the authors further evaluate the throughput of two-way relay network for TSR, PSR

and a hybrid of TSR and PSR (HPTSR) schemes.

Furthermore, Huang et al. in [26] have provided key features of a wireless powered

cellular network (WPCN). A discussion about practical implementation of SWIPT in

cellular networks is also given. Several design issues such as range of transferring power,

safety in WPCN, interference with wireless communications and powering UEs through

energy harvesting are also addressed in this work. Fundamental challenges such as

designing receiver architecture for SWIPT from base station in the downlink, SNR outage

regions in the uplink due to doubly near-far problem, broadband energy harvesting and

mutltiuser scheduling in WPCNs have been pointed out in [27]. To tackle these issues,

several guidelines such as deployment of dedicated energy source, characterization of SNR

zones, coordination among eNBs and on request energy transfer protocols are also

suggested.

The performance of energy harvesting-based D2D communications in a cognitive

network has been recently investigated for different spectrum access policies [28]. The

authors have shown that energy harvesting can be a reliable alternative to power cognitive

D2D transmitters while achieving acceptable performance. D2D communications under-

lying cellular networks, where D2D transmitter harvests energy from ambient sources has

been studied in [29]. To maximize the sum-rate of D2D links, authors have proposed a

joint resource block and power allocation scheme. A framework of the D2D communi-

cations is developed for energy harvesting-based heterogeneous cellular networks by

accounting for the energy harvesting parameters, eNB density, and the UE density [30].

Authors in [31] have proposed a wireless energy transfer scheme, where eNB and nearby

UEs transfer energy to a battery starved UE. The network operator is responsible for

managing energy transfer between these entities. These works provide the feasibility of

using energy harvesting in D2D-based cellular networks. However, energy harvesting-

based D2D communication has not been studied for a public safety environment where

network infrastructure is partially damaged.

All these works are very affective for the recent advancements in SWIPT for cooper-

ative networks. Our aim is to investigate D2D-based relay communications with energy

harvesting capabilities in public safety networks. In this article, we focus on the selection

of energy constraint RUEs based on eNB-RUE link capacity and battery status of RUEs.

We investigate its gains in terms of network lifetime and range extension. We first find an

optimal RUE for a single IUE and then globally optimize the relay selection process so that

the selected RUE cast minimum interference to other UEs.

2074 D. Munir et al.

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3 System Model

A three-tier cellular network consisting of 19 hexagonal cells has been considered, where

eNB is equipped with 3-sectored antennas deployed in the center of each cell. For PS

situation, we consider that there are only k active cells and all the UEs in other cells are

located out of the coverage area of these active cells, called IUEs. We assume that the

network employs frequency division duplex (FDD) with separate uplink (UL) and

downlink (DL) channels and a dedicated spectrum for IUEs termed as side-link (SL)

channel [32]. We consider a Rayleigh block-fading channel, where the channel gain is

invariant over each scheduling time and may vary independently from one scheduling time

to another.

UEs are randomly and uniformly deployed in each cell, where UEs that directly

communicate with the eNB are called cellular UEs (CUEs). For a cellular link to be

established with the eNB, a minimum downlink wireless access network (WAN) signal-to-

interference-plus-noise-ratio (SINR) is required. IUEs are unable to communicate directly

with eNB because they do not achieve minimum required WAN SINR from the eNB of the

active cells. Hence, IUEs require an intermediate node to assist their transmission to the

eNB, called RUEs.

Each scheduling time block T is divided into two time slots, i.e. in the first slot T/2 eNB

transmits the signals to RUE and in the next T/2 slot RUE forwards the received signal to

IUE. There are M number of IUEs and N number of cellular UEs that are acting as

candidate RUEs. We denote IUE set as I and candidate RUE set as J , where I ¼f1; 2; 3; . . .;Mg and J ¼ f1; 2; 3; . . .;Ng. The SINR between IUEs and RUEs is known as

D2D-SINR. We assume that all the UEs in the network move with a random speed and

direction. We use random way point (RWP) mobility model to represent the movement of

the UEs. To be more specific, the UE pauses for fixed time at a location and the change it’s

destination, speed and direction randomly and independently of other UEs. Also, RUEs are

equipped with single receiver antennas having SWIPT capabilities.

The set of eNBs is denoted as K ¼ f1; 2; . . .;Og, where each eNB is a multiuser system

which serves multiple UEs simultaneously. The channel between eNB k and UE j expe-

riences independent Rayleigh fading with complex channel fading gain hk;j. It is assumed

that channel state information of all the in-coverage UEs is known to the eNB, which

schedules them on orthogonal UL and DL channels to avoid interference. However, cell-

edge UEs may experience co-channel interference from neighboring eNBs as shown in

Fig. 2a. The received signal at the UE j is then given as

yk;j ¼ffiffiffiffiffi

Pk

pjhk;jjxkffiffiffiffiffiffiffi

dmk;jp þ

X

k0 :k0 6¼k

ffiffiffiffiffiffi

Pk0

p

jhk0 ;jjxk0ffiffiffiffiffiffiffiffi

dmk0;j

q þ nj; ð1Þ

where Pk is the received power from eNB k; xk is the received information signal, dk;j is the

distance between eNB k and UE j, m is the pathloss exponent and nj is the additive white

Gaussian noise (AWGN) with zero mean and variance r2j at UE j.

For energy harvesting, we adopt a power splitting receiver at UE j that splits the power

of the received signals into two portions, q portion of the power for energy harvesting and

1� q for information processing as shown in Fig. 2b. UE j harvests energy from the

received information signals and the interference signals for a duration of T/2 and hence

the harvested energy is obtained as

Information Processing and Wireless Energy Harvesting in... 2075

123

Ek;j ¼ gqPkjhk;jj2

dmk;jþ

X

k0 :k0 6¼k

Pk0 jhk0 ;jj

2

dmk0;j

0

@

1

AT=2; ð2Þ

where g is energy conversion efficiency with values varying from 0 to 1 depending upon

the energy harvesting circuitry. After q portion of the received signal power is used for

energy harvesting, remaining portion of the received signal can be written as

y0

k;j ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ð1� qÞPk

dmk;j

s

jhk;jjxk þX

k0 :k0 6¼k

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ð1� qÞPk0

dmk0;j

s

jhk0 ;jjxk0 þ nj: ð3Þ

The IUEs which reside out of the coverage area of active eNBs, receive the signals from

the intermediate node between IUE and eNB. The channel coefficient from a potential

RUE j to IUE i can be represented as hj;i. The power consumed by the transmit/receive

circuitry at the relay is assumed negligible as compared to the power utilized for trans-

mitting signals [33]. Therefore, we suppose that all the harvested energy from received

signals of eNB is used by the RUE j for recharging its battery.

The transmitted signal by RUE j is received at IUE i and can be given by

yj;i ¼ffiffiffiffiffiffi

Pj

dmj;i

s

jhj;ijxj þX

j0 :j0 6¼j

ffiffiffiffiffiffiffi

Pj0

dmj0;i

s

jhj0 ;ijxj0 þ ni; ð4Þ

where Pj is the transmission power of RUE j,P

j0 :j0 6¼j Pj

0 is the sum of transmission power

of all the interfering RUEs, hj;i is the channel fading gain from RUE j to IUE i; xj is the

transmitted information signal, dj;i is the distance between UE j and IUE i, m is the

pathloss exponent and ni is the AWGN at IUE i, which is assumed to have zero mean and

variance r2i .Similarly, when IUE i wants to send its data to eNB it sends the information signal to its

selected RUE. The received signal at RUE j from IUE i is given as

(a) (b)

Fig. 2 System model

2076 D. Munir et al.

123

yi;j ¼ffiffiffiffiffiffi

Pi

dmi;j

s

jhi;jjxi þX

i0 :i0 6¼i

ffiffiffiffiffiffiffi

Pi0

dmi0;j

s

jhi0 ;jjxi0 þ nj; ð5Þ

where Pi is the transmission power of IUE i; hi;j is the channel fading gain from IUE i to

RUE j and xi is the transmitted information signal. RUE j harvests energy from the power

of the received information signal of IUE i and the interfering signals of other IUEs and it

can be given as

Ei;j ¼ gqPijhi;jj2

dmi;jþ

X

i0 :i0 6¼i

Pi0 jhi0 ;jj

2

dmi0;j

0

@

1

AT=2: ð6Þ

After energy is harvested from the q portion of the information signal and interference

signals, the remaining 1� q portion is sent for information decoding. The signal received

for information processing is written as

y0

i;j ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ð1� qÞPi

dmi;j

s

jhi;jjxi þX

i0 :i0 6¼i

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ð1� qÞPi0

dmi0;j

s

jhi0 ;jjxi0 þ nj: ð7Þ

By Shanon’s formula rðxÞ ¼ b � log2ð1þ xÞ, throughput of each link over their respective

channels can be calculated.

4 Proposed Scheme

4.1 Battery Recharging

For the case where RUEs rely only on the harvested energy to transmit data, the accu-

mulated energy should be greater than the consumed energy i.e., Eq;j �Ec; 8 q 2 fK [ Ig.Ec denotes the energy consumed by RUE j to carry out each transmission. Since energy

harvested in a single time block is relatively small as compared to the consumed energy,

RUE j accumulates energy for U number of time blocks for one transmission. To calculate

the number of time blocks needed to accumulate this energy is complex because of the

random nature of channel coefficient and distance between RUE j and eNB k/IUE i.

However, average number of time blocks needed to accumulate enough energy for one

transmission can be obtained from the following condition,

X

U�1

x¼1

EðxÞq;j\Ec �

X

U

x¼1

EðxÞq;j ; 8 q 2 fK [ Ig and U[ 2 ð8Þ

where EðxÞq;j denotes the amount of harvested energy in time block x.

3GPP has specified a fixed transmission power for RUEs in public safety [34], and

harvested energy in one time block is not enough to carry out transmission. So, the

harvested energy will be stored in the rechargeable battery of RUEs for later use. In our

proposed scheme, RUEs do not rely solely on the harvested energy for carrying out relay

operations. RUEs use battery power to relay information to IUEs. The harvested energy, in

each time block, is added to the batteries of RUEs and serves as an additional source of

power for RUEs. From the viewpoint of long network time, the small amount of harvested

energy in each time block can be used to extend the operating time of RUEs.

Information Processing and Wireless Energy Harvesting in... 2077

123

The aggregate energy harvested at RUE j is the sum of harvested energy from the

received signals of both eNB and IUEs and can be written as

Etotj ¼ Ek;j þ Ei;j: ð9Þ

The harvested energy is stored in a battery with a storage size of Bmax. The battery size of

all the RUEs is assumed to be identical with a discrete battery model. Specifically, the

energy level of each RUE battery is quantized into L identical intervals. The current energy

level of the RUE battery is denoted by Bj and can be given by

Bj ¼ B0 þ Eq;j � Ec; 8 q 2 fK [ Ig ð10Þ

where B0 is the previous energy level of the battery. With a discrete battery model each

RUE has to report the quantized value of the energy level in the battery. This may reduce

the number of feedback bits to dlogL2e, where d�e denotes the ceiling function [35]. Intu-

itively, more feedback bits will be used for a large L, resulting in a reduced quantization

error. The proposed energy harvesting scheme for RUEs in public safety is summarized in

Algorithm 1.

Algorithm 1 Wireless energy harvesting in public safety UE relaying1: I = {i|i = 1, 2, · · · , M} Set of IUEs2: J = {j|j = 1, 2, · · · , N} Set of candidate RUEs3: K = {k|k = 1, 2, · · · , O} Set of active eNBs4: procedure UE relaying procedure using energy harvesting RUEs5: eNB k transmits radio signals in its coverage area.6: if yk,j received at RUE j from eNB k then7: Harvest ρ portion of signal power as (2)8: end if9: RUE j broadcasts its availability to requesting IUE i10: if yi,j received at RUE j from IUE i then11: Harvest ρ portion of signal power as (6)12: end if13: for (j = 1; j <= N ; j ++) do14: Accumulate total harvested energy at j as (10)15: end for16: end procedure

4.2 Relay Selection

In this section, we propose the relaying procedure for public safety environments, where

CUEs act as relays. A CUE receives reference signals from the eNB and discovery signals

from IUEs present in its proximity. CUE obtains the CSI from these received signals and

calculates the achievable throughput for each link. Based on the achievable throughput,

CUE broadcasts its status of whether or not it will take the role of RUE. Each RUE-assisted

transmission from eNB to IUE and vice versa, is divided into two phases. In the first phase,

eNB/IUE transmits signals to RUE and in the next phase, RUE forwards the signals to IUE/

eNB. As RUE uses PS receiver, it harvests the energy from a portion of received signal

power in each phase and calculates the throughput from the remaining portion of the

received signal power.

Throughput is considered as the main performance metric for a relay-assisted system

such as D2D-based network in public safety environments. This is the reason we take into

2078 D. Munir et al.

123

account the capacity of each link for the selection of RUEs. In the first phase, eNB k

periodically broadcasts reference signals in its coverage area. Candidate RUE j located in

the coverage area of eNB k, receives the reference signals and estimate the amount of

energy it can harvest from the received signal power according to (2). Because of the

power splitting receiver structure used at RUE j, the remaining signal power is used by j for

information processing. Note that RUE j may also receive reference signals from other

active eNBs, which may cause interference to j. The received signal-to-interference-plus-

noise ratio (SINR) at RUE, is given by

ck;j ¼ð1� qÞPkjhk;jj2

r2r þP

k0 :k0 6¼kð1� qÞPk

0 jhk0 ;jj2: ð11Þ

This SINR is used to calculate the link capacity by using Shanon capacity formula as

rk;j ¼ b � log2ð1þ ck;jÞ: ð12Þ

IUE i understands that it is located out of the coverage area of eNB if it does not receive

any reference signals from its attached eNB. In the same phase, if IUE i has some data to

transmit, it initiates discovery procedure by broadcasting service request message in its

proximity. All the candidate RUEs in the proximity of IUE i respond to this services

request message. This response message also contains the eNB-RUE link capacity and it’s

battery state information. Based on this information, IUE i selects a more reliable RUE for

its communication with the eNB. From received response message of each RUE, IUE i

calculates the SINR of this link as

cj;i ¼Pjjhj;ij2

r2d þP

j0 :j0 6¼j Pj

0 jhj0 ;ij2: ð13Þ

Similar approach is used to find the capacity of this link

rj;i ¼ b � log2ð1þ cj;iÞ: ð14Þ

To this point, IUE i has all the necessary information to select a reliable RUE. In other

words, a subset (J i) of candidate RUEs is defined from the view point of IUE i. These

RUEs are not only able to decode the information from eNB but also have enough energy

to forward the data to IUEs. For each candidate RUE, IUE i has the CSI of both the links,

i.e. eNB-RUE and RUE-IUE. IUE i is now able to select a reliable RUE with the highest

CSI as

j� ¼ maxj2J i

minfck;j; cj;ig

subject to :Bj;i �Bth;ð15Þ

where Bj;i ¼ max8j2J iBj is the maximum measured value of battery level of candidate

RUEs in J i.

This corresponds to the max-min fairness metric, which is an efficient metric for

selecting DF relays [36]. It is an appropriate solution for selecting DF relays and its

implementation complexity is low. This RUE selection metric represents the preferences of

the IUE i and disregards the selection preferences of other IUEs. However, when there is a

conflict between the preferences of two IUEs, the selection should be globally optimized.

After identifying the most appropriate RUE for a single IUE, now the goal of the RUE

selection metric is to optimize the following summation

Information Processing and Wireless Energy Harvesting in... 2079

123

maxf1;2;...;Ng

X

j

minfck;j; cj;ig: ð16Þ

Given the sets of IUEs and RUEs, this problem is precisely a bipartite graph matching

problem, where the value of metric is considered as the weight on the edge between an IUE

and a candidate RUE. At this point, the aim of the RUE selection scheme is to select the

RUE for each IUE in such a way that is best for overall system performance. We map our

RUE selection algorithm as a weighted bipartite matching problem [37]. As defined before

the set of IUE (with N elements) and set of RUEs (withM elements) were represented by Iand J , respectively. Each vertex in the graph is represented by an element in I and J and

vertex of each set has an edge with the vertex of the other. The weight of the edge between

i 2 I and j 2 J is

wi;j ¼ minfck;j; cj;ig ð17Þ

Finally, the bipartite graph is denoted as G ¼ ðI [ J ; I � J Þ [37], where I [ J repre-

sents all vertices and I � J represents all edges. Weight matrix is denoted by W with size

M � N, which has the elements wi;j. For example, there are three candidate RUEs for four

IUEs to select, and assume the weight matrix as

W ¼4 4 3 0

1 2 4 1

0 3 4 3

0

@

1

A ð18Þ

This particular system is depicted in Fig. 3, which shows a bipartite graph. There is a

weight line between each IUE and RUE. To integrate the distributed design of RUE

selection, the optimal stable state (OSS: stable state with maximal utility) is defined as this

terminology is used in matching games [38].

The distributed implementation of the proposed selection metric is explained here, which

is established on the approach of stable state. Different steps involved in this distributed

selection are explained in the following and are summarized in Algorithm 2. First, each IUE i

and RUE jmaintains a preference listPLi andPLj, respectively. Taking an IUE i for example,

the PLi stores all the candidate RUEs in the descending order according to their weights, i.e.,

more priority is given to the most suitable RUE for IUE i. Then, each IUE i has its own list

matched(i) specifying its selected RUE. Each RUE j has a list matched(j) consisting of the

IUE which can be chosen. Each RUE j also maintains a list candidates(j), which consists of

those IUEs that have requested for the selection of RUE j. When all the IUEs are tagged as

‘‘matched’’, the algorithm ends, i.e., RUEs have been assigned to all the IUEs.

2080 D. Munir et al.

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Algorithm 2 Proposed relay selection algorithm1: Preference list PLi for I = {i|i = 1, 2, · · · , M}2: Preference list PLj for J = {j|j = 1, 2, · · · , N}3: procedure Weighted bipartite matching problem4: Initialize5: matched(i) = ∅, ∀i ∈ I6: matched(j) = ∅, candidates(j)= ∅, ∀j ∈ J7: while ∃i ∈ I subject to: matched(i) = ∅ do8: for i ∈ I do9: if matched(i) = ∅ then10: j∗ ← most suitable RUE in PLi

11: candidates(j∗) ← candidates(j∗) {i}12: Remove j∗ from PLi

13: end if14: end for15: for j ∈ J do16: candidates(j) ← candidates(j) matched(j)17: i∗ ← first ranked element in candidates(j) according to PLj

18: i∗∗ ← matched(j)19: matched(i∗∗) = ∅20: matched(i∗) = j and matched(j)= i∗21: candidates(j) = ∅22: end for23: end while24: end procedure25: matched(i) is the final selection, ∀i ∈ I

When the algorithm is triggered for the first time (lines 8–14), each IUE, without being

matched, sends request to the most suitable RUE from PLi. The candidate list of the

corresponding RUE stores this request (line 11). Those IUEs which are accepted from

corresponding RUEs are ‘‘matched’’ and will not request again. If IUE’s request is denied,

Fig. 3 Network scenario depicted as a Bipartite graph

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the requested RUE is eliminated from PLi, to avoid requesting the same RUE for next

instance (line 12).

In the next part of the algorithm (lines 15–22), each RUE choose its favorite IUE from

already labeled as ‘‘matched’’ and those that requested (stored in the list candidates(j)) this

time. The selected IUE will be the one which is labeled ‘‘matched’’ this time. If the

selected IUE is different from already matched IUE, the original IUE is reset to ‘‘not

matched’’ (lines 18–20). As the algorithm ends, no RUE will deviate to a more favorable

RUE, and IUE can not select a better RUE without outperforming the selection criteria of

another IUE, which shows that the system will be stable. An IUE will select a new RUE if

the battery power of the selected RUE is drained and it can no longer assist the commu-

nication between IUE and eNB.

The complexity of this algorithm is O(MN). In the beginning, N IUEs request to select

their most favorite RUEs. Then, any RUE may search for more suitable IUE, which is

possible for maximum N � 1 times even if the RUE attempts all the available IUEs. In

addition, since M RUEs are present in the system, the worst case will be N þMðN � 1Þrepetition of the algorithm, which makes the complexity of the MN order.

The proposed scheme is summarized as follows. IUEs should determine the weight wi;j

for each candidate RUE. At first, the channel estimation from eNB k to candidate RUE j is

calculated. When a candidate RUE j broadcasts its status along with the information about

ck;j in its proximity, IUEs receive this information and can also estimate ci;j from the

received signals. Now IUEs can calculate the weight wi;j, and store it in its preference list.

IUEs then send the weight to candidate RUE, which upon receiving also stores these

weights in its own preference list. For this purpose, MN transmissions of a real number are

required. When the Algorithm 2 starts, the request from IUE for selecting its RUE can be

transmitted through 1-bit information, which will be responded with another 1-bit infor-

mation by the RUE, to announce the approval or denial of the request.

5 Performance Evaluation

In this section, we evaluate the performance of our proposed scheme. The performance

evaluation is carried out using event-driven simulation developed in C language [40].

These simulations are executed by using 3GPP recommended UE relaying procedure.

Table 1 Simulation parametersParameters Studied value

Number of cells 19

Inter-cell distance 1732 m

Pathloss exponent (m) 2

Carrier frequency 2.4 GHz and 700 MHz

Bandwidth 10 MHZ for UL, DL and SL

Number of UEs per cell 30–300

Mobility model RWP (speed: 0–5 m/s)

Tx power eNB to UE 46 dBm

Tx power UE to eNB 24 dBm

Tx power UE to UE 31 dBm

Noise power density - 174 dBm/Hz

s.d. of shadow fading 8 dBm (based on [39])

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Different events, such as, packet generation, WAN scheduling, WAN transmission, D2D

scheduling, D2D transmission and UE relay re-selection, are defined for step-by-step

implementation of the simulations. The parameters of interests include network life time

and expected throughput of both uplink and downlink channels from eNB to IUE via RUE.

We have considered the data transmission throughput as the main performance metric for

this relaying network as it represents the quality of service of public safety applications in a

better way. The results show how these parameters are affected by the varying number of

UEs present in the network. Also, the impact of energy harvesting ratio q on these

parameters are described.

The considered network for simulation environment consists of 19 three-sectored cells,

where only 3 cells are active and other cells are switched off to emulate a public safety

environment, as recommended by 3GPP [32]. The inter-site distance between each cell is

1732 m. We assume that the network employs frequency division duplexing with 10 MHz

for each UL and DL channel at the center frequency of 2.4 GHz and a 10 MHz channel for

SL at 700 MHz. Studied values of other important parameters are summarized in Table 1

and are in accordance with the 3GPP’s specification.

There are 30–300 UEs deployed randomly and uniformly in each cell, with a step size of

30. The mobility of all the UEs is illustrated by random waypoint mobility model, with a

speed uniformly distributed between 0 and 5 m/s and no pauses. The UEs present in the

active cells communicate with the associated eNB through direct UL and DL channels.

These UEs can also serve as RUEs for IUEs present in the coverage area of non-active

cells. RUEs and IUEs communicate with each other on direct D2D links. RUEs present in a

cell experience co-channel interference from other active cells while communicating with

eNB on the DL channel and from other IUEs on SL channel while communicating with its

attached IUEs. Full buffer traffic model is considered for all the UEs.

RUEs harvest energy from both the information and interference signals received on DL

and SL channels. The received signal power is split into two portions according to q and

1� q. To provide additional battery power to RUEs, energy is harvested from the q signal

power. The remaining (1� q) signal power is used for further information processing.

( = 0.7)( = 0.3)

Fig. 4 Achievable throughput at RUE from eNB (LTE DL)

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Note that at least -6 dBm signal power is required for successful information processing

[39].

At first, we investigate the achievable throughput of the network for 30 UEs, deployed

in each cell. The initial battery power of each UE is set to 3000 mAh, which is a common

specification for most of the smart phones commercially available. Battery of a UE is

consumed when a UE is active, transmits and receives signals. Harvested energy from the

received RF signals is converted into electrical power, which is added to the battery of

RUEs for recharging. Throughput of each link between IUE and eNB is shown for varying

values of q. In addition, a comparison is shown when there is no energy harvesting at

RUEs.

Achievable throughput at the RUE from eNB is affected by the power splitting factor

(q). This is because for higher values of q, more power is used for energy harvesting,

which results in lower throughput as compared to the lower values of q. Figure 4 shows the

achievable throughput of this link with respect to time. The monotonically decreasing

relationship between achievable throughput and time is due to decreasing number of active

links between eNB and RUEs. In other words, after a certain period of time, the batteries of

UEs drain and the number of RUEs decreases, which leads to lower throughput.

The achievable throughput at eNB is not directly effected by q, because eNB has it’s

own power and does not perform energy harvesting. However, the UL traffic generated by

RUEs may vary according to energy harvesting q. The energy harvesting RUEs prolong

their battery life, however, the throughput decreases because less power is available for

information decoding for higher values of q. RUEs relay the same decoded information

towards eNB which is lower for higher values of q. Figure 5 shows the results for the

achievable throughput at eNB from RUEs and CUEs.

Furthermore, the achievable throughput at RUEs from its attached IUEs is shown in

Fig. 6. Energy harvesting increases the battery life of RUEs which enables them to

communicate for longer period of time. The impact of energy harvesting is shown in this

figure and it can be observed that for lower values of q the achievable throughput is higher.

The RUE harvests energy from all the received signals either from its attached IUEs or

( = 0.7)( = 0.3)

Fig. 5 Achievable throughput at eNB from RUE (LTE UL)

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from other interfering IUEs. The interfering IUEs degrade the performance of RUEs in

terms of throughput.

The link between IUE and RUE is of significant importance and its performance directly

effects the performance of end-to-end link between IUE and eNB. Figure 7 shows the

achievable throughput at IUE from RUEs. As there is no energy harvesting at IUEs, there

is no direct impact of q on the achievable throughput of this link. However, the throughput

decreases for higher values of q, because less information will be forwarded by RUEs.

( = 0.7)( = 0.3)

Fig. 6 Achievable throughput at RUE from IUE (D2D UL)

( = 0.7)( = 0.3)

Fig. 7 Achievable throughput at IUE from RUE (D2D DL)

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(a) (b)

(c) (d)

Fig. 8 Different states of network at t ¼ 0 s and t ¼ 2000 s with varying value of q. a State of network forq ¼ 0:0; 0:30; 0:7at t = 0s. b State of network for q ¼ 0:0at t = 2000S. c State of network for q ¼ 0:30at t =2000s. d State of network for q ¼ 0:7at t = 2000s

( = 0.7)( = 0.3)

Fig. 9 Total throughput from eNB to IUE via RUE with varying number of UEs

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To show the effect of proposed scheme on the battery life of UEs, we capture the state

of network at two time stamps i.e. t ¼ 0 and t ¼ 2000. For varying values of q, Fig. 8shows different sates of UEs present in the network when number of UEs per cell is set to

30. There are only three active eNBs and the corresponding cells are highlighted in the

figure. In Fig. 8, attached RUEs are those UEs that are acting as relays for IUEs and those

RUEs which have zero battery are named as drained RUEs. Furthermore, IUEs that are

attached with RUEs are called attached IUEs and those IUEs that cannot find a suit-

able RUE are unattached IUEs. Finally, the UEs communicating with eNB directly on UL

and DL channels are called as CUEs.

At t ¼ 0, the state of all the UEs is same for each value of q, which is shown in Fig. 8a.

Note that there are no drained RUEs at t ¼ 0 because we consider that the battery of each

UE is fully charged. For q ¼ 0, which means without energy harvesting, the number of

active UEs is small at t ¼ 2000. At this stage of time, most of the UEs drain their battery

power which is shown in Fig. 8b. The number of active UEs increases as the value of qincreases for the same time t ¼ 2000. This is because of the fact that proposed scheme uses

RF energy harvesting for recharging the batteries of RUEs as shown in Fig. 8c, d.

We observe the total throughput of the network while deploying different number of

UEs in each cell. The total throughput of the network is calculated until all the RUEs are

alive and active. Figure 9 shows the total DL throughput with varying number of UEs. It

can be observed that as the number of UEs in each cell increases the total throughput of the

network increases initially. However, when the number of UEs per cell reaches a certain

point (240 in Fig. 9) congestion occurs due to increased interference and the total network

throughput starts decreasing.

Similarly, Fig. 10 shows total UL throughput with different number of UEs deployed in

each cell. We observe same trends as shown in Fig. 9 but the overall throughput of UL

channel is less than that of DL channel. In addition to the low transmit power of UEs as

compared to eNB, increasing number of IUEs will cause severe interference to RUEs,

which results in decreased throughput.

( = 0.7)( = 0.3)

Fig. 10 Total throughput from IUE to eNB via RUE with varying number of UEs

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The accumulate harvested power by RUEs in the proposed scheme is shown in Fig. 11

with respect to time for different values of q. It can be observed from the figure that the

accumulate harvested power linearly increase in time. Also, the rate of increase for higher

values of q is greater as compared to the lower values of q. This is because of the fact that alarger portion of signal power is utilized for energy harvesting, in case of higher values of

q. After a certain period in time, the number of RUEs is decreased and there will be less

available RUEs for energy harvesting.

6 Conclusion

In this article, we have proposed a UE relaying procedure, where relay UEs harvest energy

from the source signals and interference signals from the sources of other relay UEs. The

source can be eNB while RUE relay information to the IUE and it can be IUE when RUE

relay its information to the eNB. UE relaying helps in extending the coverage area of eNB,

specifically in the public safety environment, where the network access is partially

available. In addition to this, the energy harvesting at the relay UEs helps extend its battery

life, hence the network coverage and battery life time is improved for a better overall

performance. The performance metric used for the evaluation of the proposed scheme is

achievable throughput and battery life time of available RUEs. The performance is eval-

uated using system level simulation and the results are shown for varying number of UEs

present in a cell.

Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) Grantfunded by the Korean Government (MSIP) (2014R1A5A1011478).

Fig. 11 Aggregate harvested power at RUES for varying values of q

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References

1. Doumi, T., Dolan, M., Tatesh, S., Casati, A., Tsirtsis, G., Anchan, K., et al. (2013). LTE for publicsafety networks. IEEE Communications Magazine, 51(2), 106–112.

2. Lin, X., Andrews, J. G., Ghosh, A., & Ratasuk, R. (2014). An overview of 3GPP device-to-deviceproximity services. IEEE Communications Magazine, 52(4), 40–48.

3. Fodor, G., Parkvall, S., Sorrentino, S., Wallentin, P., Lu, Q., & Brahmi, N. (2015). Device-to-devicecommunications for national security and public safety. IEEE Access, 2, 1510–1520.

4. Shah, S. T., Hasan, S. F., Seet, B. C., Chong, P. H. J., & Chung, M. Y. (2017). Device-to-devicecommunications: A contemporary survey. Wireless Personal Communications. https://doi.org/10.1007/s11277-017-4918-4.

5. Alam, M. S., Mark, J. W., & Shen, X. (2013). Relay selection and resource allocation for multi-usercooperative OFDMA networks. IEEE Transaction in Wireless Communication, 12(5), 2193–2205.

6. Son, P. N., & Kong, H. Y. (2017). Co-channel interference energy harvesting for decode-and-forwardrelaying. Wireless Personal Communications, 95(4), 3629–3652.

7. Rajesh, R., Sharma, V., & Viswanath, P. (2011). Information capacity of energy harvesting sensornodes. In IEEE international symposium on information theory, 2011 (pp. 2363–2367).

8. Varshney, L. R. (2008). Transporting information and energy simultaneously. In IEEE internationalsymposium on information theory, 2008 (pp. 1612–1616).

9. Grover, P., & Sahai, A. (2010) Shannon meets tesla: Wireless information and power transfer. In IEEEinternational symposium on information theory, 2008 (pp. 2363–2367).

10. Popovski, P., Fouladgar, A. M., & Simeone, O. (2013). Interactive joint transfer of energy and infor-mation. IEEE Transaction on Communication, 61(5), 2086–2097.

11. Zhou, X., Zhang, R., & Ho, C. K. (2013). Wireless information and power transfer: Architecture designand rate-energy tradeoff. IEEE Transaction on Communication, 61(11), 4754–4767.

12. Ulukus, S., Yener, A., Erkip, E., Simeone, O., Zorzi, M., Grover, P., et al. (2015). Energy harvestingwireless communications: A review of recent advances. IEEE Journal on Selected Areas in Commu-nications, 33(3), 360–381.

13. Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2014). Wireless networks with RF energyharvesting: A contemporary survey. IEEE Communication Surveys & Tutorials, 17(2), 757–789.

14. Abrardo, A., Fodor, G., & Tola, B. (2017). Network coding schemes for D2D communications basedrelaying for cellular coverage extension. Transactions on Emerging Telecommunications Technologies.https://doi.org/10.1002/ett.2994.

15. Mezzavilla, M., Somasundaram, K., & Zorzi, M. (2014). Joint user association and resource allocationin UE-relay assisted heterogeneous networks. In IEEE international conference on communicationsworkshops (ICC 2014) (pp. 628–634).

16. Nishiyama, H., Ito, M., & Kato, N. (2014). Relay-by-smartphone realizing multihop D2D communi-cation. IEEE Communications Magazine, 52(4), 56–65.

17. Babun, L., Yurekli, A. I., & Guvenc, I. (2015). Multi-hop and D2D communications for extendingcoverage in public safety scenarios. In IEEE 40th local computer networks conference workshops (LCNworkshops 2015) (pp. 912–919).

18. Munir, D., Gu, J., & Chung, M. Y. (2014). Selection of UE relay considering QoS class for public safetyservices in LTE-A network. In 20th Asia-Pacific conference on communications (APCC 2014) (pp.401–405).

19. Munir, D., Gu, J., Hasan, S. F., & Chung, M. Y. (2017). Reliable cooperative scheme for public safetyservices in LTE-A networks. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.3008.

20. Munir, D., Shah, S. T., Lee, W. J., Hasan, S. F., & Chung, M. Y. (2016). Selection of relay UE withenergy harvesting capabilities in public safety environment. In International conference on informationnetworking (ICOIN 2016) (pp. 40–44).

21. Vanganuru, K., Ferrante, S., & Sternberg, G. (2012). System capacity and coverage of a cellularnetwork with D2D mobile relays. In Military communication conference (Milcom 2012) (pp. 1–6).

22. Kim, J., Yang, J. R., & Kim, D. I. (2011). Optimal relaying strategy for UE relays. In 17th Asia-Pacificconference on communications (APCC 2011) (pp. 192–196).

23. Nasir, A. A., Zhou, X., Durrani, S., & Kennedy, R. A. (2013). Relaying protocols for wireless energyharvesting and information processing. IEEE Transaction on Wireless Communication, 12(7),3622–3636.

24. Shah, S. T., Munir, D., Choi, K. W., & Chung, M. Y. (2016). Information processing and wirelessenergy harvesting in two-way amplify-and-forward relay networks. In IEEE 83rd vehicular technologyconference (VTC Spring 2016) (pp. 1–5).

Information Processing and Wireless Energy Harvesting in... 2089

123

25. Shah, S. T., Choi, K. W., Hasan, S. F., & Chung, M. Y. (2016). Throughput analysis of two-way relaynetworks with wireless energy harvesting capabilities. Ad Hoc Networks, 53, 123–131.

26. Huang, K., & Zhou, X. (2015). Cutting the last wires for mobile communications by microwave powertransfer. IEEE Communications Magazine, 53(6), 6–93.

27. Tabassum, H., Hossain, E., Ogundipe, A., & Kim, D. I. (2015). Wireless-powered cellular networks:Key challenges and solution techniques. IEEE Communications Magazine, 53(6), 63–71.

28. Sakr, A. H., & Hossain, E. (2015). Cognitive and energy harvesting-based D2D communication incellular networks: Stochastic geometry modeling and analysis. IEEE Transaction on Communication,63(5), 1867–1880.

29. Gupta, S., Zhang, R., & Hanzo, L. (2016). Energy harvesting aided device-to-device communicationunderlaying the cellular downlink. IEEE Access, 5, 7405–7413.

30. Yang, H. H., Lee, J., & Quek, T. Q. S. (2016). Heterogeneous cellular network with energy harvesting-based D2D communication. IEEE Transactions on Wireless Communications, 15(2), 1406–1419.

31. Selim, M. Y., Alsharoa, A., Kamal, A. E., & Alnuem, M. A. (2017). SURE: A novel approach for selfhealing battery starved users using energy harvesting. IEEE Access, 5, 6110–6120.

32. 3GPP, TR 36.843. (2014). Technical specification group radio access network; study on LTE device todevice proximity services; radio aspects.

33. Medepally, B., & Mehta, N. B. (2010). Voluntary energy harvesting relays and selection in cooperativewireless networks. IEEE Transaction on Wireless Communication, 9(11), 3543–3553.

34. 3GPP, TS 23.303. (2015). Technical specification group services and system aspects; proximity-basedservices (ProSe).

35. Krikidis, I., Timotheou, S., & Sasaki, S. (2012). RF energy transfer for cooperative networks: Datarelaying or energy harvesting? IEEE Communication Letter, 16(11), 1772–1775.

36. Krikidis, I., Thompson, J., McLaughlin, S., & Goertz, N. (2009). Maxmin relay selection for legacyamplify-and-forward systems with interference. IEEE Transaction on Wireless Communications, 8(6),3016–3027.

37. West, D. B. (2000). Introduction to graph theory. Englewood Cliffs: Prentice Hall.38. Laureti, P., & Zhang, Y. C. (2003). Matching games with partial information. Physica A: Statistical

Mechanics and its Applications, 324(1–2), 49–65.39. Holma, H., & Toskala, A. (2009). LTE for UMTS: OFDMA and SC-FDMA based radio access. New

York: Wiley.40. Ferg, S. (2006). Event-driven programming: Introduction, tutorial, history. http://

TutorialEventDrivenProgramming.sourceforge.net.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institu-

tional affiliations.

Daniyal Munir received his B.S. degree in electrical (telecommuni-cation) engineering from COMSATS Institute of Information Tech-nology Lahore, Pakistan, in 2010. He is currently a Ph.D. candidate inthe Department of Electrical and Computer Engineering at Sung-kyunkwan University. His research interests include public safetynetworks, LTE-Advanced networks, wireless energy harvesting anddevice-to-device communication.

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Syed Tariq Shah received his B.S. degree in telecommunicationengineering from Baluchistan University of Information Technology,Engineering and Management Sciences, Pakistan, in 2009. He recentlyreceived his Ph.D. degree from the Department of Electrical andComputer Engineering at Sungkyunkwan University. He is also affil-iated with Dept. of Telecommunication Engineering, FICT,Balochistan University of Information Technology, Engineering andManagement Sciences, Pakistan. His research interests include 5Gnetworks, LTE-Advanced networks, wireless energy harvesting anddevice-to-device communication.

Kae Won Choi received the B.S. degree in Civil, Urban, andGeosystem Engineering in 2001, and the M.S. and Ph.D. degrees inElectrical Engineering and Computer Science in 2003 and 2007,respectively, all from Seoul National University, Seoul, Korea. From2008 to 2009, he was with Telecommunication Business of SamsungElectronics Co., Ltd., Korea. From 2009 to 2010, he was a postdoctoralresearcher in the Department of Electrical and Computer Engineering,University of Manitoba, Winnipeg, MB, Canada. From 2010 to 2016,he was an assistant professor in the Department of Computer Scienceand Engineering, Seoul National University of Science and Technol-ogy, Korea. In 2016, he joined the faculty at Sungkyunkwan Univer-sity, Korea, where he is currently an associate professor in the Schoolof Electronic and Electrical Engineering. His research interests includeRF energy transfer, visible light communication, device-to-devicecommunication, cognitive radio, radio resource management. He hasserved as an editor of IEEE Communications Surveys and Tutorials

from 2014 and an editor of IEEE Wireless Communications Letters from 2015.

Min Young Chung received the B.S., M.S., and Ph.D. degrees inelectrical engineering from the Korea Advanced Institute of Scienceand Technology, Daejeon, Korea, in 1990, 1993, and 1999, respec-tively. From January 1999 to February 2002, he was a Senior Memberof Technical Staff with the Electronics and TelecommunicationsResearch Institute, where he was engaged in research on the devel-opment of multiprotocol label switching systems. In March 2002, hejoined the Faculty of Sungkyunkwan University, Suwon, Korea, wherehe is currently a Professor with the College of Information andCommunication Engineering. His research interests include D2DCommunications, Software-Defined Networking (SDN), 5G wirelesscommunication networks, and wireless energy harvesting. He workedas an editor on the Journal of Communications and Networks fromJanuary 2005 to February 2011, and is a member of IEEE, IEICE,KICS, KIPS and KISS.

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