Wireless Power Transfer in Massive MIMO-Aided HetNets With User ...

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Wireless Power Transfer in Massive MIMO-Aided HetNets With User Association
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 64, NO. 10, OCTOBER 2016 4181
Wireless Power Transfer in Massive MIMO-Aided
HetNets With User Association
Yongxu Zhu, Lifeng Wang, Member, IEEE, Kai-Kit Wong, Fellow, IEEE,
Shi Jin, Member, IEEE, and Zhongbin Zheng
Abstract—This paper explores the potential of wireless
power transfer (WPT) in massive multiple-input multiple-
output (MIMO)-aided heterogeneous networks (HetNets), where
massive MIMO is applied in the macrocells, and users aim to
harvest as much energy as possible and reduce the uplink path
loss for enhancing their information transfer. By addressing the
impact of massive MIMO on the user association, we compare
and analyze user association schemes: 1) downlink received
signal power (DRSP)-based approach for maximizing the har-
vested energy and 2) uplink received signal power (URSP)-based
approach for minimizing the uplink path loss. We adopt the linear
maximal-ratio transmission beamforming for massive MIMO
power transfer to recharge users. By deriving new statistical
properties, we obtain the exact and asymptotic expressions for
the average harvested energy. Then, we derive the average uplink
achievable rate under the harvested energy constraint. Numerical
results demonstrate that the use of massive MIMO antennas can
improve both the users’ harvested energy and uplink achievable
rate in the HetNets; however, it has negligible effect on the
ambient RF energy harvesting. Serving more users in the massive
MIMO macrocells will deteriorate the uplink information trans-
fer because of less harvested energy and more uplink interfer-
ence. Moreover, although DRSP-based user association harvests
more energy to provide larger uplink transmit power than the
URSP-based one in the massive MIMO HetNets, URSP-based
user association could achieve better performance in the uplink
information transmission.
Index Terms—Energy harvesting, heterogeneous network
(HetNet), massive MIMO, user association, wireless power
transfer.
I. INTRODUCTION
T
RADITIONAL energy harvesting sources such as solar,
wind, and hydroelectric power highly depend upon time
and locations, as well as the conditions of the environ-
ments. Wireless power transfer (WPT) in contrast is a much
Manuscript received February 9, 2016; revised May 30, 2016 and
July 9, 2016; accepted July 14, 2016. Date of publication July 27, 2016;
date of current version October 14, 2016. This work was supported by the
U.K. Engineering and Physical Sciences Research Council (EPSRC) under
Grant EP/M016005/1. The work of S. Jin was supported by the National
Natural Science Foundation of China under Grant 61531011. The associate
editor coordinating the review of this paper and approving it for publication
was S. Durrani. (Corresponding author: Lifeng Wang.)
Y. Zhu, L. Wang, and K.-K. Wong are with the Department of Electronic
and Electrical Engineering, University College London, London WC1E
6BT, U.K. (e-mail: yongxu.zhu.13@ucl.ac.uk; lifeng.wang@ucl.ac.uk;
kai-kit.wong@ucl.ac.uk).
S. Jin is with the National Mobile Communications Research Laboratory,
Southeast University, Nanjing 210096, China (e-mail: jinshi@seu.edu.cn).
Z. Zheng is with the East China Institute of Telecommunications, China
Academy of Information and Communications Technology, Shanghai 200001,
China (e-mail: ben@ecit.org.cn).
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/TCOMM.2016.2594794
more controllable approach to prolong the lifetime of mobile
devices [1]–[3]. Additionally, the potentially harmful interfer-
ence received by the energy harvester can actually become a
useful energy source. Recently, the potential of harvesting the
ambient energy in the fifth-generation (5G) networks has been
studied in [4]–[6].
Heterogeneous networks (HetNets) are identified as one of
the key enablers for 5G, e.g., [4], [7]. In HetNets, small cells
are densely deployed [7], [8], which shortens the distances
between the mobile devices and the base stations (BSs).
Recently, there is an interesting integration between WPT
and HetNets, suggesting that stations, referred to as power
beacons (PBs), can be deployed in cellular networks for
powering users via WPT [2]. In [9] and [10], the optimal
placement of power beacons in the cellular networks has been
investigated.
Recent attempts have been to understand the feasibility of
WPT in cellular networks, device-to-device (D2D) commu-
nications and sensor networks. In particular, both picocell
BSs and energy towers (or PBs) were considered in [11]
to transfer energy to the users, and their problem was to
jointly maximize the received energy and minimize the number
of active picocell BSs and PBs. Subsequently in [12], user
selection policies in dedicated RF-powered uplink cellular
networks were investigated, where the BSs acted as dedi-
cated power sources. Further, [13] studied a K -tier uplink
cellular network with energy harvesting, where the cellular
users harvested the RF energy from the concurrent downlink
transmissions in all network tiers. Then [14] studied the
D2D scenario in which the cognitive transmitters harvested
energy from the interference to support the communica-
tion. As mentioned in [15], however, ambient RF energy
harvesting is sufficient only for powering low-power sen-
sors with sporadic activities, and dedicated energy source is
required for powering mobile devices such as smartphones.
As such, [16] turned the attention to the case, where D2D
transmitters harvested energy from the PBs, and proposed
several power transfer policies. In [17], battery-free sensor
node harvested energy from the access point and ambient
RF transmitters based on the power splitting architecture,
and the locations of RF transmitters were modeled using
Ginibre α-DPP.
On the other hand, massive multiple-input multiple-
output (MIMO) systems, using a large number of antennas at
the BSs, achieve ultra-high spectral efficiency by accommo-
dating a large number of users in the same radio channel [18].
For massive MIMO to become reality, there are still some
This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
4182 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 64, NO. 10, OCTOBER 2016
issues such as high circuit power consumption [7], which
need to be addressed. The exceptional spatial selectivity of
massive MIMO means that very sharp signal beams can be
formed [19], [20] and of great importance to WPT. Motivated
by this, [21] studied the wireless information and power
transfer in a point-to-point (P2P) system including a single-
antenna user and its serving BS equipped with large antenna
array, where energy efficiency for uplink information transfer
was maximized under the quality-of-service (QoS) constraint.
Later in [22], a receiver with large number of antennas was
assumed to harvest energy from a single-antenna transmitter
and a single-antenna interferer, and an algorithm was proposed
to maximize the data rate while guaranteeing a minimum har-
vested energy with a large receive antenna array using antenna
partitioning. In contrast to [21]–[23] considered the uplink
throughput optimization in a single massive MIMO powered
cell, where an access point equipped with a large antenna
array transfers energy to multiple users. The opportunities and
challenges of deploying a massive number of distributed anten-
nas for WPT was discussed in [24]. In addition, the shorter
wavelengths at the mmWave frequencies enable mmWave BSs
to pack more antennas for achieving large array gains. Hence
recent research works such as [25] and [26] also studied
WPT in mmWave cellular networks. Particularly, in [25],
the mmWave antenna beam was characterized by using the
sectored antenna model and the energy coverage probability
was evaluated. In [26], uniform linear array (ULA) with analog
beamforming was considered for WPT in mmWave cellular
networks. Different from [25] and [26], this paper focuses on
massive MIMO enabled wireless power transfer with digital
beamforming in the conventional cellular bands, which will
be detailed later.
Regarded as a promising network architecture to meet the
increasing demand for mobile data, massive MIMO empow-
ered HetNets have recently attracted much attention [27]–[31].
In [27], downlink beamforming design for minimizing the
power consumption was investigated in a single massive
MIMO enabled macrocell overlaid with multiple small cells,
and it was shown that total power cost can be signifi-
cantly reduced while satisfying the QoS constraints. Moti-
vated by these research efforts, in this paper, we explore
the potential benefits of massive MIMO HetNets for wireless
information and power transfer (WPT and wireless infor-
mation transfer (WIT)), which is novel and has not been
conducted yet.
Different from the aforementioned literature such
as [21]–[23] where WPT and WIT were only considered
in a single cell, we study massive MIMO antennas being
harnessed in the macrocells, and employ a stochastic geometry
approach to model the K -tier HetNets. In particular, users
first harvest energy from downlink WPT, and then use the
harvested energy for WIT in the uplink. In this scenario,
user association determines whether a user is associated
with a particular base station for downlink WPT in such
networks, and therefore it is crucial to study the effect of user
association on WPT. The work of [13] considered that users
relied on ambient RF energy harvesting, and only studied the
effect of user association on uplink information transmission.
User association in massive MIMO HetNets has been recently
investigated for optimizing the throughput [28]–[30] and
energy efficiency [31]. The effect of using different user
association methods on WPT in such networks is unknown.
Hence we examine the effect of user association on the
WPT and WIT in massive MIMO HetNets by considering
two user association methods: (1) downlink received signal
power (DRSP) based for maximum harvested energy, and
(2) uplink received signal power (URSP) based for minimum
uplink path loss. One of our aims is to find out which scheme
is better for uplink WIT. In this paper, we have made the
following contributions:
• We develop an analytical framework to examine the
implementation of downlink WPT and uplink WIT in
massive MIMO aided HetNets with stochastic geomet-
ric model. As the intra-tier interference is the source
of energy, interference avoidance is not required and
maximal-ratio transmission (MRT) beamforming is used
for WPT for multiple users in the macrocells.
• We investigate the impacts of massive MIMO on the
user association of the HetNets, and examine both DRSP-
based and URSP-based algorithms by deriving the exact
and asymptotic expressions for the probability of a user
associated with a macrocell or a small cell in the HetNet.
• We derive the exact and asymptotic expressions for
the average harvested energy when users are equipped
with large energy storage. We show that the asymp-
totic expressions can well approximate the exact ones.
The implementation of massive MIMO can significantly
increase the harvested energy in the HetNets, since it
provides larger power gain for users served in the macro-
cells, and enables that users with higher received power
are offloaded to the small cells.
1
In addition, DRSP-
based user association scheme outperforms URSP-based
in terms of harvested energy, which means that it sup-
ports higher user transmit power for uplink information
transmission.
• We derive the average uplink achievable rate supported
by the harvested energy. Our results demonstrate that the
uplink performance is enhanced by increasing the number
of antennas at the macrocell BS, but serving more users
in the macrocells decreases the average achievable rate
because of lower uplink transmit power and more severe
uplink interference. For the case of dense small cells,
it can still be interference-limited in the uplink. Fur-
thermore, although DRSP-based user association scheme
harvests more energy to provide larger uplink transmit
power, URSP-based can achieve better WIT performance
in the uplink.
The notation of this paper is shown in Table I.
II. N
ETWORK DESCRIPTION
This paper considers a K -tier time-division duplex (TDD)
HetNet including macrocells and small cells such as picocells
and relays, etc. Each user first harvests the energy from its
serving BS (as a dedicated RF energy source) in the downlink,
1
Note that power gain is also referred to as array gain in the literature.
ZHU et al.: WPT IN MASSIVE MIMO-AIDED HetNets WITH USER ASSOCIATION 4183
TABLE I
N
OTATION
and uses the harvested energy for WIT in the uplink. Let
T be the duration of a communication block. The first and
second sub-blocks of duration τ T and
(
1 − τ
)
T are allocated
to the downlink WPT and uplink WIT, respectively, where
τ
(
0 ≤ τ ≤ 1
)
is the time allocation factor. We assume that
the first tier represents the class of macrocell BSs (MBSs),
each of which is equipped with a large antenna array [32].
The locations of the MBSs are modelled using a homogeneous
Poisson point process (HPPP)
M
with density λ
M
[33]. The
locations of the small-cell (such as micro/picocell, femtocell,
etc.) BSs (SBSs) in the i-th tier (i = 2,...,K ) are modelled
by an independent HPPP
i
with density λ
i
. It is assumed
that the density of users is much greater than that of BSs so
that there always will be one active mobile user at each time
slot in every small cell and hence multiple active mobile users
in every macrocell.
2
In the macrocell, S single-antenna users
communicate with an N-antenna MBS (assuming N S ≥ 1)
in the uplink over the same time slot and frequency band.
3
In the small cell, only one single-antenna user is allowed
to communicate with a single-antenna SBS at a time slot.
We assume that perfect channel state information (CSI) is
known at the BS,
4
and the effect of pilot contamination on
channel estimation is omitted. As mentioned in [7] and [35],
pilot contamination is a relatively secondary factor for all but
colossal numbers of antennas, and various methods to mitigate
pilot contamination via low-intensity base station coordination
have already been proposed in the literature such as [35].
In addition, universal frequency reuse is employed such that all
of the tiers share the same bandwidth and all the channels are
assumed to undergo independent identically distributed (i.i.d.)
quasi-static Rayleigh block fading.
2
In reality, there may be more than one active users in a small cell and this
can be dealt with using multiple access techniques.
3
We note that in [14], the probability mass function of the number of users
served by a generic BS was derived by approximating the area of a Voronoi
cell via a gamma-distributed random variable. However, the result in [14]
cannot be applied in this paper, since the Euclidean plane is not divided into
Voronoi cells based on the considered user association methods. We highlight
that it is an important work to study the case of the dynamic S following a
certain distribution in less-dense scenarios.
4
In the practical TDD massive MIMO systems, the downlink CSI can be
obtained through channel reciprocity based on uplink training.
A. User Association
We introduce two user association algorithms: (1) a user is
associated with the BS based on the maximum DRSP at the
user, which results in the largest average received power; and
(2) a user is associated with the BS based on the maximum
URSP at the BS, which will minimize the power loss of user’s
signal during the propagation.
5
Considering the effect of massive MIMO, the average
received power at a user that is connected with the -th MBS
( ∈
M
) can be expressed as
P
r,
= G
D
a
P
M
S
L
X
,M
, (1)
where G
D
a
denotes the power gain obtained by the user
associated with the MBS, P
M
is the MBS’s transmit power,
L
X
,M
= β
X
,M
−α
M
is the path loss function, β is
the frequency dependent constant value,
X
,M
denotes the
distance, and α
M
is the path loss exponent. In the small cell,
the average received power at a user that is connected with
the j-th SBS ( j ∈
i
)inthei-th tier is expressed as
P
r,i
= P
i
L
X
j,i
, (2)
where P
i
denotes the SBS’s transmit power in the i-th tier and
as above L
X
j,i
= β
X
j,i
−α
i
is the path loss function
with distance
X
j,i
and path loss exponent α
i
.
For DRSP-based user association, the aim is to maximize
the average received power. Thus, the serving BS for a typical
user is selected according to the following criterion:
BS : arg max
k∈
{
M,2,...,K
}
P
∗
r,k
, (3)
where
P
∗
r,M
= max
∈
M
P
r,
, and P
∗
r,i
= max
j∈
i
P
r,i
. (4)
By contrast, for URSP-based user association, the objective
is to minimize the uplink path loss, and as such, the serving
BS for a typical user is selected by
BS : arg max
k∈
{
M,2,...,K
}
L
∗
(
|
X
k
|
)
, (5)
where
L
∗
(
|
X
M
|
)
= G
U
a
max
∈
M
L
X
,M
, (6)
L
∗
(
|
X
i
|
)
= max
j∈
i
L(
X
j,i
). (7)
Here, G
U
a
is the power gain of the serving MBS and L
∗
(
|
X
M
|
)
can be viewed as compensated path loss due to the power gain.
B. Downlink WPT Model
For wireless energy harvesting, the RF signals are inter-
preted as energy. Therefore, in the massive MIMO macrocell,
we adopt the simplest linear MRT beamforming
6
to direct the
5
Although user association for the downlink and uplink can be decoupled
to maximize both the DRSP and URSP, the main drawback for the decoupled
access is that channel reciprocity in massive MIMO systems will be lost [36].
6
Since there is no interference concern in the downlink power trans-
fer, other beamforming methods involving interference mitigation such as
zero-forcing (ZF) will reduce power gain and increase the power consumption
of the MBS.
参数B未提及

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