Simultaneous Wireless Information and Power Transfer in K-tier Heterogeneous Cellular Networks

Abstract

In this paper, we develop a tractable model for joint downlink (DL) and uplink (UL) transmission of K-tier heterogeneous cellular networks (HCNs) with simultaneous wireless information and power transfer (SWIPT) for efficient spectrum and energy utilization. In the DL, the mobile users (MUs) with power splitting receiver architecture decode information and harvest energy based on SWIPT. While in the UL, the MUs utilize the harvested energy for information transmission. Since cell association greatly affects the energy harvesting in the DL and the performance of wireless powered HCNs in the UL, we compare the DL and the UL performance of a random MU in HCNs with nearest base station (NBS) cell association to that with maximum received power (MRP) cell association. We first derive the DL average received power for the MU with the NBS and the MRP cell associations. To evaluate the system performance, we then derive the outage probability and the average ergodic rate in the DL and the UL of a random MU in HCNs with the NBS and the MRP cell associations. Our results show that increasing the small cell base station (BS) density, the BS transmit power, the time allocation factor, and the energy conversion efficiency, weakly affect the DL and UL performance of both cell associations. However, the UL performance of both cell associations can be improved by increasing the fraction of the DL received power used for energy harvesting. Index Terms—Simultaneous wireless information and power transfer, heterogeneous cellular networks, energy efficiency, spectral efficiency, stochastic geometry.

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Transactions on Wireless Communications
1
Simultaneous Wireless Information and Power
Transfer in K-tier Heterogeneous Cellular Networks
Sunila Akbar, Student Member, IEEE, Yansha Deng, Member, IEEE, Arumugam Nallanathan, Senior
Member, IEEE, Maged Elkashlan, Member, IEEE, and A. Hamid Aghvami, Fellow, IEEE
Abstract—In this paper, we develop a tractable model for joint
downlink (DL) and uplink (UL) transmission of K-tier hetero-
geneous cellular networks (HCNs) with simultaneous wireless
information and power transfer (SWIPT) for efficient spectrum
and energy utilization. In the DL, the mobile users (MUs) with
power splitting receiver architecture decode information and
harvest energy based on SWIPT. While in the UL, the MUs utilize
the harvested energy for information transmission. Since cell
association greatly affects the energy harvesting in the DL and the
performance of wireless powered HCNs in the UL, we compare
the DL and the UL performance of a random MU in HCNs with
nearest base station (NBS) cell association to that with maximum
received power (MRP) cell association. We first derive the DL
average received power for the MU with the NBS and the MRP
cell associations. To evaluate the system performance, we then
derive the outage probability and the average ergodic rate in the
DL and the UL of a random MU in HCNs with the NBS and the
MRP cell associations. Our results show that increasing the small
cell base station (BS) density, the BS transmit power, the time
allocation factor, and the energy conversion efficiency, weakly
affect the DL and UL performance of both cell associations.
However, the UL performance of both cell associations can be
improved by increasing the fraction of the DL received power
used for energy harvesting.
Index Terms—Simultaneous wireless information and power
transfer, heterogeneous cellular networks, energy efficiency, spec-
tral efficiency, stochastic geometry.
I. INT ROD UC TI ON
ENERGY efficiency is envisioned as one of the major
challenges in the design of fifth generation (5G) systems
considering the ever-growing energy consumption [2], and the
harmful impact on the environment [3]. At the same time, due
to the dramatic increase in the multimedia applications along
with the emerging future applications, such as smart cities,
health monitoring devices, and driverless cars, the 5G system
will require much higher capacity and spectrum efficiency
[4]. Radio frequency wireless power transfer (RF-WPT) is an
emerging technology which enables the wireless devices to
harvest energy from the RF signals for their information pro-
cessing and transmission, therefore provides efficient energy
Manuscript received Oct. 12, 2015; revised Feb. 18, 2016; accepted May
10, 2016. This work was supported by the UK Engineering and Physical
Sciences Research Council (EPSRC) with Grant No. EP/M016145/1. This
paper was presented in part at the IEEE Global Communications Conference,
San Diego, CA, USA, December 2016 [1]. The editor coordinating the review
of this manuscript and approving it for publication was Dr. Mehdi Bennis.
S. Akbar, Y. Deng, A. Nallanathan, and H. Aghvami are with the
Department of Informatics, King’s College London, London, UK (e-
mail: {sunila.akbar, yansha.deng, arumugam.nallanathan, hamid.aghvami}
@kcl.ac.uk).
M. Elkashlan is with Queen Mary University of London, London E1 4NS,
UK (e-mail: maged.elkashlan@qmul.ac.uk).
utilization [5, 6]. Recently, simultaneous wireless information
and power transfer (SWIPT) technique has emerged as a
novel research direction, which provides significant gains
in terms of spectral efficiency and energy consumption by
transmitting information and energy via the same signal [7].
Moreover, SWIPT not only offers a low cost option for energy
harvesting with no requirement of additional infrastructure
at the transmitter side, but also provides better interference
control unlike conventional RF-WPT [8].
More recently, there has been an increasing interest in
enhancing network capacity via the deployment of small cell
base stations (BSs) (e.g., micro, pico, and femto) underly-
ing the conventional macrocell BSs, namely, heterogeneous
cellular networks (HCNs). The HCNs boost the network
capacity through a better spatial resource reuse [9–11], but the
challenge is the resulting increased interference [12–14]. The
densely deployed BSs make HCNs attractive for efficient RF-
WPT, since the distance between the mobile user (MU) and
the BS in HCNs is much shorter than that in homogeneous
macrocell networks. Moreover, the aggregate interference in
HCNs due to full frequency reuse could be a supplementary
energy source. Self-sustained low-cost SWIPT technique can
boost the spectrum and energy efficiency while making use of
the interference in HCNs.
A. Related Work and Motivations
1) Energy Harvesting in Wireless Powered Communication
Networks: Lately, energy harvesting in wireless powered
communication networks (WPCNs) has received considerable
attention, where wireless devices harvest energy from the
ambient RF signals in the wireless network. The work in [15]
proposed ‘harvest then transmit’ protocol in single-antenna
WPCN, where MUs harvest energy in the downlink (DL)
for transmitting the uplink (UL) information. The work in
[16] studied the energy beamforming design with transmit
power control to maximize the UL throughput performance
in multi-antenna WPCN. The work in [7] highlighted the
potential benefits of SWIPT in resource allocation algorithms
and cognitive radio networks. Furthermore, WPCN designs are
developed for user cooperation [17], full duplex (FD) network
[18], massive multiple input multiple output (MIMO) system
[19], and cognitive relay network [20].
2) Energy Harvesting in Wireless Cellular Networks: In
[21], RF signal transmitted by primary users was used to
power the secondary users in cognitive radio network. In [22],
the device to device (D2D) communication was powered by
the energy harvested from the concurrent DL transmissions

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Transactions on Wireless Communications
2
of the macro BSs. In [23], the power beacons (PBs) were
deployed in the cellular network to power the MUs for the
UL information transmission, but the deployment of dedicated
PBs incur additional operation and maintenance costs. The
work in [24] studied RF-WPT in the DL for UL information
transmission in K-tier HCNs. In [25], the UL transmission of
MUs are powered by the ambient interference. However, it
has been mentioned in [26] that harvesting energy from the
non-dedicated ambient interference signals could be unstable
and unreliable. Applying SWIPT in HCNs can provide stable
and reliable energy for MUs by harvesting energy from the
dedicated serving BS (similar to PBs), as well as from the DL
interference signals at no extra cost.
3) Modeling of Wireless Powered HCNs: Recently, model-
ing and analysis of HCNs using stochastic geometry has been
validated to provide tractable yet accurate performance bounds
[27]. A crucial factor in modeling the wireless powered HCNs
is cell association which substantially affects the network
performance [28]. The UL cell association in wireless powered
HCNs has been studied in [29] and [25], where the MUs
are powered by the harvested energy from the ambient RF
signals. In [29], the UL cell association was based on nearest
BS (NBS) cell association, while in [25], it was based on
flexible cell association.
B. Contributions and Organization
Motivated to jointly support energy sustainability and high
throughput performance, this work aims to integrate SWIPT
with HCNs. We use SWIPT in the DL transmission of HCNs
where the MUs with no built-in power supply harvest energy
and decode information. The MUs then utilize the harvested
energy for information transmission in the UL. The NBS cell
association both in the DL and the UL is an optimal approach
for the proposed HCN due to the following reasons: 1) the
low path loss in the UL information transmission as was
considered in [29] for wireless powered HCNs, 2) the simple
implementation with no system overheads for averaging the
received power within a measurement period as with the
maximum received power (MRP) cell association, and 3) the
design and operation of the logical, transport, and physical
channel is less complicated in the coupled cell association,
where the MU associates with the same BS in both the DL
and UL [30]. Moreover, to study the impact of cell association
on SWIPT based wireless powered HCNs, we compare the
DL and the UL performance of the proposed HCN with the
NBS cell association to that with the conventional MRP cell
association.
The main contributions of the paper are summarized as
follows:
•Using stochastic geometry, we present the analytical
model for SWIPT in HCNs with the NBS and the
MRP cell associations, in both DL and UL. We derive
the closed-form expression for the DL average received
power at the typical MU with the NBS and the MRP
cell associations which plays a pivotal role in the UL
performance evaluation.
•We derive the analytical expressions for the DL outage
probability and the DL average ergodic rate with the
NBS and the MRP cell associations. We find that the DL
performance of a random MU in HCNs with the NBS
cell association can achieve comparable performance to
that with the MRP cell association. Our results show
that increasing the small cell BS density improves the
DL performance of macrocell and picocell MUs with the
NBS cell association, whereas has little impact on that
with the MRP cell association.
•We evaluate the UL performance in terms of the UL
outage probability and the UL average ergodic rate for
the NBS and the MRP cell associations. Interestingly,
the UL performance of a random MU in HCNs with
the NBS cell association is comparable to that with the
MRP cell association. We find that increasing the small
cell BSs improves the UL performance of both macrocell
and picocell MUs with the NBS cell association, whereas
degrades that with the MRP cell association.
The rest of the paper is organized as follows. In Section
II, we present the system model of SWIPT in HCNs. In
Section III, we derive the DL average received power for the
NBS and the MRP cell associations. We then evaluate the
network performance in terms of the DL and the UL outage
probabilities, and the DL and the UL average ergodic rates for
the NBS and the MRP cell association in Section IV. Finally,
the numerical results are discussed in Section V before the
paper is concluded in Section VI.
II. SY ST EM MO DE L
A. Network Model
We consider a conventional HCN model with Ktiers of
BSs spatially distributed in R2as a homogeneous Poisson
point process (HPPP) Φjwith spatial density λj, BS transmit
power Pt,bj, and path loss exponent `j, where j= 1,··· , K
is the index of the jth tier. The MUs are also modeled as
an independent HPPP Φuwith density λu. We assume MUs
with large storage battery which eliminates the randomness
of instantaneous received power and provides fixed transmit
power [23]. We denote the jth tier BS and jth tier MU as bj,
and uj, respectively. We denote ‘k∈ {1,·· · , K }’as the index
of the tier with which a typical MU is associated, and bb
kas
the typical serving BS in the kth tier.
B. Channel Model
We model the channel path loss over the distance kxkas
L0kxk−`j, where L0is the path loss at a reference distance
of 1m. We consider Rayleigh fading with unit mean to model
the random channel fluctuations, and the channel coefficients
are assumed to be independent and identically distributed
across all links. We consider no intra-cell interference, where
orthogonal multiple access is employed within a cell. We
assume time division duplex (TDD) mode. Furthermore, we
assume time division multiple access (TDMA), where several
MUs share the same channel in different time slots, thus the BS
transmit power is independent of the density of active MUs.

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Transactions on Wireless Communications
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Uplink Information Transmission
T
αT (1− α)T
Wireless Power Transfer
Downlink Information Transmission
( )
u0
,
1
k
P
ρ
−
u0
,k
r
P
ρ
u0
,kr
P
r
Fig. 1: Frame Structure
C. Transmission Block Model
The transmission block structure is shown in Fig. 1, we
assume that the transmission block time is normalized as
T= 1. A fraction of the block time αT is used for SWIPT,
where α(0 ≤α≤1) is called the time allocation factor.
The remaining portion of time (1 −α)Tis used for the UL
information transmission, which is powered by the energy
harvested from the first αT time.
In the DL, the receiver with power splitting architecture
splits the received signal for energy harvesting and information
decoding. We assume Pru0,kas the DL received power at the
typical user in the kth tier, a fraction of which ρPru0,kis used
for energy harvesting, where ρ(0 ≤ρ≤1) is called the power
splitting factor. The remaining fraction of the received power
(1 −ρ)Pru0,kis used for information decoding.
D. Cell Association Model
To ensure low path loss in the UL, we consider the NBS
cell association. For a typical MU u0located at the origin, the
location of the serving nearest BS in the kth tier, xbb
kis given
as
xbb
kNBS = argmin
{x∈Φj}j=1,··· ,K kxk,(1)
where kxkdenotes the Euclidean distance between a BS to
the typical MU.
We compare the performance of HCNs with the NBS cell
association to that with the MRP cell association. In the MRP
cell asociation, the MU connects to the BS which offers the
maximum (long term averaged) received power to the MU,
i.e., small scale fading is ignored, as in [31]. For a typical
MU u0located at the origin, the location of the serving BS
in the kth tier that offers the maximum received power to the
typical MU, xbb
kis given as
xbb
kMRP = argmax
{x∈xbb
j|MRP}
j=1,··· ,K
Pt,bjkxk−`j.(2)
In (2), we have xbb
jas the the location of the BS in the
jth tier that offers the maximum received power to the typical
MU, given as
xbb
jMRP = argmax
{x∈Φj}j=1,··· ,K
Pt,bjkxk−`j,(3)
where Φjdenotes the position sets of BSs in the jth tier.
In the UL information transmission, the typical MU trans-
mits information to the same serving BS of the HCN as in the
current cellular networks [28].
E. Wireless Power Transfer Model
A short range propagation model [32] is used for wireless
power transfer to avoid the singularity caused by proximity
between BSs and MUs i.e., to ensure that the power received
at the MU is finite [23]. The received power of a typical MU
that is associated to the BS in the kth tier can be written as
Pru0,k=Pt,bb
khbb
ku02L0max 
xbb
k,u0
, d−`k
| {z }
Ibb
k
+
K
X
j=1 X
bj∈Φj\bb
k
Pt,bjhbju02L0max 
xbju0
, d−`j
| {z }
Ibx
,
(4)
where Ibb
kis the useful signal, Ibxis the intercell interference,
d≥1is a constant, hbb
ku0is the small-scale fading channel
coefficient from the serving BS to the typical MU, 
xbb
k,u0

is the distance between the serving BS and the typical MU,
hbju0is the small-scale fading interfering channel coefficient
from the jth tier BS to the typical MU, and 
xbju0
is the
distance between the jth tier BS and the typical MU.
F. Downlink Information Transmission Model
In the DL information transmission, a fraction of the re-
ceived power (1 −ρ)Pru0,kat the MU is used for information
decoding in the αT time.
For the DL analysis, we shift all point processes such that
the typical MU is located at the origin. According to Slivnyak’s
theorem, the distribution of the shifted HPPPs remain the
same as the original HPPPs with the same intensities [33].
The signal-to-interference-plus-noise ratio (SINR) of the DL
information transmission is given by
SI N RDL
k
=(1 −ρ)Pt,bb
khbb
ku02L0
xbb
ku0
−`k
(1 −ρ)
K
P
j=1 P
bj∈Φj\bb
k
Pt,bjhbju02L0
xbju0
−`j+σ2
,
(5)
where σ2is the noise power.
G. Uplink Information Transmission Model
In the UL information transmission, the MUs keep asso-
ciated with the serving BSs that powered them in the first
αT time, and use the harvested energy to transmit the UL
information in the (1 −α)Ttime.
We assume large storage MUs so that the randomness
of instantaneous received power is suppressed and the large
storage provides fixed average received power. We define the

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received energy converted into DC for the MU battery in the
period αT as ηαT ρE{Pru0,k }, where 0< η < 1is the
energy conversion efficiency. Thus, the signal power for UL
information transmission in the period (1 −α)Tis given as
φE{Pru0,k}, where φ=η ρα
(1−α).
For the UL analysis, we use Slivnyak’s theorem to shift the
points of HPPPs such that the serving BS bb
kis located at the
origin. The UL SINR at the serving BS in the kth tier is given
by
SI N RU L
k
=φE{Pru0,k}hu0,b b
k2L0
xu0,bb
k
−`k
K
P
j=1 P
uj∈˜
Φj\u0
φE{Pruj,j }huj,bb
k2L0
xuj,bb
k
−`j+δ2
,
(6)
where hu0,bb
kis the small-scale fading channel coefficient from
the MU to its serving BS, 
xu0,bb
k
is the distance between
the typical MU and the serving BS, huj,bb
kis the small-scale
fading interfering channel coefficient from the jth tier MU uj
to the serving BS, 
xuj,bb
k
is the distance between the jth tier
MU and the serving BS, ˜
Φjdenotes HPPP corresponding to
the interfering MUs in the jth tier, and δ2is the noise power.
Based on the model defined in Section II, we aim to derive
the analytical expression for the average received power in
the DL with the NBS and the MRP cell associations before
evaluating the system performance.
III. ANALYSIS OF DOW NL IN K POWER TRANSFE R
To determine the UL transmit power of a typical MU in the
kth tier, we derive the average received power at the typical
MU with the NBS and the MRP cell associations in Lemma
1 and Lemma 2, respectively.
Lemma 1. The average received power at the typical MU as-
sociated with the BS in the kth tier using NBS cell association
is given by
EPru0,kNBS =Pt,b b
kL0(d−`kχ1+χ2)
+ 2πL0
K
X
j=1
Pt,bjλj(χ3−χ4+χ5),(7)
where
χ1=1 −exp{κd2},(8)
χ2=κ`k/4d−`k/2exp −1
2κd2W−`k/2,1/2(1−`k/2) κd2,
(9)
χ3=`jd2
2d`j(`j−2)[1 −exp{−κx2}],(10)
χ4=1
2κd`jγ(2, κ),(11)
χ5=
πPK
j=1 λj(3`j−2)/4
`j−2d−`j/2+1 exp −1
2κx2
W(`j−2)/4,`j/4(κd2),(12)
and
κ=π
K
X
j=1
λj,(13)
where d≥1is a constant, defined in (4), Wλ,µ(.)is Whittaker
function [34], and γ(., .)is lower incomplete gamma function
[34].
Proof. See Appendix A.
Lemma 2. The average received power at the typical MU as-
sociated with the BS in the kth tier using MRP cell association
is given by
EPru0,kMRP =Pt,b b
kL0
Υk
(d−`kΞ1+ Ξ2)
+2πL0
Υk
K
X
j=1
Pt,bjλjΞ3+ (`j−2)−1Ξ4,
(14)
where
Ξ1=
d
Z
0
xexp{−
K
X
j=1
µk,j x2`k/`j}dx, (15)
Ξ2=
∞
Z
d
x−(`k−1)expn−
K
X
j=1
µk,j x2`k/`jodx, (16)
Ξ3=
θj,k
Z
0
x
2d`j `jd2
(`j−2) −(δDL
j,k )2/`jx2`k/`j!
expn−
K
X
j=1
µk,j x2`k/`jodx, (17)
Ξ4=
∞
Z
θj,k
xexpn−
K
P
j=1
µk,j x2`k/`jo
((δDL
j,k )1/`jx`k/`j)`j−2dx, (18)
θj,k =d`j/`k(δDL
j,k )−`k,(19)
Υk=
∞
Z
0
rexpn−
K
X
j=1
µk,j r2`k/`jodr, (20)
µk,j =πλj(δDL
j,k )2/`j,(21)
and
δDL
j,k =Pt,bj/Pt,bk,(22)
where d≥1is a constant, defined in (4).
Proof. See Appendix B.
IV. PER FO RM AN CE EVAL UATIONS: ANALYS IS
The performance of the DL and the UL transmission of the
HCN is characterized by the outage probability and average
ergodic rate.

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A. Downlink Outage Probability
The DL outage probability is the probability that the instan-
taneous DL data rate of a randomly selected MU in HCNs is
less than the target DL data rate. According to the law of total
probability, the DL outage probability of a random MU in K
tier HCNs is given by
PDL
out =
K
X
k=1
ΛDL
kPDL
out,k,(23)
where ΛDL
kis the probability that a typical MU is associated
with the kth tier, and PDL
out,k is the DL outage probability of
the typical MU associated with the kth tier.
In (23), the probability that a typical MU is associated to
the BS in the kth tier with the NBS cell association is given
as
ΛkNBS = 1 + PK
j=1,j6=kλj
λk!−1
,(24)
and the probability that a typical MU is associated to the BS
in the kth tier with the MRP cell association is given as
ΛkMRP = 2πλk
∞
Z
0
rexpn−
K
X
j=1
µk,j r2`k/`jodr, (25)
where µk,j is given in (21).
In (23), the DL outage probability for the typical MU at a
distance 
xbb
k,u0
from its associated BS is defined as
PDL
out,k (Rs) =E

xbb
k,u0


hPr αln 1 + SI N RDL
k
xbb
k,u0

≤Rsi
=E

xbb
k,u0


Pr SI N RDL
k
xbb
k,u0
≤β,
(26)
where Rsis the rate threshold, and
β= eRs/α −1.(27)
1) General Case: In this section we provide our general
result for the DL outage probability of a typical MU associated
with the BS in the kth tier from which the special result for
interference-limited case will follow.
Theorem 1. The DL outage probability of a typical MU as-
sociated with the BS in the kth tier using NBS cell association
is derived as
PDL
out,k,N BS (Rs) =1 −2κ
∞
Z
0
xexpn−σ2βΩDL
kx`k
−
K
X
j=1
πλjϑk,j +x2odx, (28)
where
ϑk,j =f2/`j
k,j
∞
Z
f
−2/`j
k,j x2
1
1 + z`j/2dz, (29)
ΩDL
k=(1 −ρ)Pt,bb
kL0−1,(30)
and
fk,j =βδDL
j,k x`k,(31)
κ,β, and δDL
j,k are given in (13),(27), and (22), respectively.
Proof. See Appendix C.
Theorem 2. The DL outage probability of a typical MU asso-
ciated with the BS in the kth tier using MRP cell association
is derived as
PDL
out,k,M RP (Rs) =1 −1
Υk
∞
Z
0
xexpn−σ2βΩDL
kx`k
−
K
X
j=1
πλj$k,j + (δDL
j,k )2/`jx2`k/`jodx,
(32)
where
$k,j =f2/`j
k,j Z∞
β−2/`j
1
1 + z`j/2dz, (33)
Υk,β,δDL
j,k ,ΩDL
k, and fk,j are given in (20),(27),(22),(30),
and (31), respectively.
Proof. See Appendix D.
2) Interference-Limited Case, Equal Path Loss Exponents
{`j}= 4:In HCNs with high transmit power BSs, the inter-
ference dominates the noise. The thermal noise can therefore
be neglected in the rest of this section.
Corollary 1. With {`j}= 4 and σ2= 0, the DL outage
probability of a typical MU associated with the kth tier using
NBS cell association is derived as
PDL
out,k,N BS (Rs)
= 1 −κ
K
P
j=1
πλjqβδDL
j,k arctan nqβδDL
j,k o+ 1,(34)
where κ,β, and δDL
j,k are given in (13),(27), and (22),
respectively.
Corollary 2. With {`j}= 4 and σ2= 0, the DL outage
probability of a typical MU associated with the kth tier using
MRP cell association is derived as
PDL
out,k,M RP (Rs)
= 1 −
K
P
j=1
λjqδDL
j,k
K
P
j=1
λjqδDL
j,k √βarctan n√βo+ 1!,(35)

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where βand δDL
j,k are given in (27) and (22), respectively.
The expressions in (34) and (35) are in closed-form. We
find that the DL outage probability in the interference-limited
scenario is independent of the power splitting factor ρ. This is
due to the fact that the term (1−ρ)in the SI N RD L
kexpression
in (5) cancels out with σ2= 0.
B. Downlink Average Ergodic Rate
The DL average ergodic rate of a Ktier HCNs measures
the spectral efficiency of HCNs in the DL. The DL average
ergodic rate of a random MU in the Ktier HCNs is given by
RDL =
K
X
k=1
ΛDL
kRDL
k,(36)
where RDL
kis the DL average ergodic rate of a typical MU
associated with the kth tier and ΛUL
kis given in (24) for the
NBS cell association, and in (25) for the MRP cell association,
respectively.
In (36), the DL average ergodic rate for a typical MU at
a distance 
xbb
k,u0
from its associated BS in the kth tier is
defined as
RDL
k=E

xbb
k,u0


ESI NRD L
khα
ln 1 + SI N RDL
k
xbb
k,u0
i.(37)
1) General Case: We now present the general result for
the DL average ergodic rate of a typical MU associated with
the kth tier followed by the special result for the interference-
limited scenario.
Theorem 3. The DL average ergodic rate of a typical MU
associated with the kth tier using NBS cell association is
derived as
RDL
k,N BS =2κ
∞
Z
0
∞
Z
0
xexpn−σ2(et/α −1)ΩDL
kx`k−
K
X
j=1
πλj
(et/α −1)δDL
j,k x`k2/`jZ∞
((et/α−1)δDL
j,k x`k)−2/`jx2
1
1 + z`j/2dz −x2odtdx, (38)
where ΩDL
kand δDL
j,k are given in (30) and (22), respectively.
Proof. See Appendix E.
Theorem 4. The DL average ergodic rate of a typical MU
associated with the kth tier using MRP cell association is
derived as
RDL
k,M RP =1
Υk
∞
Z
0
∞
Z
0
xexpn−σ2(et/α −1)ΩDL
kx`k−
K
X
j=1
πλj
(et/α −1)δDL
j,k x`k2/`jZ∞
(et/α−1)−2/`j
dz
1 + z`j/2
+ (δDL
j,k )2/`jx2`k/`jodtdx, (39)
where ΩDL
kand δDL
j,k are given in (30) and (22), respectively.
Proof. See Appendix F.
2) Interference-Limited Case, Equal Path Loss Exponents
{`j}= 4:In the following, we present the DL average ergodic
rate of a typical MU associated with the kth tier in HCNs in
the interference-limited scenario.
Corollary 3. With {`j}= 4 and σ2= 0, the DL averaqe
ergodic rate of a typical MU associated with the kth tier
using NBS cell association is derived as (40) at the top of
the next page, where κand δDL
j,k are given in (13) and (22),
respectively.
Corollary 4. With {`j}= 4 and σ2= 0, the DL averaqe
ergodic rate of a typical MU associated with the kth tier using
MRP cell associations is derived as (41) at the top of the next
page, where δDL
j,k is given in (22).
In these corollaries, the double integral in Theorem 3 and 4
is simplified to a single integral. We find that the DL average
ergodic rate in the interference-limited scenario is independent
of the power splitting factor due to the fact that with σ2= 0,
the term (1 −ρ)disappears in the SI N RDL
kgiven in (5).
In the following, we present the UL performance of the
HCN which reflects the DL energy harvesting efficiency of
SWIPT with the NBS and the MRP cell associations. We
characterize the UL performance in terms of the UL outage
probability and the UL average ergodic rate.
C. Uplink Outage Probability
The UL outage probability is the probability that the instan-
taneous UL data rate at the serving BS in HCNs is less than
the target UL data rate. The UL outage probability in HCNs
is given by
PUL
out =
K
X
k=1
ΛUL
kPUL
out,k,(42)
where ΛUL
kis given in (24) for the NBS cell association, and
in (25) for the MRP cell association, and PUL
out,k is the UL
outage probability of a typical MU associated with the kth
tier.
In (42), the UL outage probability for a typical MU at a
distance 
xu0,bb
k
from its associated BS is defined as
PUL
out,k (Rs) =E

xbb
k,u0


Pr (1 −α)
ln 1 + SI N RU L
k
xbb
k,u0
≤Rs
=E

xbb
k,u0


Pr SI N RU L
k
xbb
k,u0
≤Ψ.
(43)
where
Ψ=eRs/(1−α)−1.(44)

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RDL
k,N BS =
∞
Z
0
κ
K
P
j=1
πλjqet/α −1δDL
j,k arctan nqet/α −1δDL
j,k o+ 1dt, (40)
RDL
k,M RP =
∞
Z
0
K
P
j=1
λjqδDL
j,k
K
P
j=1
λjqδDL
j,k qet/α −1arctan nqet/α −1o+ 1!dt, (41)
1) General Case: In the following, we derive the general
result for the UL outage probability of a typical MU associated
with the kth tier.
Theorem 5. The UL outage probability of a typical MU
associated with the kth tier using NBS cell association is
derived as
PUL
out,k,N BS (Rs) =1 −2κ
∞
Z
0
xexp(−σ2ΨΩUL
kx`k
−
K
X
j=1 ζUL
k,j x
2`k
`jΨ
2
`j+πλjx2)dx,
(45)
where
ΩUL
k=φE{Pru0,k}L0−1,(46)
ζUL
k,j =πλjδU L
j,k 2
`jΓ1 + 2
`jΓ1−2
`j,(47)
δUL
j,k =
EhPruj,j i
EPru0,k,(48)
and Ψis given in (44).
Proof. The proof follows similar steps to Theorem 1.
Theorem 6. The UL outage probability of a typical MU
associated with the kth tier using MRP cell association is
derived as
PUL
out,k,M RP (Rs) =1 −1
Υk
∞
Z
0
xexp(−σ2ΨΩUL
kx`k
−
K
X
j=1 ζUL
k,j x
2`k
`jΨ
2
`j+µk,j x2`k/`j)dx,
(49)
where Ψ,ΩUL
k,ζUL
k,j , and µk,j are given in (44),(46),(47),
and (21), respectively.
Proof. The proof follows similar steps to Theorem 2.
2) Interference-Limited Case, Equal Path Loss Exponents
{`j}= 4:We now present the UL outage probability of a
typical MU associated with the kth tier in the interference-
limited case.
Corollary 5. With {`j}= 4 and δ2= 0, the UL outage
probability of a typical MU associated with the kth tier using
NBS cell association is derived as
PUL
out,k,N BS (Rs)=1−κ
K
P
j=1
πλjπ
2qδUL
j,k Ψ+1,(50)
where κ,δUL
j,k , and Ψare given in (13),(48), and (??),
respectively.
Corollary 6. With {`j}= 4 and δ2= 0, the UL outage
probability of a typical MU associated with the kth tier using
MRP cell association is derived as
PUL
out,k,M RP (Rs) = 1 −
K
P
j=1
λjqδDL
j,k
K
P
j=1
λjπ
2qδUL
j,k Ψ + qδDL
j,k ,
(51)
where δDL
j,k ,δUL
j,k , and Ψare given in (22),(48), and (??),
respectively.
We find that the UL outage probabilities for the NBS
and the MRP cell associations are independent of the energy
conversion efficiency and the power splitting factor. This can
be explained by the fact that the term φ=ηρα
(1−α)in (6) cancels
out with δ2= 0.
D. Uplink Average Ergodic Rate
The UL average ergodic rate of a random MU in Ktier
HCNs is given by
RUL =
K
X
k=1
AUL
kRUL
k,(52)
where RUL
kis the UL average ergodic rate of a typical MU
associated with the kth tier and ΛUL
kis given in (24) for the
NBS cell association, and in (25) for the MRP cell association,
respectively.
In (52), the UL average ergodic rate of a random MU
located at a distance 
xu0,bb
k
from its associated BS in the
kth tier is defined as
RUL
k=E

xu0,bb
k


ESI NRU L
kh(1 −α)
ln 1 + SI N RU L
k
xu0,bb
k
.(53)

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Transactions on Wireless Communications
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1) General Case: We now provide the general result for
the UL average ergodic rate of a random MU associated with
the BS in kth tier.
Theorem 7. The UL average ergodic rate of a random MU
associated with the BS in kth tier using NBS cell association
is derived as
RUL
k,N BS =2(1 −α)κ
∞
Z
0
∞
Z
0
x
1 + texp "−δ2ΩUL
ktx`k
−
K
X
j=1 ζUL
k,j t
2
`jx
2`k
`j+πλjx2#dxdt. (54)
where κ,ΩUL
k, and ζUL
k,j are given in (13),(46), and (47),
respectively.
Proof. The proof follows similar steps to Theorem 3.
Theorem 8. The UL average ergodic rate of a random MU
associated with the BS in kth tier using MRP cell association
is derived as
RUL
k,M RP =(1 −α)
Υk
∞
Z
0
∞
Z
0
x
1 + texp "−δ2ΩUL
ktx`k
−
K
X
j=1 ζUL
k,j t
2
`jx
2`k
`j+µk,j x2`k/`j#dxdt,
(55)
Proof. The proof follows similar steps to Theorem 4.
2) Interference-Limited Case, Equal Path Loss Exponents
{`j}= 4:We present the UL average ergodic rate of a typical
MU associated with the kth tier for the interference-limited
network in the following corollaries.
Corollary 7. With {`j}= 4 and δ2= 0, the UL average
ergodic rate of a typical MU associated with the kth tier using
NBS cell association is derived as
RUL
k,N BS =
∞
Z
0
(1 −α)κ
(1 + t)
K
P
j=1
πλjπ
2qδUL
j,k t+ 1dt, (56)
where κand δUL
k,j are given in (13) and (48), respectively.
Corollary 8. With {`j}= 4 and δ2= 0, the UL average
ergodic rate of a typical MU associated with the kth tier using
MRP cell association is derived as
RUL
k,M RP (Rs) =
∞
Z
0
(1 −α)
K
P
j=1
λjqδDL
j,k
(1 + t)
K
P
j=1
λjπ
2qδUL
j,k t+qδDL
j,k dt,
(57)
where δDL
j,k and δUL
j,k are given in (22) and (48), respectively.
In the interference-limited scenario, the UL average ergodic
rate does not depend on the energy conversion efficiency and
the power splitting factor with the NBS and the MRP cell
associations. This can be seen in (6) that with δ2= 0, the term
φ=ηρα
(1−α)cancels out and SI N RU L
kbecomes independent
of ηand ρ.
In the following, we present the global (GL) performance
of the HCN which reflects the impact of power splitting factor
on the overall (DL+UL) performance. We characterize the GL
performance in terms of the GL average ergodic rate. We
characterize the GL performance in terms of the GL average
ergodic rate.
E. Global Average Ergodic Rate
We define the GL average ergodic rate of a random MU in
Ktier HCNs as the sum of the DL average ergodic rate and
the UL average ergodic rate, as follows
RGL =RDL +RU L,(58)
where RDL and RU L are given in (36) and (52), respectively.
The GL average ergodic rate is defined to find the optimal
power splitting factor ρ∗that maximizes the GL average
ergodic rate. The evaluation for the exact expression of ρ∗
turns out to be intractable, therefore, we numerically evaluate
the optimal ρ∗that maximizes RGL in the numerical results.
V. NU ME RI CA L RES ULTS
In this section, we compare the system performance with
the NBS cell association to that with the MRP cell association
in terms of the DL outage probability, the DL average ergodic
rate, the UL outage probability, and the UL average ergodic
rate. We plot the DL outage probability, the DL average
ergodic rate, the UL outage probability, and the UL average
ergodic rate for the NBS cell association using (28), (45), (38),
and (54), respectively. We plot the DL outage probability, the
DL average ergodic rate, the UL outage probability, and the
UL average ergodic rate for the MRP cell association using
(32), (39), (49), and (55), respectively. The analytical results
are validated by Monte Carlo simulations, where the BSs and
the MUs are deployed according to the proposed model for a
two-tier HCN. In all the figures, the path loss is assumed to
be L0=−38.5dB at 1 meter, and the path loss exponents
are `1= 3.8and `2= 3.5. The thermal noise power at the
MU and the BS are fixed as σ2=δ2=−104 dB for 10 MHz
bandwidth. Unless otherwise stated, the time allocation factor
α= 0.5, the power splitting factor ρ= 0.5, and the energy
conversion efficiency η= 0.5.
A. Effect of Picocell BSs Density and BS Transmit Power
In this subsection, we examine the effect of the density of
picocell BSs and the transmit power at the BSs on the DL
outage probability, the DL average ergodic rate, the UL outage
probability, and the UL average ergodic rate of a random MU
in HCNs with the NBS and the MRP cell associations. In Figs.
2, 3, 4, and 5, we set λ1= 10−3and Rs= 0.5nats/s/Hz.
Downlink Performance: Fig. 2a and Fig. 2b compare the
impact of the density of picocell BSs λ2and the BS transmit
power Pt,b of each tier on the DL outage probability with
the NBS cell association PDL
out,NB S (Rs), to the DL outage

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10−3 10−2 10−1 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
λ2
DL Outage Probability - NBS
Simu. Pt,b
1
= 46 dBm, Pt,b
2
= 30 dBm
Simu. Pt,b
1
= 46 dBm, Pt,b
2
= 37 dBm
Simu. P
t,b
1
= 53 dBm, Pt,b
2
= 30 dBm
Exact Analysis
Tier 2
Tier 1
HCN
(a) DL outage probability with the NBS cell association.
10
−3 10
−2 10−1 100
0.57
0.58
0.59
0.60
0.61
0.62
0.63
0.64
0.65
0.66
λ2
DL Outage Probability - MRP
Tier 2
HCN
Tier 1
Simu. Pt,b1
= 46 dBm, Pt,b2= 30 dBm
Simu. Pt,b1
= 46 dBm, Pt,b2= 37 dBm
Simu. P
t,b1= 53 dBm, Pt,b2= 30 dBm
Exact Analysis
(b) DL outage probability with the MRP cell association.
Fig. 2: Impact of picocell BS density and BS transmit power
in a two-tier HCN.
probability with the MRP cell association PDL
out,MRP (Rs),
respectively. Fig. 3a and Fig. 3b compare the impact of the
density of picocell BSs λ2and the BS transmit power of
each tier on the DL average ergodic rate with the NBS cell
association RDL
NB S , to the DL avearge ergodic rate with the
MRP cell association RDL
MRP respectively.
With the increase of λ2,PDL
out,NB S and RDL
NB S improve due
to the increase in signal strength at the MU from the nearest
serving BS. Interestingly, the increase in λ2does not have a
significant affect on PDL
out,MRP and RDL
MRP at both tiers. We
also observe that PDL
out,NB S ,RDL
NB S ,PDL
out,MRP , and RDL
MRP of
a random MU in HCNs are approximately the same with the
increase in λ2.
With the increase of Pt,b in the kth tier, PDL
out,NB S (Rs)
and RDL
NB S of the kth tier improve, while that of other tiers
10
−3 10
−2 10
−1 10
0
0
0.5
1
1.5
2
2.5
3
λ2
DL Average Ergodic Rate (nats/s/Hz) - NBS
Tier 1
HCN
Tier 2
Simu. Pt,b1
= 46 dBm, Pt,b2= 30 dBm
Simu. Pt,b1
= 46 dBm, Pt,b2= 37 dBm
Simu. P
t,b1= 53 dBm, Pt,b2= 30 dBm
Exact Analysis
(a) DL average ergodic rate with the NBS cell association.
10
−3 10
−2 10−1 10
0
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
λ2
DL Average Ergodic Rate (nats/s/Hz) - MRP
Tier 1
HCN
Tier 2
Simu. Pt,b1
= 46 dBm, Pt,b2= 30 dBm
Simu. Pt,b1
= 46 dBm, Pt,b2= 37 dBm
Simu. P
t,b1= 53 dBm, Pt,b2= 30 dBm
Exact Analysis
(b) DL average ergodic rate with the MRP cell association.
Fig. 3: Impact of picocell BS density and BS transmit power
in a two-tier HCN.
degrade. This is due to the increased signal power at the MUs
in the kth tier, and the increased interference at the MUs of
the other tiers. Surprisingly, the increase in Pt,b of the kth
tier slightly affects PDL
out,MRP (Rs)and RDL
MRP of both the
tiers. Furthermore, it is shown that PDL
out,NB S (Rs),RDL
NB S ,
PDL
out,MRP (Rs), and RDL
MRP of a random MU in HCNs cannot
be greatly improved by increasing Pt,b.
Uplink Performance: Fig. 4a and Fig. 4b show the effect
of picocell BS density λ2and the BS transmit power Pt,b
on the UL outage probability with the NBS PUL
out,NB S (Rs),
to the UL outage probability with the MRP cell association
PUL
out,MRP (Rs), respectively. Fig. 5a and Fig. 5b compare the
impact of the density of picocell BSs λ2and the BS transmit
power of each tier on the UL average ergodic rate with the
NBS cell association RUL
NB S , to the UL avearge ergodic rate

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Transactions on Wireless Communications
10
10−3 10−2 10
−1 10
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
λ2
UL Outage Probability - NBS
Tier 2
Tier 1
HCN
Simu. Pt,b1
= 46 dBm, Pt,b2= 30 dBm
Simu. Pt,b1
= 46 dBm, Pt,b2= 37 dBm
Simu. P
t,b1= 53 dBm, Pt,b2= 30 dBm
Exact Analysis
(a) UL outage probability with the NBS cell association.
10
−3 10
−2 10
−1 10
0
0.4
0.5
0.6
0.7
0.8
0.9
1
λ2
UL Outage Probability - MRP
Tier 2
HCN
Tier 1
Simu. Pt,b1
= 46 dBm, Pt,b2= 30 dBm
Simu. Pt,b1
= 46 dBm, Pt,b2= 37 dBm
Simu. P
t,b1= 53 dBm, Pt,b2= 30 dBm
Exact Analysis
(b) UL outage probability with the MRP cell association.
Fig. 4: Impact of picocell BS density and BS transmit power
in a two-tier HCN.
with the MRP cell association RUL
MRP , respectively.
Increasing λ2improves PUL
out,NB S (Rs)and RU L
NB S due to
the increased harvested energy from the serving nearest BS and
the decreased path loss. However, increasing λ2to a certain
value degrades PUL
out,NB S (Rs)and RU L
NB S of the macrocell
MUs due to the dominant effect of the increased interference
from the macrocell MUs with increased transmit power. In
contrast, for the MRP cell association, the increase in λ2
degrades PUL
out,MRP (Rs)and RUL
MRP of both the tiers due
to the dominant effect of higher interference from the large
number of other picocell MUs. Furthermore, PDL
out,NB S (Rs),
RDL
NB S ,PDL
out,MRP (Rs), and RDL
MRP are slightly affected by
increasing λ2.
The increase in Pt,b in the kth tier improves PUL
out,NB S (Rs)
and RUL
NB S in the kth tier and degrades that in other tiers.
The low path loss results in the increased signal power at
10
−3 10
−2 10
−1 10
0
0
0.5
1
1.5
2
2.5
3
λ2
UL Avearge Ergodic Rate (nats/sec/Hz) - NBS
Tier 1
HCN
Tier 2
Simu. Pt,b1
= 46 dBm, Pt,b2= 30 dBm
Simu. Pt,b1
= 46 dBm, Pt,b2= 37 dBm
Simu. P
t,b1= 53 dBm, Pt,b2= 30 dBm
Exact Analysis
(a) UL average ergodic rate with the NBS cell association.
10
−3 10
−2 10
−1 10
0
0
0.2
0.4
0.6
0.8
1
1.2
λ
2
UL Avearge Ergodic Rate (nats/s/Hz) - MRP
Tier 2
HCN
Tier 1
Simu. Pt,b1
= 46 dBm, Pt,b2= 30 dBm
Simu. Pt,b1
= 46 dBm, Pt,b2= 37 dBm
Simu. P
t,b1= 53 dBm, Pt,b2= 30 dBm
Exact Analysis
(b) UL average ergodic rate with the MRP cell association.
Fig. 5: Impact of picocell BS density and BS transmit power
in a two-tier HCN.
the BS of its own tier, and the increased interference at the
BS of the other tier. The opposite holds true for the MRP
cell association, increasing the BS transmit power in the kth
tier degrades PUL
out,MRP (Rs)and RUL
MRP of the kth tier while
improves that of other tiers. This is because increasing the BS
transmit power in the kth tier results in the increased distance
between the MU and the associated BS of the kth tier as
opposed to the decrease of the distance between the MU and
the associated BS of the other tiers as in (B.1). Increasing BS
transmit power only slightly effects PUL
out,NB S (Rs),RU L
NB S ,
PUL
out,MRP (Rs), and RUL
MRP of a random MU in HCNs.
B. Effect of Time Allocation Factor, and Power Splitting
Factor on the DL and the UL performance
In this subsection, we examine the effect of the time
allocation factor and the power allocation factor on the DL and

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Transactions on Wireless Communications
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.4
0.5
0.6
0.7
0.8
0.9
1
α
DL/UL Outage Probability - HCN
ρ=0.2
ρ=0.8, ρ=0.2
ρ=0.8
Simu. DL (NBS)
Simu. DL (MRP)
Simu. UL (NBS)
Simu. UL (MRP)
Exact Analysis
(a) DL/UL outage probability.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
0.2
0.4
0.6
0.8
1
1.2
α
DL/UL Average Ergodic Rate (nats/s/Hz) - HCN
ρ=0.8
ρ=0.2
ρ=0.2, ρ=0.8
Simu. DL (NBS)
Simu. DL (MRP)
Simu. UL (NBS)
Simu. UL (MRP)
Exact Analysis
(b) DL/UL average ergodic rate.
Fig. 6: Impact of time allocation factor and power splitting
factor on the DL/UL performance in a two-tier HCN.
UL outage probability and average ergodic rate of a random
MU in HCNs with the NBS and the MRP cell associations. In
Fig. 6, we set λ1= 10−3,λ2= 2 ×10−3,Pt,b1= 46 dBm,
and Pt,b2= 37 dBm.
Fig. 6a examines the impact of the time allocation factor
αand the power splitting factor ρon the DL and the UL
outage probability of a random MU in HCNs with the NBS
and the MRP cell associations. Fig. 6b examines the impact
of αand ρon the DL and the UL average ergodic rate of
a random MU in HCNs with the NBS and the MRP cell
associations. With the increase of α, the DL outage probabil-
ities, PDL
out,NB S (Rs)and PDL
out,MRP (Rs), and the DL average
ergodic rates, RDL
NB S and RDL
MRP , improve due to allocating
large fraction of time to the DL transmission. For the UL
performance, first we observe that with the increase in α, the
UL outage probabilities, PUL
out,NB S (Rs)and PU L
out,MRP (Rs),
and the UL average ergodic rates, RUL
NB S and RUL
MRP , improve
and then degrade. This is because for small α, the noise plays
a dominant role in the SI N RU L
kas shown in (6), thus the
SI N RU L
kincreases with increasing α. However, for large α,
the degradation of the PUL
out,NB S (Rs),RU L
NB S ,PU L
out,MRP (Rs),
and RUL
MRP is due to allocating a large fraction of time to the
DL transmission than to the UL transmission. Interestingly,
with the increase of ρ, the DL performance of a random MU
in HCNs remains almost unchanged. This is because, with high
density of high transmit power BSs, the interference plays a
dominant role in the SI N RDL
kin (5) and as such the thermal
noise is ignored. However, the UL performance of a random
MU in HCNs improves by increasing ρ. Transmitting the UL
information using a larger fraction of the DL average received
power results in the improved UL performance.
C. Effect of Rate Threshold on the DL and the UL perfor-
mance
Fig. 7 compares the DL and the UL outage probability of
a random MU in HCNs with the NBS to that with the MRP
cell association. In Fig. 7, we set λ1= 10−3,λ2= 2 ×10−3,
Pt,b1= 46 dBm, and Pt,b2= 37 dBm.
The DL outage probability of a random MU in HCNs
with the MRP cell association is narrowly better than that
with the NBS cell association. This is due to the lower
aggregate interference in the SIN RDL
kwith the MRP cell
association than that with the NBS cell association. The UL
outage probability a random MU in HCNs with the NBS
cell association is comparable to that with the MRP cell
association.
D. Effect of Power Splitting Factor on the Global Average
Ergodic Rate
Fig. 8 examines the impact of power splitting factor ρ
on the GL average ergodic rate for the NBS and MRP
cell associations using (58). In Fig. 8, we set λ1= 10−3,
λ2= 2 ×10−3,Pt,b1= 46 dBm, and Pt,b2= 37 dBm.
We observe that the GL average ergodic rate first increases,
then decreases with increasing ρ. The increasing trend is due
to the increase in the UL average ergodic rate. The sudden
decrease is due to the decreases in the DL average ergodic
rate. We observe that the optimal power splitting factor ρ∗, that
maximizes the GL average ergodic rate, occurs near one, i.e.,
ρ∗
NB S = 0.999 and ρ∗
MRP = 0.9995. Moreover, we observe
that the improvement in the GL average ergodic rate for ρ=
0.4to the optimal ρ∗is very small, which reveals that the
optimal operation region for ρis [0.4, 0.999].
VI. CO NC LU SI ON
We have presented a tractable analytical model of K-tier
HCNs with SWIPT where the MUs harvest energy and decode
information simultaneously in the DL, and the harvested
energy at the MU is then utlized for information transmission
in the UL. We have derived the analytical expression for the
DL average received power at a random MU with the NBS
and the MRP cell associations to demonstrate the effect of

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0.5 0.6 0.7 0.8 0.9 1 1.1 1.2
0.6
0.65
0.7
0.75
0.8
0.85
Rs
DL/UL Outage Probability - HCN
Simu. DL (NBS)
Simu. DL (MRP)
Simu. UL (NBS)
Simu. UL (MRP)
Exact Analysis
Fig. 7: Impact of rate threshold on the DL/UL outage proba-
bility in a two-tier HCN.
Simu. DL (NBS)
Simu. DL (MRP)
Exact Analysis
0.4
0 0.1 0.3 1
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
0.9995 0.9996 0.9997 0.9998
1.124
1.12405
1.1241
0.999 0.9992 0.9994 0.9996
1.1033
1.10335
1.1034
Global Average Ergodic Rate (nats/s/Hz)
0.4 0.70.50.2 0.6 0.8 0.9
ρ
Simu. DL (NBS)
Simu. DL (MRP)
Exact Analysis
Fig. 8: Impact of power splitting factor on the GL average
ergodic rate in a two-tier HCN.
harvested energy on the UL information transmission. We have
derived the DL and the UL performance in terms of the outage
probability and the average ergodic rate of a random MU in
HCNs with the NBS and the MRP cell associations. The DL
and UL performance of the NBS cell association is comparable
to that of the conventional MRP cell association despite the
fact that the UL path loss in the NBS cell association is
low. Owing to its simple implementation with low system
overheads, the NBS cell association sounds an optimal choice.
We have shown that although the harvested energy at the
MU can be increased by deploying more small cell BSs
and increasing the BS transmit power, the UL performance
of a random MU in HCNs with the NBS and the MRP
cell associations can not be improved. Nevertheless, the UL
performance of a random MU can be improved by increasing
the power splitting factor. With the advancements in WPT
and interference cancellation, HCNs with SWIPT prove to be
promising candidates for 5G systems.
APP EN DI X A
PROO F OF LE MM A 1
We first derive the average value of Ibb
kin (4), as follows
E[Ibb
k] =EhPt,bb
k|hbb
k,u0|2L0max 
xbb
k,u0
, d−`ki
(a)
=Pt,bb
kL0Zd
0
d−`kf

xbb
k


(x)dx
+Z∞
d
x−`kf

xbb
k


(x)dx,(A.1)
where (a) follows from the fact that |hbb
k|2∼exp(1). In (A.1),
the PDF of 
xbb
k
with the NBS cell association is given by
[31]
f

xbb
k


(x)NBS = 2κx exp{−κx2},(A.2)
where κis given in (13).
Substituting (A.2) into (A.1), and simplifying the resulting
equation using [34, eq. 3.381.1] and [34, eq. 3.381.6], we
derive [Ibb
k]. Further, the average value of Ibxis derived as
E[Ibx] =
K
X
j=1
EhhPt,bjL0|hbju0|2i
Ex"EΦjhX
bj∈Φj\bb
kmax 
xbjuo
, d−`ji#.
(A.3)
The interfering BSs need to be located outside a disc of a
radius rmin =
xbb
k
=xto satisfy the NBS cell association.
Applying the Campbell’s Theorem [33] to (A.3), and utilizing
the fact that |hbju0|2∼1, we derive
E[Ibx] =
K
X
j=1
2πPt,bjLoλj"∞
Z
0h∞
Z
rmin
(max{r, d})−`jrdri
f

xbb
k


(x)dx#.(A.4)
Inserting rmin =xinto (A.4), we have
E[Ibx] =
K
X
j=1
2πPt,bjLoλj"d
Z
0hd−`j
d
Z
x
rdr +
∞
Z
d
r−(`j−1)dri
f

xbb
k


(x)dx +
∞
Z
dh∞
Z
x
r−(`j−1)drif

xbb
k


(x)dx#.
(A.5)
Substituting the PDF of 
xbb
k
with NBS cell association
from (A.2) into (A.5), and solving the resulting equation by
using [34, eq. 3.381.1] and [34, eq. 3.381.6], we obtain E[Ibx].
Combining the equations of E[Ibb
k]and E[Ibx], we obtain the
average received power at the typical kth tier MU with NBS
cell association in (7) as Lemma 1.

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Transactions on Wireless Communications
13
APP EN DI X B
PROO F OF LE MM A 2
We first write the PDF of 
xbb
k
with the MRP cell
association as given by [31]
f

xbb
k


(x)MRP =x
Υk
expn−
K
X
j=1
µk,j x2`k/`jo,(B.1)
where Υkis given in (20)
The average value of Ibb
kis derived by substituting the
PDF of 
xbb
k
with MRP cell association from (B.1) into
(A.1). Further, the average value of Ibxis derived as (A.4)
with the interfering BSs located outside the disc of radius
rmin =δj,k2/`jx`k/`jto satisfy the MRP cell association.
Combining the resulting equations of E[Ibb
k]and E[Ibx], and
finally substituting the PDF of 
xbb
k
with the MRP cell
association from (B.1) we derive the average received power
at the typical kth tier MU with the MRP cell association as
Lemma 2.
APP EN DI X C
PROO F OF TH EO RE M 1
According to (5) and (26), the DL outage probability of the
typical MU in the kth tier is given as
PDL
out,k (β) =1 −
∞
Z
0
Pr "hbb
k,u02
xbb
k,u0
−`k
(IDL
bxj+σ2)ΩDL
k
> β#
f

xbb
k,u0


(x)dx, (C.1)
where ΩDL
kis given in (30), IDL
bx=P
bj∈Φj\bb
k
(1 −
ρ)Pt,bjhbj,u02L0
xbj,u0
−`j, and f

xbb
k


(x)with the NBS
cell association is given in (A.2).
In (C.2) the CCDF of a typical MU at a distance xfrom
its associated BS in kth tier is given as
Pr "hbb
k,uo2
xbb
k,uo
−`k
(IDL
bx+σ2)ΩDL
k
> β#
=EIbxhPr hhbb
k,uo2>(IDL
bx+σ2)βΩDL
k
xbb
k,uo
−`kiIDL
bxi
(a)
=
∞
Z
0
exp n−ΩDL +σ2βΩDL
k
xbb
k,uo
−`ko
dP r IDL
bx≤ΩDL
(b)
= exp n−σ2βΩDL
k
xbb
k,uo
`koLIDL
bxβΩDL
k
xbb
k,u0
−`k,
(C.2)
where (a) follows from the fact that |hbb
ku0|2∼1, and (b) fol-
lows from the definition of Laplace transform of interference
LIDL
bx(s) =
∞
R
x
exp −sΩDLdP r IDL
bx≤ΩDL, where the
integration limit follows from the fact that the nearest interferer
in jth tier is at least at rmin =x. Using generating functional
of HPPP in [33] LIDL
bx(s)is given as
LIDL
bx(s) = exp n2π
K
X
j=1
λj
∞
Z
x1−Eh−s(1 −ρ)Pt,bjhbj,u02
L0
xbj,u0
−`jydyo
(a)
= exp n2π
K
X
j=1
λj
∞
Z
x1−1
1 + fk,j y−`jydyo(C.3)
= exp n−
k
X
j=1
πλjϑk,j o,(C.4)
where (a) follows from the fact that |hbju0|2∼1and fj,k
is given in (31). Simplifying (C.3) by employing change of
variables z=f−2/`j
k,j y2we derive (C.4) where ϑk,j is given
in (29). Substituting (C.4) into (C.2), we derive,
Pr SI N RDL
k
xbb
k,u0
> β=expn−σ2βΩDL
k
xbb
k,uo
`k
−
k
X
j=1
πλjϑk,j o(C.5)
Finally plugging (C.5) and (A.2) into (C.1), we obtain
Theorem 1.
APP EN DI X D
PROO F OF TH EO RE M 2
For the MRP cell association, the Laplace transform in (C.2)
is evaluated with lower integration limit rmin =δj,k2/`jx`k/`j
by utilizing the fact that the nearest interferer in the jth tier
is at least at δj,k2/`jx`k/`j. Then following the similar steps
as of Theorem 1 with the PDF of 
xbb
k
for the MRP cell
association given in (B.1), we derive Theorem 2.
APP EN DI X E
PROO F OF TH EO RE M 3
Based on (53), the DL average ergodic rate of a typical
MU associated with the kth tier using NBS cell association is
derived as
RDL
k=
∞
Z
0
ESI NRD L
kαln 1 + SI N RDL
k(x)f

xbb
k,u0


(x)dx
=
∞
Z
0
∞
Z
0
Pr hSI N RDL
k(x)>(et/α −1)idtf

xbb
k,u0


(x)dx.
(E.1)
Simplifying (E.1) as of (C.5) and substituting (A.2), we
obtain Theorem 3.
APP EN DI X F
PROO F OF TH EO RE M 4
The DL average ergodic rate of a typical MU associated
with the kth tier using MRP cell association is derived by
simplifying (E.1) following the similar steps as of Theorem 2
for the MRP cell association and substituting (B.1).

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Transactions on Wireless Communications
14
REF ER EN CE S
[1] S. Akbar, Y. Deng, A. Nallanathan, and M. Elkashlan, “Downlink
and uplink transmission in K-tier heterogeneous cellular network with
simultaneous wireless information and power transfer,” in IEEE Global
Commun. Conf. (GLOBECOM), Dec. 2015, pp. 1–6.
[2] P. Demestichas, A. Georgakopoulos, D. Karvounas, K. Tsagkaris,
V. Stavroulaki, J. Lu, C. Xiong, and J. Yao, “5G on the horizon: Key
challenges for the radio-access network,” IEEE Trans. Veh. Technol.,
vol. 8, no. 3, pp. 47–53, Sept. 2013.
[3] R. Hu and Y. Qian, “An energy efficient and spectrum efficient wireless
heterogeneous network framework for 5G systems,” IEEE Commun.
Mag., vol. 52, no. 5, pp. 94–101, May 2014.
[4] J. Andrews, S. Buzzi, W. Choi, S. Hanly, A. Lozano, A. Soong, and
J. Zhang, “What will 5G be?” IEEE J. Sel. Areas Commun., vol. 32,
no. 6, pp. 1065–1082, June 2014.
[5] X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “Wireless networks
with RF energy harvesting: A contemporary survey,” IEEE Commun.
Surveys Tuts., vol. 17, no. 2, pp. 757–789, Secondquarter 2015.
[6] S. Bi, C. Ho, and R. Zhang, “Wireless powered communication: op-
portunities and challenges,” IEEE Commun. Mag., vol. 53, no. 4, pp.
117–125, Apr. 2015.
[7] I. Krikidis, S. Timotheou, S. Nikolaou, G. Zheng, D. Ng, and R. Schober,
“Simultaneous wireless information and power transfer in modern com-
munication systems,” IEEE Commun. Mag., vol. 52, no. 11, pp. 104–110,
Nov. 2014.
[8] K. Huang and E. Larsson, “Simultaneous information and power transfer
for broadband wireless systems,” IEEE Trans. Signal Process., vol. 61,
no. 23, pp. 5972–5986, Dec. 2013.
[9] S. Parkvall, A. Furuskar, and E. Dahlman, “Evolution of lte toward imt-
advanced,” IEEE Commun. Mag., vol. 49, no. 2, pp. 84–91, Feb. 2011.
[10] A. Khandekar, N. Bhushan, J. Tingfang, and V. Vanghi, “Lte-advanced:
Heterogeneous networks,” in European Wireless Conference (EW), Apr.
2010, pp. 978–982.
[11] D. Liu, Y. Chen, K. K. Chai, T. Zhang, and M. Elkashlan, “Oppor-
tunistic user association for multi-service hetnets using nash bargaining
solution,” IEEE Commun. Lett., vol. 18, no. 3, pp. 463–466, Mar. 2014.
[12] R. Madan, J. Borran, A. Sampath, N. Bhushan, A. Khandekar, and T. Ji,
“Cell association and interference coordination in heterogeneous LTE-
A cellular networks,” IEEE J. Sel. Areas Commun., vol. 28, no. 9, pp.
1479–1489, Dec. 2010.
[13] A. Ghosh, N. Mangalvedhe, R. Ratasuk, B. Mondal, M. Cudak, E. Vi-
sotsky, T. A. Thomas, J. G. Andrews, P. Xia, H. S. Jo, H. S. Dhillon,
and T. D. Novlan, “Heterogeneous cellular networks: From theory to
practice,” IEEE Commun. Mag., vol. 50, no. 6, pp. 54–64, June 2012.
[14] J. G. Andrews, H. Claussen, M. Dohler, S. Rangan, and M. C. Reed,
“Femtocells: Past, present, and future,” IEEE J. Sel. Areas Commun.,
vol. 30, no. 3, pp. 497–508, Apr. 2012.
[15] H. Ju and R. Zhang, “Throughput maximization in wireless powered
communication networks,” IEEE Trans. Commun., vol. 13, no. 1, pp.
418–428, Jan. 2014.
[16] L. Liu, R. Zhang, and K.-C. Chua, “Multi-antenna wireless powered
communication with energy beamforming,” IEEE Trans. Commun.,
vol. 62, no. 12, pp. 4349–4361, Dec. 2014.
[17] H. Ju and R. Zhang, “User cooperation in wireless powered communi-
cation networks,” in 2014 IEEE Global Commun. Conf. (GLOBECOM),
Dec. 2014, pp. 1430–1435.
[18] X. Kang, C. Ho, and S. Sun, “Full-duplex wireless-powered communi-
cation network with energy causality,” IEEE Trans. Wireless Commun.,
vol. 14, no. 10, pp. 5539–5551, Oct. 2015.
[19] G. Yang, C. K. Ho, R. Zhang, and Y. L. Guan, “Throughput optimization
for massive mimo systems powered by wireless energy transfer,” IEEE
J. Sel. Areas Commun., vol. 33, no. 8, pp. 1640–1650, Aug. 2015.
[20] Y. Liu, S. A. Mousavifar, Y. Deng, C. Leung, and M. Elkashlan,
“Wireless energy harvesting in a cognitive relay network,” IEEE Trans.
Wireless Commun., vol. 15, no. 4, pp. 2498–2508, Apr. 2016.
[21] S. Lee, R. Zhang, and K. Huang, “Opportunistic wireless energy
harvesting in cognitive radio networks,” IEEE Trans. Wireless Commun.,
vol. 12, no. 9, pp. 4788–4799, Sept. 2013.
[22] A. H. Sakr and E. Hossain, “Cognitive and energy harvesting-based D2D
communication in cellular networks: Stochastic geometry modeling and
analysis,” IEEE Trans. Commun., vol. 63, no. 5, pp. 1867–1880, May
2015.
[23] K. Huang and V. K. N. Lau, “Enabling wireless power transfer in
cellular networks: Architecture, modeling and deployment,” CoRR,
2012. [Online]. Available: http://arxiv.org/abs/1207.5640
[24] Y. Deng, L. Wang, M. Elkashlan, M. D. Renzo, and J. Yuan, “K-tier
heterogeneous cellular networks with wireless power transfer,” in IEEE
Int. Commun. Conf. (ICC), May 2016.
[25] A. Sakr and E. Hossain, “Analysis of multi-tier uplink cellular networks
with energy harvesting and flexible cell association,” in IEEE Global
Commun. Conf. (GLOBECOM), Dec. 2014, pp. 4525–4530.
[26] K. Huang and X. Zhou, “Cutting last wires for mobile communication
by microwave power transfer,” CoRR, vol. abs/1408.3198, 2014.
[Online]. Available: http://arxiv.org/abs/1408.3198
[27] H. ElSawy, E. Hossain, and M. Haenggi, “Stochastic geometry for
modeling, analysis, and design of multi-tier and cognitive cellular
wireless networks: A survey,” IEEE Commun. Surveys and Tutorials,
vol. 15, no. 3, pp. 996–1019, 2013.
[28] D. Liu, L. Wang, Y. Chen, M. Elkashlan, K. K. Wong, R. Schober, and
L. Hanzo, “User association in 5g networks: A survey and an outlook,”
IEEE Commun. Surveys Tuts., vol. PP, no. 99, pp. 1–1, 2016.
[29] A. Sakr and E. Hossain, “Analysis of K-tier uplink cellular networks with
ambient RF energy harvesting,” IEEE J. Sel. Areas Commun., vol. 33,
no. 10, pp. 2226–2238, Oct. 2015.
[30] F. Boccardi, J. Andrews, H. Elshaer, M. Dohler, S. Parkvall, P. Popovski,
and S. Singh, “Why to decouple the uplink and downlink in cellular
networks and how to do it,” IEEE Commun. Mag., vol. 54, no. 3, pp.
110–117, Mar. 2016.
[31] H.-S. Jo, Y. J. Sang, P. Xia, and J. G. Andrews, “Heterogeneous cellular
networks with flexible cell association: A comprehensive downlink SINR
analysis,” IEEE Trans. Wireless Commun., vol. 11, no. 10, pp. 3484–
3495, Oct. 2012.
[32] F. Baccelli, B. Blaszczyszyn, and P. Muhlethaler, “An aloha protocol for
multihop mobile wireless networks,” IEEE Trans. Inf. Theory, vol. 52,
no. 2, pp. 421–436, Feb. 2006.
[33] D. Stoyan, W. Kendall, and J. Mecke, “Stochastic geometry and its
applications,” Wiley New York, vol. 2, 1987.
[34] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series and
Products, 7th ed. San Diego, C.A.: Academic Press, 2007.
Sunila Akbar (S’15) received the B.E degree in
Electrical Engineering from NED University of
Engg. and Tech. (NEDUET), Karachi, Pakistan, in
1998. She then worked as a Projects Coordinator
for the Electrical Division at a local Contracting
Company in Dubai, UAE, for five years before
joining NEDUET as a Lecturer in 2004. In 2007, she
completed M.Engg degree in Telecommunications
Engineering from NEDUET and appointed as an
Assistant Professor at the same in 2008. She is
currently working towards the PhD degree in the
Department of Informatics, King’s College London, UK. She is the recipient
of the prestigious Commonwealth Scholarship, UK for PhD studies. Ms.
Akbar has been a reviewer in several IEEE conferences. Her current research
interests include statistical modeling of wireless networks, heterogeneous
cellular networks, massive MIMO, and energy efficient communications.
Yansha Deng (M’16) received the Ph.D. degree
in Electrical Engineering from Queen Mary Uni-
versity of London, UK, 2015. She is currently the
postdoctoral research fellow in the Department of
Informatics, at King’s College London, UK.
Her research interests include massive MIMO,
HetNets, molecular communication, cognitive radio,
cooperative networks, and physical layer security.
She has served as TPC member for many IEEE
conferences such as IEEE GLOBECOM and ICC.

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TWC.2016.2570209, IEEE
Transactions on Wireless Communications
15
Arumugam Nallanathan (S’97–M’00–SM’05) is a
Professor of Wireless Communications in the De-
partment of Informatics at King’s College London
(University of London). He served as the Head
of Graduate Studies in the School of Natural and
Mathematical Sciences at King’s College London,
2011–2012. He was an Assistant Professor in the
Department of Electrical and Computer Engineering,
National University of Singapore from August 2000
to December 2007. His research interests include
5G Technologies, Millimeter wave communications,
Cognitive Radio and Relay Networks. In these areas, he co-authored more than
250 papers. He is a co-recipient of the Best Paper Award presented at the 2007
IEEE International Conference on Ultra-Wideband (ICUWB’2007). He is an
IEEE Distinguished Lecturer. He is an Editor for IEEE TRANSACTIONS
ON COMMUNICATIONS and IEEE TRANSACTIONS ON VEHICULAR
TECHNOLOGY. He was an Editor for IEEE TRANSACTIONS ON WIRE-
LESS COMMUNICATIONS (2006–2011), IEEE WIRELESS COMMUNI-
CATIONS LETTERS and IEEE SIGNAL PROCESSING LETTERS. He
served as the Chair for the Signal Processing and Communication Elec-
tronics Technical Committee of IEEE Communications Society, Technical
Program Co-Chair (MAC track) for IEEE WCNC 2014, Co-Chair for the
IEEE GLOBECOM 2013 (Communications Theory Symposium), Co-Chair
for the IEEE ICC 2012 (Signal Processing for Communications Sympo-
sium), Co-Chair for the IEEE GLOBECOM 2011 (Signal Processing for
Communications Symposium), Technical Program Co-Chair for the IEEE
International Conference on UWB 2011 (IEEE ICUWB 2011), Co-Chair
for the IEEE ICC 2009 (Wireless Communications Symposium), Co-Chair
for the IEEE GLOBECOM 2008 (Signal Processing for Communications
Symposium) and General Track Chair for IEEE VTC 2008. He received the
IEEE Communications Society SPCE outstanding service award 2012 and
IEEE Communications Society RCC outstanding service award 2014.
Maged Elkashlan (M’06) received the Ph.D. de-
gree in Electrical Engineering from the University
of British Columbia, Canada, 2006. From 2007 to
2011, he was with the Wireless and Networking
Technologies Laboratory at Commonwealth Scien-
tific and Industrial Research Organization (CSIRO),
Australia. During this time, he held an adjunct ap-
pointment at University of Technology Sydney, Aus-
tralia. In 2011, he joined the School of Electronic
Engineering and Computer Science at Queen Mary
University of London, UK. He also holds visiting
faculty appointments at the University of New South Wales, Australia, and
Beijing University of Posts and Telecommunications, China. His research
interests fall into the broad areas of communication theory, wireless com-
munications, and statistical signal processing for distributed data processing,
heterogeneous networks, and Massive MIMO.
Dr. Elkashlan currently serves as Editor of IEEE TRAN SAC TI ONS O N
WIRELESS COM MUN IC ATION S, IEEE T RAN SAC TI ONS O N VEH ICU LA R
TECHNOLOGY, and IE EE CO MM UNI CATI ON S LETT ERS . He also serves as
Lead Guest Editor for the special issue on “Green Media: The Future of
Wireless Multimedia Networks” of the IEE E WIRELESS COM MUN IC ATIO NS
MAGA ZIN E, Lead Guest Editor for the special issue on “Millimeter Wave
Communications for 5G” of the IE EE COMM UN ICATI ON S MAGA ZIN E, Guest
Editor for the special issue on “Energy Harvesting Communications” of the
IEEE COM MUN IC ATION S MAGA ZI NE, and Guest Editor for the special issue
on “Location Awareness for Radios and Networks” of the IE EE JOUR NAL O N
SEL ECT ED AR EAS I N COMM UN ICAT ION S. He received the Best Paper Award
at the IEEE International Conference on Communications (ICC) in 2014,
the International Conference on Communications and Networking in China
(CHINACOM) in 2014, and the IEEE Vehicular Technology Conference
(VTC-Spring) in 2013. He received the Exemplary Reviewer Certificate of
the IEEE Communications Letters in 2012.
Abdol-Hamid Aghvami (M’89–SM’91–F’05) is
a Professor of telecommunications engineering at
King’s College London. He has published over 600
technical papers and given invited talks and courses
worldwide on various aspects of personal and mobile
radio communications. He was Visiting Professor at
NTT Radio Communication Systems Laboratories in
1990, Senior Research Fellow at BT Laboratories
in 1998–1999, and was an Executive Advisor to
Wireless Facilities Inc., USA, in 1996–2002. He
was a member of the Board of Governors of the
IEEE Communications Society in 2001–2003, was a Distinguished Lecturer
of the IEEE Communications Society in 2004–2007, and has been member,
Chairman, and Vice-Chairman of the technical programme and organising
committees of a large number of international conferences. He is also
the founder of International Symposium on Personal Indoor and Mobile
Radio Communications (PIMRC), a major yearly conference attracting nearly
1000 attendees. Dr. Aghvami was awarded the IEEE Technical Committee
on Personal Communications (TCPC) 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, Fellow of the IET, and in 2009 was awarded
a Fellowship of the Wireless World Research Forum in recognition of his
personal contributions to the wireless world.