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Antenna Selection and Power All
资料介绍
Massive MIMO is one of the key technologies in future 5G communications which can satisfy the requirement of high speed and large capacity. This paper considers antenna selection and power allocation design to promote energy conservation then provide good quality of service (QoS) for the whole massive MIMO uplink network. Unlike pre-vious related works, hardware impairment, transmission efficiency, and energy consump-tion at the circuit and antennas are involved in massive MIMO networks. In order to ensure the QoS, we consider the minimum rate con-straint for each user and the system, which increases the complexity of power allocation problem for maximizing energy and spectral efficiency in massive MIMO system. To this end, a quantum-inspired social emotional op-timization (QSEO) algorithm is proposed to obtain the optimal power control strategy in massive MIMO uplink networks. Simulation results assess the great advantages of QSEO which previous strategies do not have.
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| 1575721095Antenna_Selection_and_Power_Allocation_Design_for_5G_Massive_MIMO_Uplink_Networks.pdf | 1M |
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（完整内容请下载后查看）COMMUNICATIONS THEORIES & SYSTEMS
Antenna Selection and Power Allocation Design for
5G Massive MIMO Uplink Networks
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Hongyuan Gao1, , Yumeng Su , Shibo Zhang , Ming Diao
*
1 College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
* The corresponding author, email:
Abstract: Massive MIMO is one of the key
technologies in future 5G communications
which can satisfy the requirement of high
speed and large capacity. This paper considers
antenna selection and power allocation design
to promote energy conservation then provide
good quality of service (QoS) for the whole
massive MIMO uplink network. Unlike pre-
vious related works, hardware impairment,
transmission efficiency, and energy consump-
tion at the circuit and antennas are involved in
massive MIMO networks. In order to ensure
the QoS, we consider the minimum rate con-
straint for each user and the system, which
increases the complexity of power allocation
problem for maximizing energy and spectral
efficiency in massive MIMO system. To this
end, a quantum-inspired social emotional op-
timization (QSEO) algorithm is proposed to
obtain the optimal power control strategy in
massive MIMO uplink networks. Simulation
results assess the great advantages of QSEO
which previous strategies do not have.
wireless networks, the carbon emissions and
operating costs caused by wireless commu-
nication are increasing day by day [1]. Now-
adays, promoting energy conservation then
building intensive community has become a
heated research topic [2-4]. Therefore, careful
planning and deployment of the base station
(BS) infrastructure is necessary to decrease
the energy consumption in line with green
objectives [5-7]. As a key technology of 5G
communication system, massive MIMO has
received substantial attention in both academic
and industry domain since 2010 [8-12]. Com-
pared to traditional communication networks,
massive MIMO has great advantages of higher
data rate, larger capacity, lower latency and
greater throughput [13].
Large number of antennas at the base sta-
tion is the major characteristic of massive
MIMO compared with conventional MIMO
technology [14]. The promising technology
has been studied in many aspects [15-22].
Distributed MIMO has been illustrated to be
capable of significantly increasing the sys-
tem capacity [15]. In [16], the author studied
the sum rate in different signal-to-noise ratio
(SNR) conditions and illustrated how dense
multiple antenna arrays can be designed in
massive MIMO system. In [17], a coordination
approach was proposed to reduce the negative
influence of pilot contamination on channel es-
Keywords: 5G; massive MIMO; antenna se-
lection; power allocation; quantum-inspired
social emotional optimization
I. INTRODUCTION
Received: Apr. 19,2018
Revised: Aug. 23, 2018
Editor: Da Chen
With the increasing demands of high-speed
communication and rapid development of
China Communications • April 2019
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timation. Importantly, the author demonstrated
that the effect of pilot contamination can be
completely vanished under certain conditions
on the channel covariance. To reduce the com-
putational complexity of channel estimation,
[18] decomposed the space of the received
signals into three subspaces according to fac-
tor analysis, and an interference-free subspace
was created to obtain accurate channel esti-
mation. In [19], a joint pilot design and power
allocation strategy was considered to mitigate
pilot contamination and provide good service
in multi-cell massive MIMO system, which
can be used as a benchmark for pilot design
in ideal or non-ideal hardware scenarios. Due
to the high hardware complexity, linear pro-
cessing methods are widely used in massive
MIMO system [20]. In [21], a low-complexity
hybrid precoding method named phased-ZF
(PZF) was proposed to approach the perfor-
mance of the optimal linear precoding scheme
in massive MIMO systems. In [22], two inter-
ference-suppressed precoding methods were
proposed and significantly suppressed the
mutual interference between the users with
statistical and imperfect instantaneous channel
state information (CSI). Pilot-based channel
estimation can be avoided by utilizing statisti-
cal CSI.
author of [25] proposed an antenna selection
strategy based on the theory of rectangular
maximum volume submatrices. However, this
strategy is invalid if a square matrix with max-
imum-volume is not given.
A quantum-inspired
social emotional op-
timization (QSEO)
algorithm is proposed
to obtain the optimal
power control strategy
in massive MIMO up-
link networks.
Due to the growing demand of environ-
mental protection, energy efficiency (EE) and
spectral efficiency (SE) have become two
important concerns in massive MIMO net-
works [26-28]. Power allocation is an essential
technique to enhance the system performance
and promote energy conservation. To further
exploit the benefits of power allocation, more
and more works have been proposed for pow-
er allocation in massive MIMO networks [29-
32]. For maximizing the achievable uplink
rate in multi-cell massive MIMO systems, a
pilot power allocation strategy was proposed
in [29]. Considering pilot allocation, hardware
impairments and other system parameters, the
resource allocation problem for maximizing
SE in multi-cell massive MIMO system was
discussed in [30]. In [31], an approximate
power allocation scheme for maximizing EE
was proposed in massive MIMO networks.
The author considered power amplifier effi-
ciency, transmission power and circuit pow-
er. To ensure the QoS of the whole massive
MIMO system, minimum rate constraint of
the system was considered in [32]. The paper
developed a unified framework for EE op-
timization and proposed a power allocation
method based on fractional programming the-
ory. However, all schemes mentioned above
could not guarantee the QoS for each user and
the system simultaneously. Especially in more
complex conditions, it’s hard for mathematical
approximation to obtain the optimal solution.
With the dramatic increase of antenna quan-
tity, antenna selection is an effective technique
to decrease the operating cost and the number
of radio frequency chains in massive MIMO
networks. It’s impractical to obtain the opti-
mal antenna selection by exhaustive search
because of the high computational complexity.
Some researches on suboptimal antenna se-
lection methods are emerged in recent years
[23-25]. In [23], a successive removal strategy
for antenna selection was proposed. The suc-
cessive elimination strategy was performed
according to the received channel coefficients
from the previous users. Considering the trade-
off between the performance and computation-
al complexity, a mixed-integer programming
approach was proposed to jointly optimize
antenna selection and precoding scheme [24].
In order to maximize the channel capacity, the
1.1 Motivation and contributions
In this paper, we propose an effective anten-
na selection and power allocation strategy to
optimize the EE and SE in massive MIMO
uplink networks. To reduce the computation
complexity and satisfy the changing require-
ments of the practical system in real time, an
effective antenna selection strategy is designed
to select a group of antennas from the avail-
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China Communications • April 2019
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