毫米波大规模MIMO系统中的能效优化功率分配

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"本文探讨了毫米波大规模MIMO系统中基于非正交多址接入(NOMA)的能量效率(EE)问题。针对多个用户的信道相关性和增益差异，设计了多用户簇。为此，我们提出了一种混合模拟/数字预编码方案，适用于基站的低射频链结构。在此基础上，我们构建了一个旨在在满足用户服务质量要求和每个簇的功率约束下最大化EE的功率分配问题。我们提出了一种迭代算法来求解最优功率分配。仿真结果表明，所提出的NOMA方案在EE性能上优于传统的正交多址(OMA)方案。关键词：大规模MIMO，毫米波，混合预编码，能量效率，NOMA。" 毫米波大规模多输入多输出(MIMO)系统是下一代无线通信的关键技术之一，因其能提供更高的数据速率和更宽的带宽而备受关注。然而，随着系统规模的扩大，能源消耗成为了一个不容忽视的问题。因此，提高系统的能量效率成为了研究的热点。非正交多址接入(NOMA)作为一种新兴的多址接入技术，通过允许不同用户在同一时频资源上共享，能够在有限的频谱资源中实现更高的用户容量。与传统的正交多址(OMA)相比，NOMA可以更好地利用功率域，从而有可能提高系统整体的能源效率。本研究聚焦于在毫米波大规模MIMO系统中采用NOMA的情况。由于毫米波的短波长特性，使得天线阵列可以在小尺寸设备上实现，但同时也带来了较高的路径损耗和多径衰落。为解决这一问题，研究者提出了一个混合模拟/数字预编码方案。这种预编码策略考虑了基站的低射频链结构，旨在降低硬件复杂度和功耗，同时保持良好的信号传输性能。文章提出了一种功率分配问题，其目标是在满足每个用户的服务质量和集群功率限制的条件下，最大化系统的能量效率。为了解决这个问题，作者设计了一种迭代算法，该算法能够逐步调整各用户的功率分配，以达到优化EE的目标。通过仿真，研究证明了采用NOMA的系统在能源效率上明显优于传统的OMA系统。这项工作为毫米波大规模MIMO系统的能源效率优化提供了新的视角，并为NOMA技术在实际系统中的应用提供了理论支持。其提出的混合预编码和功率分配策略对于未来无线通信网络的绿色可持续发展具有重要的实践意义。

782 IEEE WIRELESS COMMUNICATIONS LETTERS, VOL. 6, NO. 6, DECEMBER 2017

Energy-Efﬁcient Power Allocation in Millimeter Wave Massive

MIMO With Non-Orthogonal Multiple Access

Wanming Hao, Ming Zeng, Zheng Chu, Member, IEEE, and Shouyi Yang

Abstract—In this letter, we investigate the energy efﬁ-

ciency (EE) problem in a millimeter wave massive MIMO system

with non-orthogonal multiple access (NOMA). Multiple two-user

clusters are formulated according to their channel correlation

and gain difference. Following this, we propose a hybrid ana-

log/digital precoding scheme for the low radio frequency chains

structure at the base station. On this basis, we formulate a power

allocation problem aiming to maximize the EE under users’

quality of service requirements and per-cluster power constraint.

An iterative algorithm is proposed to obtain the optimal power

allocation. Simulation results show that the proposed NOMA

scheme achieves superior EE performance than conventional

OMA scheme.

Index Terms—Massive MIMO, millimeter wave, hybrid

precoding, energy efﬁciency, NOMA.

I. INTRODUCTION

OWER domain non-orthogonal multiple access (NOMA)

has been recognized as a promising candidate for next

generation wireless communication systems [1]. By apply-

ing superposition coding and successive interference cancel-

lation (SIC), NOMA allows multiple users to access the

same time-frequency resource, leading to further increase

in the spectral efﬁciency (SE) compared with orthogonal

multiple access (OMA) [2]. Recently, MIMO has been applied

to NOMA (MIMO-NOMA) systems to further increase

SE [3]–[7]. In a MIMO-NOMA system, users are paired into

clusters, with users in each cluster sharing the same beam-

forming [3]–[6]. Reference [3] proposes a user clustering and

power allocation algorithm to maximize the sum capacity.

In [4], a low-feedback NOMA scheme is proposed for a mas-

sive MIMO (mMIMO) system, in which the performance of

two scenarios, namely perfect user ordering and one-bit feed-

back, is evaluated under the proposed scheme. Reference [6]

jointly investigates user clustering, beamforming design and

power allocation problem to maximize the capacity.

Manuscript received June 17, 2017; revised July 23, 2017; accepted

August 13, 2017. Date of publication August 18, 2017; date of current version

December 15, 2017. This work was supported in part by the Special Project

for Inter-Government Collaboration of State Key Research and Development

Program under Grant 2016YFE0118400, and in part by the National Natural

Science Foundation of China under Grant U1604159. The associate editor

coordinating the review of this paper and approving it for publication was

K. W. Choi. (Corresponding author: Wanming Hao.)

W. Hao is with the School of Information Engineering, Zhengzhou

University, Zhengzhou 450001, China, and also with Kyushu University,

Fukuoka 819-0395, Japan (e-mail: wmhao@hotmail.com).

M. Zeng is with the Faculty of Engineering and Applied Science, Memorial

University, St. Johns, NL A1B 3X9, Canada (e-mail: mzeng@mun.ca).

Z. Chu is with the Faculty of Science and Technology, Middlesex

University, London NW4 4BT, U.K. (e-mail: z.chu@mdx.ac.uk).

S. Yang is with the School of Information Engineering, Zhengzhou

University, Zhengzhou 450001, China (e-mail: iesyyang@zzu.edu.cn).

Digital Object Identiﬁer 10.1109/LWC.2017.2741493

Fig. 1. Cluster-based downlink mmWave mMIMO-NOMA system model.

However, the above works mainly focus on the SE of the

system. With energy efﬁciency (EE) becoming one of the

major concerns for 5G, more efforts need to be paid to its

study. So far, only a few works have studied NOMA with

the perspective of EE [7], [8]. Sun et al. [7] investigate the

EE maximization problem for two users and propose a near-

optimal power allocation scheme, whereas [8] considers the

EE maximization problem under SISO channel.

Different from previous works, in this letter, we investi-

gate the EE optimization problem in a downlink millimeter

wave (mmWave) mMIMO-NOMA system. Indeed, the use of

NOMA in mmWave is preferable because users’ channels can

be highly correlated due to the highly directional feature of

mmWave transmission [9]. To reduce the hardware complexity,

we apply low radio frequency (RF) chains structure at the base

station (BS), where the hybrid analog/digitial precoding is con-

sidered. Speciﬁcally, we ﬁrst pair users into clusters according

to their channel correlation and gain difference. Following

this, we design an analog beamforming vector for each cluster

based on the codebook. For digital precoding, to coordinate

inter-cluster interference, we apply the conventional zero forc-

ing (ZF) precoding based on the channel of the strong user.

To this end, we formulate the power allocation problem aim-

ing to maximize the EE under the users’ QoS requirements.

To ensure the cluster’ fairness, per-cluster power constraint

is considered. We transform the fractional EE problem into

a subtractive-form one, while it is still nonconvex. Then, we

divide the problem into multiple independent convex optimiza-

tion problems by setting initial feasible power and propose an

iterative algorithm to obtain the optimal solution. Simulation

results verify that the EE under NOMA outperforms that under

conventional OMA.

Notation: We use the following notations throughout this

letter: (·)

and (·)

denote the transpose and Hermitian trans-

pose, respectively, · denotes the Frobenius norm, C

x×y

denotes the space of x × y complex matrix.

II. S

YSTEM AND CHANNEL MODEL

As shown in Fig. 1, we consider a downlink mmWave

mMIMO-NOMA transmission scenario, in which one BS

2162-2345

 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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