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10156 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 11, NOVEMBER 2017

Achieving High Availability in Heterogeneous

Cellular Networks via Spectrum Aggregation

Jie Jia , Member, IEEE, Yansha Deng , Member, IEEE,JianChen, Member, IEEE,

Abdol Hamid Aghvami, Fellow, IEEE, and Arumugam Nallanathan , Fellow, IEEE

Abstract—The exponential growth in data trafﬁc and dramatic

capacity demand in ﬁfth generation (5G) have inspired the move

from traditional single-tier cellular networks toward heteroge-

neous cellular networks (HCNs). To face the coming trend in 5G,

the high availability requirement in new applications needs to be

satisﬁed to achieve low latency service. Usually, these applications

require an availability of six nines or even higher. In this paper, we

present a tractable multitier multiband availability model for spec-

trum aggregation-based HCNs. We ﬁrst derive a closed-form ex-

pression for the availability of spectrum aggregation-based HCNs

using the signal-to-interference-plus-noise model. By doing so, we

formulate two optimization problems, one is to maximize the av-

erage availability, and the other one is to minimize the average

power consumption. These two optimization problems are both

nonconvex problems, which are challenging to solve. To cope with

them, we propose to apply genetic algorithm for the joint user

equipment (UE) association, subcarrier assignment, and power al-

location problem. Our results show that the average availability in

spectrum aggregation-based HCNs improves with decreasing num-

ber of UEs, as well as increasing power budget ratio. We also show

that increasing the maximum number of aggregated subcarriers

decreases the average power consumption, but cannot guarantee

the substantial improvement of average availability.

Index Terms—Genetic algorithm, heterogeneous cellular net-

work, high availability, power consumption, spectrum aggregation.

Manuscript received January 10, 2017; revised May 16, 2017 and July 24,

2017; accepted September 12, 2017. Date of publication September 26, 2017;

date of current version November 10, 2017. This work was supported in part

by the National Natural Science Foundation of China under Grants 61772126,

61402096, 61173153, and 61572123, in part by the Fundamental Research

Funds for the Central Universities under Grant N150404006, in part by the

National Science Foundation for Distinguished Young Scholars of China under

Grants 61225012 and 71325002, in part by the Specialized Research Fund of

the Doctoral Program of Higher Education for the Priority Development Areas

under Grant 20120042130003. The review of this paper was coordinated by

Prof. Y.-B. Lin. This paper was presented at the IEEE Global Communications

Conference, Washington, DC, USA, December 2016. (Corresponding author:

Yansha Deng.)

J. Jia and J. Chen are with the Key Laboratory of Medical Image Computing,

Northeastern University, Ministry of Education, Shenyang 110819, China, and

with the School of Computer Science and Engineering, Northeastern University,

Shenyang 110819, China, and also with the Department of Informatics, King’s

College London, London WC2R 2LS, U.K. (e-mail: jiajie@mail.neu.edu.cn;

chenjian@mail.neu.edu.cn).

Y. Deng and A. H. Aghvami are with the Department of Informatics, King’s

College London, London WC2R 2LS, U.K. (e-mail: yansha.deng@kcl.ac.uk;

hamid.aghvami@kcl.ac.uk).

A. Nallanathan is with the Department of Informatics, King’s College Lon-

don, London WC2R 2LS, U.K., and also with the Queen Mary University of

London, London E1 4NS, U.K. (e-mail: a.nallanathan@qmul.ac.uk).

Color versions of one or more of the ﬁgures in this paper are available online

at http://ieeexplore.ieee.org.

Digital Object Identiﬁer 10.1109/TVT.2017.2755504

I. INTRODUCTION

IN THE past, the target of wireless technologies has mainly

focused on achieving higher data rates and data volumes.

However, high average rate and high total data are not the only

performance indicators that guarantee the ubiquitous connectiv-

ity in next generation wireless networks. According to ABI Re-

search (Allied Business Intelligence Inc.), more than 30 billion

devices will be wirelessly connected to the Internet by 2020 [2].

The target of next generation wireless networks has extended

to realize high availability and low latency, in order to support

the upcoming new applications under the context of Internet of

Things (IoT), such as haptic communication [3], cloud comput-

ing [4], smart energy grids [5], vehicular communication [6], or

industrial automation [7]. The availability requirement of these

applications is six nines or higher. A detailed analysis on future

application as well as high availability requirement can be found

in [8].

The rapid growth of wireless data trafﬁc, fueled by an ever

increasing availability requirement of smart mobile computing

devices, imposes a huge challenge on current cellular networks.

Deploying more macro base stations (BSs) is no longer a sus-

tainable solution to handle the trafﬁc load. Whereas, deploy-

ing inexpensive, small-scale, low-power nodes in conventional

macrocells becomes a cost-effective solution, which is the so

called heterogeneous cellular networks (HCNs) [9]. These low

power nodes could be pico or femto BSs. However, due to the

heterogeneous deployments of those low power nodes, the in-

terference management among tiers becomes very challenging

and extremely important. In [10], [11], the ambient interfer-

ence from BSs have been ultized for energy transfer to improve

the energy efﬁciency of HetNets. With the irresistible demand

to support the aforementioned new applications in HCNs, the

modeling, characterization and optimization of availability in

HCNs becomes extremely important.

According to the reliability theory [12], generally, there are

two feasible methods to achieve high availability in a system.

The ﬁrst method is to substitute or improve some unreliable

sub-components to make the system more reliable. The other

method is to incorporate redundancy in order to improve the

system reliability, through utilizing multiple sub-components

in parallel. With multiple less reliable links connected to BSs

in parallel boost equivalent availability as that a single more

reliable link with higher transmit power or more robust coding.

Data transmission availability can be bootstrapped from

physical layer technology. For instance, Spectrum aggregation

0018-9545 © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

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JIA et al.: ACHIEVING HIGH AVAILABILITY IN HETEROGENEOUS CELLULAR NETWORKS VIA SPECTRUM AGGREGATION 10157

(carrier aggregation) [13] is a well-known technique that enables

multiple less reliable links in parallel to boost availability. As

speciﬁed by 3GPP in [14], spectrum aggregation, which enables

the concurrent utilization of multiple component carriers (CCs)

in the physical layer, was originally proposed to increase bit

rates and capacity. With spectrum aggregation, the aggregated

bandwidth as large as 100 MHz can be obtained by aggregating

5 20 MHz CCs, and the propagation characteristics of differ-

ent component carriers may also vary signiﬁcantly. e.g., a CC

in the 800 MHz has very different propagation characteristic

from a CC in the 2.4 GHz. Recently, spectrum aggregation has

been regarded as the primary feature deployed by operators with

commercial LTE-Advanced service [15]. In [16], the spectrum

aggregation was proposed to improve peak data rate in multi-

band HCNs.

The spectrum aggregation has recently been applied to en-

hance the availability. In [17], the spectrum aggregation was

applied to guarantee high availability by a joint transmission

over multiple links over different carrier frequencies. However,

their work was limited to Rayleigh-fading channel. The work

in [17] was extended to [18] by including selection combin-

ing and maximal ratio combining over Nakagami-m fading. It

is revealed in [17] and [18] that it is more beneﬁcial in terms

of power to utilize multiple links in parallel rather than boost-

ing the power of a single link. In [19], combined macro- and

microdiverse uplink connections and composite correlated dis-

tributions of Nakagami fading and log-normal shadowing was

investigated. More recently, an analytical model for availability

in multi-connectivity systems utilizing macro- and microdiver-

sity was studied in [20]. Nevertheless, all of the aforementioned

works have neglected path loss in the availability model or in-

terference in each carrier.

In order to provide the availability for emergency calls, the

priority based schemes has been designed, where network re-

sources are occupied only by these emergency services [21].

Different from emergency services, IoT applications coexist

with traditional data-centric applications, and share the network

resources with each other. Due to the different achievable ca-

pacity of each link and cumulative interference caused by all

the simultaneously transmitting nodes, nearby or faraway, sim-

ply considering the received power from the desired transmitter

may not accurately capture the availability characteristics. A

more appropriate model taking into account the interference

statistics is the signal-to-interference-plus-noise ratio (SINR)

model, which is also the main element determining the shannon

capacity. The SINR model can be widely found in solving the

optimization problem in spectrum allocation [22], power control

[23], load balancing [24] and UE association [25]. Assuming the

shadowing fading as a random variable, [26] studied the high

availability in wireless networks with different transmit power

at the BS based on SINR model. However, modeling and ana-

lyzing the availability in HCNs based on SINR model can be

computationally and analytically challenging.

Resource allocation has been proposed to solve power con-

sumption problem in [27]–[30]. In [27], a power optimization

scheme was proposed for interference-limited wireless commu-

nications. In [28], the energy-efﬁcient spectrum sharing problem

was studied in cognitive radio femtocell networks. In [29], the

BS sleep-mode strategies in HCNs with the small cell deploy-

ment were proposed to minimize the power consumption. In

[30], the resource allocation and UE association was jointly in-

vestigated to ﬁnd the near optimal solution for the minimum

total energy consumption of the cellular system using iterative

algorithm. However, most of existed resource allocation algo-

rithms consider continuous transmit power allocation, which

can not be directly applied to in systems supporting discrete

transmit power allocation. For instance, the 3GPP LTE cellular

networks only support discrete power allocation in the down-

link with a use-speciﬁc data-to-pilot-power offset parameters

[31]. Compared with the continuous power control, the discrete

power control offers two main beneﬁts [32]: (i) the transmitter

is simpliﬁed, and more importantly, (ii) the overhead of in-

formation exchange among networks is signiﬁcantly reduced.

Nevertheless, using simple discretization on the solution ob-

tained by existed continuous power control is not an effective

approach. Discrete power allocation for cellular networks has

been proposed in [32], [33]. In [32], two discrete power control

algorithms were proposed to maximize the weighted system

capacity. In [33], a discrete power control was proposed for

multi-cell networks aiming at energy efﬁciency. However, to

the best of our knowledge, there is no work dealing with the

discrete power control for availability optimization.

Unlike existing works, the aim of this work is to propose a

joint UE association, subcarrier assignment and discrete power

allocation technique to optimize the availability and power con-

sumption via genetic algorithms (GAs) [34] in HCNs. Due to the

advantages in versatility, scalability, and computational simplic-

ity, GAs have become increasingly popular method of solving

combinatorial optimization problems in wireless networks [35]–

[43]. GAs are proposed to solve the problem of antenna selection

for MIMO networks [35], subcarrier pairing and power alloca-

tion for cognitive relay networks [36], channel assignment for

wireless mesh networks [37], [43], channel and bandwidth allo-

cation for mobile cellular networks [38], [39], energy saving for

LTE networks [40], cell deployment for 5G networks [41], and

routing and trafﬁc scheduling for multi-hop cellular networks

[42]. The main contributions of this paper are summarized as

follows:

1) We present an analytical model for availability in HCNs

based on SINR model. Unlike [44] and [45], where a

UE connects to one BS offering the highest instantaneous

SINR, we assume each UE connects to multiple BSs with

arbitrary SINR values simultaneously. This results is a

novel approach to model and analyze availability with

multiple connections.

2) We derive an exact closed-form expression for the avail-

ability of a random UE in HCNs, which is veriﬁed by

Monte Carlo simulation. Its numerical results reveal the

importance of the UE association, the subcarrier as-

signment and the power allocation in achieving high

availability.

3) We formulate two optimization problems with the aims

of maximizing the average availability under the power

budget constraint, and minimizing the average power

10158 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 11, NOVEMBER 2017

TAB LE I

NOTATIONS

Symbol Deﬁnition

KSet of BS tiers

NSet of all the UEs in the network

BSet of all the BSs in the network

BkSet of BSs in tier k

NsSet of UEs associated with the sth BS

MSet of subcarriers at each BS

ρMaximum number of subcarriers that can be aggregated

for each UE due to the hardware constraints

vm

s,n Binary variable indicates if the mth subcarrier of the sth

BS is allocated to the nth UE or not

Pmax

sMaximum transmit power of the sth BS

Pmax

s,m Maximum transmit power at the mth subcarrier of the

sth BS

LMaximum integer level of transmit power

ls,m Power allocation level at the mth subcarrier of the sth

BS

δPower budget ratio at any BS

Hs,n Channel power gain between the sth BS and the nth UE

ds,n Distance between the sth BS and the nth UE

N0Noise power

αqPath loss exponent of the qth band

CqPath loss constant of the qth band

μqWavelength of the qth band

τPredeﬁned SINR threshold

consumption while satisfying the availability requirement.

Due to the complex topology of HCNs, these two opti-

mization problems are NP-hard in nature.

4) We propose to apply GA for the joint UE association,

subcarrier assignment and power allocation problem. The

average availability in spectrum aggregation-based HCNs

improves with decreasing the number of UEs, and in-

creasing the maximum number of aggregated subcarriers

allowed for each UE. The average power consumption

decreases with increasing the maximum number of ag-

gregated subcarriers, and decreasing the number of UEs.

To the best of our knowledge, this is the ﬁrst work of the

availability optimization in spectrum aggregation-based

HCNs using GA.

The remainder of this paper is organized as follows. In

Section II, we present the multi-tier multi-band availability

model. Next, in Section III, we formulate the availability max-

imization problem and the power consumption minimization

problem. Section IV applies GA for the joint UE association,

subcarrier assignment and power allocation problem. Section

V presents the numerical results and Section VI highlights our

conclusions.

II. SYSTEM MODEL AND AVAILABILITY CHARACTERIZATION

A. System Model

We consider HCNs with K={1,...,K}denoting the set

of Ktiers which may include macrocells, picocells, femtocells,

and further radiating elements. In this paper, we focus on the

downlink transmission and assume open access for all the small

cells. We list all the notations in Table I.

We denote the set of UEs as N={1,2,...,N}and the set

of BSs as B=B1∪B

2∪...∪B

K={1,2,...,S}, where Bk

represents the set of BSs in tier k. To achieve high availability via

multiple link connections, each UE is allowed to be connected

with multiple BSs simultaneously. We assume the massive non-

continuous carrier aggregation [46] is applied, where UEs can

aggregate a large number of (up to 32) continuous and non-

continuous subcarriers from heterogeneous spectrum bands. We

denote the set of UEs associated with the sth BS as Ns, and

thus N=N1∪N

2∪...∪N

S. We assume that each BS has

maximum Qavailable bands (e.g., 800 MHz, 2.5 GHz, . . . ),

and each band contains Fsubcarriers. We denote the set of bands

in each BS as Q={1,2,...,Q}, and the set of subcarriers at

each BS as M={1,...,F

band1

,...,(Q−1)F+1,...,QF

bandQ

}.

We assume that the maximum subcarrier transmit power at

the mth subcarrier of the sth BS is Pmax

s,m , and the maximum

transmit power of the sth BS is Pmax

s. We consider the discrete

power allocation at the mth subcarrier of the sth BS with integer

level ls,m, where

ls,m ∈[1,L]If UE occupied mth subcarrier of sth BS

=0 If no UE occupied mth subcarrier of sth BS,

(1)

and Lis the maximum integer level. Thus, the transmit power

allocated to each subcarrier of a BS belongs to the set [0,1

LPmax

s,m ,

2

LPmax

s,m ,··· ,ls,m

LPmax

s,m ,··· ,Pmax

s,m ].

To specify the UE association and the subcarrier assignment,

we denote vm

s,n as the resource-allocation indicator, which is a

binary variable. If vm

s,n =1, it indicates that the mth subcarrier

of the sth BS (s∈B)is allocated to the nth UE (n∈N), and

vm

s,n =0(m∈M)if otherwise.

We assume the following resource assignment constraint, sub-

carrier aggregation constraint, and per-BS power constraint need

to be satisﬁed:

1) The variable vm

s,n must satisfy that each subcarrier for a

BS can only be occupied by at most one UE.

2) The total number of aggregated subcarriers for each UE

should be at most ρdue to hardware constraints.

3) The total power consumption at each BS over all its sub-

carriers m∈M

ls,m

LPmax

s,m should not exceed a power bud-

get δPmax

swith the power budget ratio δ.

We use different path loss exponents for different bands to

capture the possible large differences in propagation characteris-

tics associated with each band’s carrier frequency. We formulate

the SINR of the nth UE associated with the mth subcarrier of

the sth BS as

SINRm

s,n =

ls,m

LPmax

s,m Hs,nCqd−αq

s,n vm

s,n

i∈B\s

li,m

LPmax

i,m Hi,nCqd−αq

i,n

Im

s,n

+N0

,(2)

where q=m/F , and · is the ceiling function. For instance,

if m=15, F=10, we have q=2. In (2), Im

s,n is the aggregate

interference at the nth UE from all the other BSs over the mth

subcarrier, Hs,n is the channel power gain between the sth BS

and the nth UE, ds,n is the distance between the sth BS and the

nth UE, N0is the noise power, αqis the path loss exponent of the

JIA et al.: ACHIEVING HIGH AVAILABILITY IN HETEROGENEOUS CELLULAR NETWORKS VIA SPECTRUM AGGREGATION 10159

qth band, and Cqis the path loss constant depending strongly on

carrier frequency with Cq=(

μq

4π)2for the wavelength μq.Sim-

ilar as [16], [47]–[49], we ignore shadowing and only consider

independent quasistatic Rayleigh fading with Hi,n ∼exp(1)

for simplicity. The extension to take the shadowing into account

or Rician fading can be incorporated in the availability analysis

in Section II via some mathematical manipulations, remind that

the GAs proposed in this work will be still valid.

B. Availability Analysis

The signal cannot be successfully received if the SINR value

SINRm

s,n is below a certain threshold τ. Therefore, the outage

probability of the nth UE associated with the mth subcarrier of

the sth BS is characterized as

Om

s,n =PSINRm

s,n ≤τ.(3)

Thus the availability of the nth UE associated with the mth

subcarrier of the sth BS can be derived as

Am

s,n =1−Om

s,n =1−PSINRm

s,n ≤τ

=PSINRm

s,n >τ

.(4)

Generally, Am

s,n denotes the availability of a single connection

between UE nwith an arbitrary BS sover subcarrier m, and

Am

s,n is given in the form of 1 −10−x, where xindicates the

number of nines. Considering that UE nmay connect multiple

BSs over multiple connections, its availability is deﬁned by

the combination of multiple connection availabilities, which is

derived in the following theorem.

Theorem 1: The availability of the nth UE connected to mul-

tiple BSs in HCNs is derived as

An=1−

s∈B,m∈M 1−Am

s,n,∀n∈N,(5)

where the availability of the nth UE associated with the mth

subcarrier of the sth BS is given by

Am

s,n

=⎧

⎪

⎪

⎪

⎪

⎪

⎨

⎪

⎪

⎪

⎪

⎪

⎩

0if vm

s,n =0

exp (−ΘsτN0)ifvm

s,n =1,Im

s,n =0

S

i=1ΘiS

j=1,j=s

exp(−ΘsτN0)

Θs(Θj+Θsτ)S

k=1,k=s,j (Θk−Θj)

if vm

s,n =1,Im

s,n =0,

(6)

where

Θξ=L/(lξ,mPmax

ξ,m Cqd−αq

ξ,n ),(7)

and ξcan be s,i,j, and k.

Proof: For vm

s,n =0, we can directly obtain Am

s,n =0.

For vm

s,n =1 with no interference (Im

s,n =0), we present

Am

s,n as

Am

s,n =PSINRm

s,n >τ

=1−Pls,m

LPmax

s,m Hs,nCqd−αq

s,n ≤τN0

(a)

=exp(−ΘsτN0),(8)

where Θsis given by (7), and (a)is performed based on Hs,n ∼

exp(1).

For vm

s,n =1 and Im

s,n =0, we employ the change of variables

X=Im

s,n +N0,Y=ls,m

LPmax

s,m Hs,nCqd−αq

s,n , and Z=Y/X to

obtain

Am

s,n =P(Z>τ)

=∞

τ

fZ(z)dz

=∞

τ∞

0

xfX(x)fY(xz)dxdz. (9)

We have

fY(xz)=Θ

sexp (−Θsxz),(10)

where Θsis given by (7).

Next, we focus on computing fX(x)with X=Im

s,n +N0

and

Im

s,n =

i∈B\s

Ωi,(11)

where

Ωi=li,m

LPmax

i,m Hi,nCqd−αq

i,n .(12)

According to the distribution of channel power gain, we derive

fΩi(x)=Θ

iexp (−Θix),(13)

where Θiis given by (7).

In order to obtain the probability density function (PDF) of the

sum of independent exponential random variables i∈B\sΩi,

we apply the following lemma [50].

Lemma 1: Let (Wi)i=1...n ,n≥2, be the independent expo-

nential random variables with pairwise distinct respective pa-

rameters Θi, the PDF of their sum is given as

fW1+W2+...+Wn(w)=

n

i=1

Θin

j=1

e−Θjw

n

k=1,k=j(Θk−Θj).

(14)

Based on Lemma 1, we derive the PDF of Xas

fX(x)=fIm

s,n (x−N0)

=

S

i=1,i=s

Θi

S

j=1,j=s

eΘjN0

S

k=1,k=s,j (Θk−Θj)e−Θjx,

(15)

where Θi,Θj, and Θkcan be obtained by using (7).

10160 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 11, NOVEMBER 2017

Substituting (10) and (15) into (9), we obtain

Am

s,n

=∞

τ∞

N0

x

S

i=1,i=s

Θi

S

j=1,j=s

eΘjN0e−ΘjxΘse−Θsxz

S

k=1,k=s,j (Θk−Θj)dxdz

=

S

i=1

Θi

S

j=1,j=s

eΘjN0

S

k=1,k=s,j (Θk−Θj)Z(τ,N0,Θj+Θ

sz).

(16)

To solve (16), we derive Z(τ,N0,Θj+Θ

sz)as

Z(τ,N0,Θj+Θ

sz)

=∞

τ∞

N0

xe−(Θj+Θsz)xdxdz

=∞

τN0e−N0(Θj+Θsz)

(Θj+Θ

sz)+e−N0(Θj+Θsz)

(Θj+λsz)2dz.

(b)

=∞

N0(Θj+Θsτ)N0

Θs

e−u

u+N0

Θs

e−u

u2du

=e−N0(Θj+Θsτ)

Θs(Θj+Θ

sτ),(17)

where (b) is performed by using u=N0(Θj+Θ

sz).

Substituting (17) into (16), we ﬁnally derive Am

s,n as

Am

s,n =

S

i=1

Θi

S

j=1,j=s

e−N0Θsτ

Θs(Θj+Θ

sτ)S

k=1,k=s,j (Θk−Θj),

(18)

where Θs,Θi,Θj, and Θkcan be obtained by using (7).

Note that the derived availability of an arbitrary UE in spec-

trum aggregation-based HCNs is a easy-to-evaluate closed-form

expression. Based on this expression, each UE connects to sev-

eral BSs, which enables the optimal solution of the proposed

availability optimization and power consumption optimization

problem. In other words, the connection between each UE and

the BSs is decided to achieve the optimal overall network per-

formance.

It should be observed that the availability deﬁned in (5) is dif-

ferent from that of reliability. According to [51], reliability refers

to the probability to guarantee a required function/performance

under stated conditions within a given time latency, and the spe-

ciﬁc reliability requirements differ for various types of services

and applications. While availability is a transport-agnostic def-

inition from the applications point, and showcases the presence

or absence of reliability [52].

Due to the fact that wireless communication systems are typ-

ically not designed to provide a reliable level at all times and

in every reception scenario, this would harm the acceptance of

ultra reliable communication (URC) services and restrict their

usage. Our availability measurement is also different from tra-

ditional methods, where the availability can be calculated by

measuring the ping non-responses and interpolating differences

in time between down link alert and uplink alert during months

[53]. With the help of availability deﬁnition and evaluation in

Fig. 1. Single link availability.

Fig. 2. Multiple links availability.

(5), we can quickly evaluate the availability under given con-

ditions, and ﬁnd those factors inﬂuencing current availability.

Thus, the URC services can be quickly deployed in a wide range

of scenarios by just considering whether the obtained availabil-

ity meets its requirement [52].

C. Availability Validation

To verify the derived analytical results for the availability,

we plot the analytical curves for the single link availability

and the multiple link availability using (18) and (5) with the

simulation curves using Monte Carlo simulation in Figs. 1 and

2, respectively. In these two ﬁgures, we assume the macro BS

with Pmax

1,m =43 dBm and all the pico BSs with Pmax

j,m =30 dBm

for any subcarrier (j=1) for two-tier HCNs, where the distance

between the UE and the sth BS is randomly generated. Both

ﬁgures showcase that the derived analytical results match well

with the simulation, which proves the accuracy of our derived

results.

JIA et al.: ACHIEVING HIGH AVAILABILITY IN HETEROGENEOUS CELLULAR NETWORKS VIA SPECTRUM AGGREGATION 10161

Fig. 1 plots the single link availability versus the power al-

location level l1,m at the macro BS in two-tier HCNs. We set

Cq=(

0.375

4π)2for all band q. The power allocation level lj,m at

the pico BSs is equal to L, which indicates the full power allo-

cation at each pico BSs. As expected, the single link availability

of UE connected to the macro BS increases with increasing the

transmit power of macro BS. Increasing the number of BSs in

HCNs increases the interference, which degrades the single link

availability. Importantly, the single link availability is very low,

and can hardly achieve the availability with six nines, which

reveals the potential of improving the availability via multiple

links.

In Fig. 2, we assume that the number of subcarriers at each BS

is M=2 with Cq1=(

0.375

4π)2, and Cq2=(

0.125

4π)2, respectively.

By comparing Figs. 2 with 1, we see that the availability of

a UE connected with multiple links substantially outperforms

that with single link, which reveals the need to apply spectrum

aggregation technique. We can also see that the multiple link

availability decreases with increasing the transmit power, and

the highest availability of a UE achieved for the lowest power

allocation level L=1 and the minimum number of BSs S=2

reveals the importance of joint optimization on power allocation,

UE association and subcarrier assignment in multi-tier multi-

band HCNs.

III. PROBLEM FORMULATION

Next, We formulate two optimization problems to achieve

the maximum average availability, and to achieve minimum

power consumption in spectrum aggregation-based HCNs, re-

spectively.

Availability Maximization Problem: Network aggregate util-

ity is conventionally regarded as a measure for evaluating

the performance of resource management protocols [54]–[56].

Based on this criterion, the objective of this problem is to maxi-

mize the average availability over all the UEs. Here, the average

availability is the sum of availability of all UEs averaging over

the total number of UEs as shown in (19). This can be achieved

by searching the optimal UE association, subcarrier assignment,

and discrete power allocation under the total power consump-

tion constraint. This availability maximization problem is for-

mulated as

max n∈N An

N(19)

s.t.

n∈N

vm

s,n ≤1,∀s∈B,∀m∈M,(20)

s∈B

m∈M

vm

s,n ≤ρ, ∀n∈N,(21)

ls,m ≤L, ∀s∈B,∀m∈M,(22)

m∈M

ls,m

Pmax

s,m

L≤δPmax

s,∀s∈B.(23)

The constraints in (20)–(23) are named as the UE association

and subcarrier assignment constraint in (20), the subcarrier ag-

gregation constraint in (21), the power level constraint in (22)

and the per-BS power constraint in (23). The subcarrier as-

signment and UE association constraint in (20) represents that

each subcarrier of each BS can be allocated to at most one UE.

The subcarrier aggregation constraint in (21) implies that the

maximum number of aggregated subcarriers must satisfy the

hardware constraints. The power level constraint in (22) repre-

sents that the maximum discrete transmit power level of each

subcarrier is L.Theper-BS power constraint in (23) represents

that the maximum transmit power at each BS is limited by its

total power budget.

Power Consumption Minimization Problem: The objective

of the problem is to minimize the average power consumption

while satisfying each UE’s availability requirement, which is

formulated as

min s∈B m∈M

ls,m

LPmax

s,m

N(24)

s.t.

n∈N

vm

s,n ≤1,∀s∈B,∀m∈M,(25)

s∈B

m∈M

vm

s,n ≤ρ, ∀n∈N,(26)

ls,m ≤L, ∀s∈B,∀m∈M,(27)

m∈M

ls,m

Pmax

s,m

L≤δPmax

s,∀s∈B,(28)

1−

s∈B,m∈M 1−Am

s,n≥Ath ,∀n∈N.(29)

Note that the constraints of (25)–(28) are the same as (20)–

(23) in the availability maximization problem, while the per-UE

availability requirement in (29) represents that the availability

requirement for each UE should be satisﬁed.

Instinctively, both of these two optimization problems are in

the form of mixed integer non-linear programming (MINLP)

problem, which are generally NP-hard and cannot be solved by

traditional optimization methods [30]. In the next section, we

will develop the bio-inspired GA to solve these two optimization

problems.

IV. GENETIC ALGORITHM APPROACH

For these above MINLP problems, a straightforward solu-

tion is to conduct an exhaustive search by testing all feasible

channel and power allocation vectors vm

s,n and ls,m . This ap-

proach, however, is infeasible for networks with larger number

of BSs and UEs. Some other algorithms, such as those in [30],

[57], are based on decomposition. In their algorithm, the near-

optimal subcarrier assignment and UE association is determined

ﬁrst via heuristic algorithm under ﬁxed power allocation, and

the optimal or near-optimal power allocation is obtained via

Lagrangian dual based method or iterative heuristic approach

with the predetermined optimal subcarrier assignment. How-

ever, their approach may be suboptimal due to the fact that the

subcarrier assignment and power allocation are interacting with

each other, and the subcarrier assignment and power allocation

should be optimized in a compact form [58]. Therefore, we

10162 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 11, NOVEMBER 2017

apply GA to integrate these two steps to achieve the interaction

between the subcarrier assignment and power allocation.

By simulating the process of evolution in the natural system,

GA can be considered as an adaptive heuristic search algorithms,

and is very suitable to provide a robust, near optimal solution

for many real world NP-hard problems, such as BS placement

optimization for LTE heterogeneous networks [59], channel as-

signment for wireless mesh networks [43]. GA is inherently an

evolutionary process that involves individual encoding, selec-

tion, crossover, mutation, and replacement operations [34].

A. Individual Encoding

GA cannot deal with the solutions of the optimization problem

directly. The solutions needs to be represented as chromosomes

in terms of data structure. In our optimization problems, an

integer-based encoding scheme reﬂecting the UE association,

the subcarrier assignment, and the power allocation, is proposed

to represent the potential solutions.

We ﬁrst generate an initial population Rwith Rindividu-

als, and each individual consists of two integer-based matri-

ces, which are the potential solutions of the considered opti-

mization problem. These matrices are generated according to

Algorithm 1 in order to satisfy the UE association and subcar-

rier assignment constraint, the subcarrier aggregation constraint,

the power level constraint, and the per-BS power constraint dur-

ing initialization to accelerate the convergence process. We rep-

resent the two integer-based matrices in the rth individual as

follows (1≤r≤R):

1) UE association and subcarrier assignment matrix Γris

Γr=⎡

⎢

⎢

⎢

⎢

⎢

⎣

γr

1,1,··· ,γ

r

1,M

γr

2,1,··· ,γ

r

2,M

.

.

..

.

..

.

.

γr

S,1,··· ,γ

r

S,M

⎤

⎥

⎥

⎥

⎥

⎥

⎦

,(30)

where the matrix element γr

s,m (1≤s≤S, 1≤m≤M)indi-

cates the γr

s,m th UE associated with the mth subcarrier of the

sth BS. For instance, γr

s,m =nindicates the nth UE associated

with the mth subcarrier of the sth BS, thus vm

s,n =1; γr

s,m =0

indicates no UE associated with the mth subcarrier of the sth

BS, thus n∈N vm

s,n =0.

Note that this matrix always satisﬁes the subcarrier assign-

ment and UE association constraint. According to the population

initialization in Algorithm 1, we count the number of subcarri-

ers assigned to the nth UE cnto ensure that cnis no larger than

the subcarrier aggregation constraint ρ.Ifcn>ρ,thenth UE

will become infeasible and be excluded from the set of feasible

UEs Nfeasible .

2) Power allocation matrix Lris

Lr=⎡

⎢

⎢

⎢

⎢

⎢

⎣

lr

1,1,··· ,l

r

1,M

lr

2,1,··· ,l

r

2,M

.

.

..

.

..

.

.

lr

S,1,··· ,l

r

S,M

⎤

⎥

⎥

⎥

⎥

⎥

⎦

,(31)

where lr

s,m represents the power level allocated to the mth sub-

carrier of the sth BS.

To satisfy the per-BS power constraint, the matrix element

lr

s,m is initialized in sequence with increasing m. According

to Algorithm 1, we compare the maximum subcarrier trans-

mit power Pmax

s,m with the remaining power premain

sat each BS,

where premain

s=δPmax

s−passign

s, with passign

srepresenting the

power allocated for the sth BS. If premain

s≥Pmax

s,m , the trans-

mit power allocated to the mth subcarrier can be randomly

selected from [1,L], thus lr

s,m =randi(L). Otherwise we set

lr

s,m =randi(L

Pmax

s,m premain

s), to guarantee that the assigned

power cannot be larger than the maximum transmit power at

each BS, where · is the ceiling function.

One example of encoding scheme is illustrated in Fig. 3 with

4 BSs and 6 UEs deployed in HCNs, where each BS has 3 sub-

carriers and each UE can associate at most 2 subcarriers. We

set the maximum transmit power at each BS Pmax

s=40 W,

the maximum transmit power at each subcarrier Pmax

s,m =16 W,

and the maximum power level L=16. For instance, γ3,1=5

and l3,1=9 indicates that the power level allocated by the 1st

BS at the 3rd subcarrier to the 5th UE is 9. It can be also ob-

served that this encoding scheme meets all the constraints except

JIA et al.: ACHIEVING HIGH AVAILABILITY IN HETEROGENEOUS CELLULAR NETWORKS VIA SPECTRUM AGGREGATION 10163

Fig. 3. Individual encoding scheme.

the per-UE availability requirement of the power consumption

minimization, which will be satisﬁed in the following selection

process.

B. Fitness Functions and Natural Selection

In GA, selection operation is applied to choose individuals to

participate in reproduction, which has a signiﬁcant impact on

driving the search towards a promising trend and ﬁnding optimal

solutions in a short time. We adopt the famous roulette wheel

selection method to select the individual based on its selection

probability, which is proportional to its ﬁtness function. The

selection probability of the rth individual is deﬁned as

qr=f(r)

r∈R f(r),(32)

where f(r)is the ﬁtness function of individual r. The quality of

the individual is judged by this ﬁtness function.

For the availability maximization problem, since all the con-

straints are satisﬁed during initialization, we directly take the

objective function as the ﬁtness function, which is given by

fI(r)=n∈N An

N.(33)

For the power consumption minimization problem, the ﬁt-

ness function is deﬁned by taking the average network power

consumption and a penalty function determined by the relative

degree of infeasibility. To provide an efﬁcient search and ensure

that the ﬁnal best solution is feasible, the penalty method [60]

is adopted to deal with the availability constraint. The ﬁtness

Fig. 4. Two-point crossover and individual repair.

function is expressed as

fII(r)=−s∈B m∈M

ls,m

LPmax

s,m

N

+

n∈N

αnmax (Ath −An,0),(34)

where αnrepresents the penalty coefﬁcient determined by the

per-UE availability requirement. This transforms the power con-

sumption minimization problem to a maximization problem.

C. Crossover and Mutation

The crossover operation is used to mix between the individu-

als to increase their ﬁtness. In this paper, two-point crossover is

performed to produce new solutions. In order to avoid violating

the per-BS power constraint, we limit the crossover operation

between arbitrary row of the matrices of one individual and that

of another individual. Every elements between the two points

are switched between two parent individuals to produce two

child individuals. The subcarrier aggregation constraint may be

violated after crossover operation, thus some elements of UE as-

sociation and subcarrier assignment matrix need to be repaired

by allocating to other UEs.

We illustrate an example of two-point crossover and individ-

ual repair operation in Fig. 4, the parameters setting of which

is the same as that of Fig. 3, and the randomly generated two

crossover points are c1=1 and c2=3. The crossover between

parent Aand parent Bis performed by switching the rows of

the 1th BS and the 4th BS in both matrices of parent Awith

10164 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 11, NOVEMBER 2017

that of parent B. After crossover, the assigned subcarriers for

the 2th UE and the 4th UE violate the subcarrier aggregation

constraint ρ=2 in child A. As such, we repair γ1,3and γ2,2in

child Ausing randomly generated number 5 and 1 to obtain a

repaired child A.

In the mutation operation, the elements in both matrices of

each individual are randomly altered to diversify the population

after the crossover operation, which will pave the way towards

global optima. 1) For the mutation occurring at the arbitrary

element of the UE association and subcarrier assignment ma-

trix, repair operation may be required to satisfy the subcarrier

aggregation constraint to speed up the convergence; 2) For the

mutation occurring at the arbitrary element lr

s,m of the power

allocation matrix, mutation operation will be performed using

lr

s,m = randi

⎛

⎝⎡

⎢

⎢

⎢

min ⎛

⎝Pmax

s−

M

i=1,i=m

ls,i

Pmax

s,i

L,Pmax

s,m ⎞

⎠L

Pmax

s,m ⎤

⎥

⎥

⎥⎞

⎠,

(35)

where · is the ceiling function.

D. Replacement

After generating a new population through the crossover and

mutation operators, an elitist model based replacement is em-

ployed to update a certain number of individuals in the old

population with the new generated individuals. The low quality

individuals with the low ﬁtness values in the parental population

are replaced by their children in the next generation.

Now, we have designed the key components of the GA oper-

ation, which are the individual encoding, population initializa-

tion, selection, crossover, mutation, and replacement operation.

The joint optimization of UE association, subcarrier assignment

and power allocation based on GA is depicted in Algorithm 2,

where Gis the given number of generations, Ris the popula-

tion size, qcis the crossover probability, and qmis the mutation

probability.

In the proposed GA-based optimization, the computational

complexity is dominated by the complexity in evaluating the

objective function in (33) or (34), which has to be evaluated

Rtimes in each iteration. For the availability maximization

problem, with the number of subcarriers as Mand the number of

UEs as N, the time complexity in calculating the ﬁtness function

of the average availability in (33) is O(MNR)within a iteration.

For the power consumption minimization problem, with the

number of subcarriers as M, the number of UEs as N, and

the number of BSs as S, the time complexity in calculating the

ﬁtness function of the power consumption in (34) is O(R(MS +

MN)) within a iteration.

Apart from this, a GA-based approach also depends on other

factors, which are difﬁcult to clearly enumerate, such as strate-

gies to generate new population, and the tolerance allowable

for cumulative changes in ﬁtness values [61]. Excluding these

parameters, the total complexity of our algorithm in solving the

availability maximization problem and the power consumption

TAB LE I I

SIMULATION PARAMETERS

Parameter Value

The number of macro BS 1

The number of pico BS 9

The number of UEs N2∼20

Maximum transmit power of macro BS 46 dBm (40 W)

Maximum transmit power of pico BS 30 dBm (1 W)

Maximum aggregated subcarriers per UE 1 ∼10

The availability threshold Ath 1−10−6(six nines)

800MHz band’s wavelength μ10.375 m

2.5GHz band’s wavelength μ20.125 m

800MHz band’s path loss exponent α13

2.5GHz band’s path loss exponent α24

The number of subcarriers in each band 10

Maximum integer power level L1∼32

Maximum subcarrier transmit power of macro BS (40/10)W

Maximum subcarrier transmit power of pico BS (1/10)W

Noise PSD −174 dBm

SINR threshold τ1

Population size 20

Crossover probability 0.95

Mutation probability 0.005

Maximum generation 2000

minimization problem are O(G(MNR+R2)) and O(G

(MSR+MNR +R2)), respectively.

V. N UMERICAL RESULTS

In this section, we provide numerical results to illustrate the

performance of our proposed algorithm. We consider spectrum

JIA et al.: ACHIEVING HIGH AVAILABILITY IN HETEROGENEOUS CELLULAR NETWORKS VIA SPECTRUM AGGREGATION 10165

Fig. 5. (a) Convergence behavior of the availability maximization problem. (b) Convergence behavior of the power consumption minimization problem.

aggregation-based HCNs consisting of 2 tiers (macro and pico)

with 2 bands (800 MHZ and 2.5 GHZ). The set-up is a circle

area with size (π5002)m

2, where the macro BS is located at the

center, the pico BSs and UEs are randomly distributed in this

circle area. The details of parameters are summarized in Table II

unless otherwise speciﬁed. The corresponding simulations are

implemented in Matlab 7 in a laptop with Intel (i5-4300) CPU.

All the results are obtained by averaging 100 simulations.

A. Convergence Behavior

In GA, the convergence behavior is affected by many control

parameters, such as the initial population, mutation probability,

crossover mechanism, etc.. To the best of our knowledge, the

conditions for GAs to converge have been proved only for the

binary encoding with Markov chain models [62]. However, for

the GA algorithm with integer or real encoding, the convergence

is still an open problem [39]. In this paper, instead of using

an analytical approach, extensive simulations are employed to

look at the convergence issue. In our simulations, we set the

maximum number of generation as 2000. Actually, the number

of generations depends on the number of size of individuals.

For instance, more generations are needed for a larger number

of UEs or number of subcarriers.

Fig. 5(a) plots the convergence behavior of the availabil-

ity maximization problem with the maximum number of ag-

gregated subcarriers ρ=5, and the power budget ratio δ=1.

Fig. 5(b) plots the convergence behavior of the power consump-

tion minimization problem with the availability threshold of

6 nines (Ath =1−10−6), and ρ=5. From Fig. 5(a) and (b),

we can observe that the algorithm converge after approximately

500 number of generations for various number of UEs. It takes

20 seconds to converge for N=10 HCNs. This is sufﬁcient for

many applications. If we use a more powerful computer, it is

expected that it can converge much faster.

For the availability maximization problem, the average avail-

ability with random allocation at the initialization is 0.564944,

while the ﬁnal average availability after optimization with GA

is 0.999859, which showcase that the GA achieves nearly 50%

more average availability compared with that of the random

TABLE III

OPTIMIZED AVERAGE AVAILABILITY VALUE

N4 8 12 16 20

Availability by GA 10 nines 7 nines 5 nines 3 nines 3 nines

Optima 11 nines 7 nines 5 nines 3 nines 3 nines

TAB LE I V

OPTIMIZED POWER CONSUMPTION VALUE

N4 8 12 16 20

Fitness 0.009 0.023 0.138 0.771 3.448

Availability 6 nines 6 nines 5 nines 3 nines 3 nines

APC (W) 0.009 0.023 0.042 0.087 0.214

OPC (W) 0.009 0.021 0.038 0.079 0.193

resource allocation. For the power consumption minimization

problem, the GA achieves a huge decrease of ﬁtness value dur-

ing evolution, this can be explained by the fact that the random

resource allocation cannot satisfy the per-UE availability re-

quirement, thus a large penalty value is introduced in the ﬁtness

function in (34). Additionally, it is revealed that the converge

speed can be substantially increased with reduced number of

UEs in HCNs.

We then present the optimized average availability, and the

optimized power consumption with corresponding achieved av-

erage availability for various number of UEs in Tables III and

IV, where APC means the average power consumption. Ad-

ditionally, we present the optimal availability and power con-

sumption that based on brute force approach, where OPC means

the optimal power consumption. In both Tables, we see that the

availability of 6 nines can be achieved when the number of UEs

is less than 8. In Table IV, due to the availability of 6 nines

requirement is satisﬁed for N=4 and N=8, no penalty value

is introduced to the ﬁtness value, and results in equal value

as the power consumption. However, the violation of per-UE

availability requirement (6 nines) for N=12, 16, and 20 re-

sults in the added penalty values as shown in the ﬁtness values.

10166 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 11, NOVEMBER 2017

Fig. 6. (a) Average availability versus the number of UEs. (b) Average power consumption versus the number of UEs.

Fig. 7. (a) Average availability versus different power levels. (b) Average power consumption versus different power levels.

We also observe that the optimized value based on GA closely

approaches the optima obtained by brute force approach, which

showcases the effective of GA for availability maximization or

power consumption minimization.

B. Impact of the Number of UEs and the Subcarrier

Aggregation Constraint

Fig. 6(a) plots the average availability versus the number of

UEs for various subcarrier aggregation constraint ρ. We ob-

serve that the average availability decreases with increasing the

number of UEs. This can be explained by the fact that the trans-

mit power allocated to the UE decreases and the interference

from the same subcarrier at other BSs increases with increasing

the number of UEs. More importantly, the average availability

can be improved by relaxing the maximum number of aggre-

gated subcarriers. For the availability maximization problem, we

can observe that the substantial improvement of average avail-

ability is achieved from single subcarrier constraint to three

aggregated subcarriers constraint, however further increasing

the maximum number of aggregated subcarriers can not achieve

much improvement. This indicates that increasing the maximum

number of aggregated subcarriers may not guarantee substantial

improvement of average availability.

Fig. 6(b) plots the optimized average power consumption ver-

sus the number of UEs for various subcarrier aggregation con-

straint ρ. Due to the increased per-subcarrier interference with

increasing the number of UEs, the average power consumption

increases with increasing the number of UEs. Another important

observation is that utilizing multiple connections can be an efﬁ-

cient way to save power and improve availability. For instance,

for HCNs with 9 UEs fulﬁlling the availability requirement,

the average power consumption with ρ=4 is around 0.059 W,

whereas that with ρ=9 is around 0.023 W.

C. Impact of the Maximum Power Levels and Power Budget

Ratio

Fig. 7(a) plots the average availability versus the maximum

power levels for various number of UEs. It is shown that the av-

erage availability increases with increasing the maximum power

levels for the same number of UEs. And the achieved availability

is much larger than that with on power control (L=1), which

showcases the importance of discrete power control. However,

JIA et al.: ACHIEVING HIGH AVAILABILITY IN HETEROGENEOUS CELLULAR NETWORKS VIA SPECTRUM AGGREGATION 10167

Fig. 8. (a) Average availability of 10 UEs versus different power budget ratios. (b) Average availability of 20 UEs versus different power budget ratios.

Fig. 9. Average power consumption of 4 UEs.

the average availability of 6 nines is not achievable in HCNs

with N=16 or 20 UEs even with L=32, which means that

increasing Lcan not guarantee substantial improvement in the

average availability. Fig 7(b) plots the power consumption ver-

sus different Lfor different number of users N. We see that

average power consumption decreases with increasing L, espe-

cially for Nis larger. However, when Nis small, increasing

Lcan not guarantee substantial improvement in minimizing

average power consumption.

Fig. 8(a) and (b) plot the average availability versus different

power budget ratio δfor different ρ. It is shown that the average

availability increases with increasing δfor the same ρ, which

results from the increased received power. The six nines of

average availability can be achieved for HCNs with 10 UEs for

ρ=5∼9 and δ=1, these availability values are sufﬁcient for

the requirement of many real-time applications. However, the

average availability of 6 nines is not achievable in HCNs with

20 UEs even with δ=1 and ρ=9. Similar as the observation

in Fig. 6(b), increasing the maximum number of aggregated

subcarriers can not guarantee substantial improvement in the

average availability.

D. Impact of the Maximum Number of Aggregated Subcarriers

Fig. 9 plots the average power consumption versus different

maximum number of aggregated subcarriers ρfor various avail-

ability threshold Ath with N=4 UEs. We see that in order

to achieve higher per-UE availability requirement, more num-

ber of allowed aggregated subcarriers is needed. It is revealed

that the average power consumption decreases with increasing

the maximum number of aggregated subcarriers. The higher

per-UE availability requirement results in higher average power

consumption.

VI. CONCLUSION

In this paper, we have presented the theoretical model and

optimization algorithm to achieve high availability in spectrum

aggregation-based HCNs. We have developed a novel availabil-

ity model under the SINR model. We have also derived a closed-

form expression for the availability in spectrum aggregation-

based HCNs. We have formulated two optimization problems

to maximize the average availability and minimize the aver-

age power consumption. To solve the non-convex optimization

problems, we have proposed an efﬁcient GA-based algorithm

for the joint optimization of the UE association, the subcar-

rier assignment, and the power allocation. The average avail-

ability in spectrum aggregation-based HCNs can be improved

by decreasing the number of UEs as well as increasing the

power budget ratio. Increasing the maximum number of ag-

gregated subcarriers decreases the average power consumption,

but can not guarantee the substantial improvement of average

availability.

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Jie Jia received the Ph.D. degree in computer sci-

ence and technology in 2009 from the Northeastern

University, Shenyang, China, where she is currently

an Associate Professor. In 2016, she was a Visiting

Research Associate at the King’s College London.

She is a member of various international societies,

such as the IEEE and China Computer Federation.

She has published more than 100 technical papers

on various aspects of wireless networks. Her current

research mainly includes HetNets, IoT, and cognitive

radio networks.

Yansha Deng (S’13–M’17) received the Ph.D. de-

gree in electrical engineering from the Queen Mary

University of London, London, U.K., in 2015. From

2015 to 2017, she was a Postdoctoral Research

Fellow in the Department of Informatics, King’s Col-

lege London, London, U.K., where she is currently

a Lecturer. Her research interests include massive

MIMO, HetNets, molecular communication, cogni-

tive radio, cooperative networks, and physical layer

security. She received the Best Paper Award in IEEE

International Conference on Communications 2016.

She is currently an Editor of the IEEE COMMUNICATIONS LETTERS. She has also

served as a TPC member for many IEEE conferences, such as IEEE GLOBE-

COM and IEEE ICC.

Jian Chen received the Ph.D. degree in computer sci-

ence and technology in 2010 from the Northeastern

University, Shenyang, China, where he is currently

an Associate Professor. He is also a Senior Soft-

ware Engineer at the Neusoft Corporation, Shenyang,

China. In 2016, he was a Visiting Research Associate

at King’s College London. His research interests in-

clude D2D communication, location technology, net-

work management, and signal and image processing.

Abdol Hamid Aghvami (M’89–SM’91–F’05) is cur-

rently a Professor of telecommunications engineer-

ing at King’s College London, London, U.K. He

joined the academic staff at King’s College London

in 1984. In 1989, he was promoted to a Reader, and

in 1993 was promoted to a Professor in telecommu-

nications engineering. He is the founder of the Centre

for Telecommunications Research at King’s College

London. He was the Director of the centre from 1994

to 2014.

He carries out consulting work on digital radio

communications systems for British and international companies. He has pub-

lished more than 560 technical journal and conference papers, ﬁled 30 patents,

and given invited talks and courses the world over on various aspects of mobile

radio communications. He was a Visiting Professor at the NTT Radio Com-

munication Systems Laboratories in 1990, a Senior Research Fellow at the BT

Laboratories during 1998–1999, and an Executive Advisor to the Wireless Fa-

cilities Inc., USA, during 1996–2002. He is the Chairman of Advanced Wireless

Technology Group Ltd. He is also the Managing Director of the Wireless Multi-

media Communications Ltd, London, U.K., his own consultancy company. He

is also the Founder of the International Symposium on Personal Indoor and Mo-

bile Radio Communications, a major yearly conference attracting some 1000

attendees.

Prof. Aghvami received the IEEE Technical Committee on Personal Commu-

nications Recognition Award in 2005 for his outstanding technical contributions

to the communications ﬁeld, and for his service to the scientiﬁc and engineering

communities. He is a Fellow of the Royal Academy of Engineering and a Fellow

of the IET. In 2009, he received a fellowship from the Wireless World Research

Forum in recognition of his personal contributions to the wireless world, and for

his research achievements as the Director of the Centre for Telecommunications

Research, King’s College London.

Arumugam Nallanathan (S’97–M’00–SM’05–

F’17) has been a Professor of wireless communi-

cations in the School of Electronic Engineering and

Computer Science, Queen Mary University of Lon-

don, London, U.K, since September 2017. From De-

cember 2007 to August 2017, he was with the Depart-

ment of Informatics, King’s College London, where

he was a Professor of wireless communications from

April 2013 to August 2017. He was an Assistant Pro-

fessor in the Department of Electrical and Computer

Engineering, National University of Singapore, from

August 2000 to December 2007. He has published more than 350 technical pa-

pers in scientiﬁc journals and international conferences. His research interests

include 5G wireless networks, Internet of Things, and molecular communi-

cations. He is a corecipient of the Best Paper Award presented at the IEEE

International Conference on Communications 2016 and the IEEE International

Conference on Ultra-Wideband 2007. He is an IEEE Distinguished Lecturer.

He has been selected as a Web of Science (ISI) Highly Cited Researcher in

2016. He is an Editor of the IEEE TRANSACTIONS ON COMMUNICATIONS and

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY. He was an Editor of the

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2006–2011), the IEEE

WIRELESS COMMUNICATIONS LETTERS, and the IEEE SIGNAL PROCESSING

LETTERS.