毫米波MIMO系统中低复杂度混合预编码算法优化

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毫米波多输入多输出(MMWave MIMO)通信是5G网络的关键技术之一，它通过利用大阵列天线和方向性波束形成来对抗高路径损耗。然而，随着天线数量的增加，传统的数字预编码器在硬件成本和能耗方面面临着巨大挑战。为了降低这些复杂性和成本，混合预编码（Hybrid Precoding）方案应运而生。本篇研究论文由 Ye Wang 和 Weixia Zou 提出，他们来自北京邮电大学的通用无线通信实验室。他们针对毫米波MIMO系统设计了一种迭代的启发式混合预编码算法，该算法在完全的信道状态信息(CSI)下运行。论文的核心贡献在于提出了一种能够在保持性能接近最优的同时实现低复杂度的解决方案。作者证明了他们的算法具有竞争力，能够在性能和复杂性之间提供一个有效的平衡。通过精心设计的迭代过程，该算法能够有效处理大规模天线阵列，同时避免了传统数字预编码所需的高昂计算开销。此外，算法的高效性还体现在其能够实现满意的信号质量和吞吐量，这对于实际部署中的能量效率至关重要。论文深入探讨了混合预编码的权衡策略，特别是在硬件实施和系统性能之间的权衡，这对于未来5G网络的设计和优化具有实际指导意义。通过理论分析和仿真验证，该算法展示了其在实际应用场景中的可行性和实用性，为毫米波MIMO系统的低成本、高效设计提供了新的思路。这篇研究论文不仅提出了一个创新的低复杂度混合预编码算法，还对毫米波MIMO系统的性能优化进行了深入探讨，为该领域的研究者和工程师提供了一个有价值的参考框架。

Low Complexity Hybrid Precoding Algorithm

in Millimeter Wave MIMO Systems

Ye Wang

, Weixia Zou

Key Lab of Universal Wireless Communications, MOE, Beijing University of Posts and Telecommunications,

Beijing 100876, P. R. China

Email: yewautumn@live.com; zwx0218@bupt.edu.cn

Abstract—Millimeter wave (mmWave) communication

will become a key technology for 5G networks. To combat

the high path loss of mmWave, large antenna arrays and

directional beamforming are used by mmWave commu-

nication systems. However, with the increase of number

of antennas, the hardware and energy cost of traditional

digital precoder is very high. Hence, hybrid precoding

structure is introduced to overcome the difﬁculty. In

this paper, we will propose an iterative heuristic hybrid

precoding algorithm with full channel state information.

We show that the proposed algorithm achieves near-optimal

performance with low complexity. Therefore, the proposed

algorithm provides a competitive trade-off strategy between

performance and complexity for hybrid precoding design.

Moreover, we ﬁnd that the proposed algorithm is also able

to achieve satisfactory spectral efﬁciency in quantization

scenarios.

Index Terms—Millimeter wave communication, hybrid

precoding, low complexity, alternating minimization.

I. INTRODUCTION

The demand of wireless networks has dramatically

increased in recent years. In the future, the preparing

5G networks plan to achieve the requirements of high

data rate, low latency and low cost [1]. To overcome

the lack of spectrum resource in low frequency band,

millimeter wave (mmWave) band is introduced for 5G

networks [1], [2]. The high frequency of mmWave leads

to high free space loss and less diffraction [2]. Due to

the short wavelength of mmWave, the antenna elements

of mmWave are very small. This characteristic makes

it feasible to package more antenna elements in an

antenna array. Therefore, massive MIMO can be used

to compensate the high path loss of mmWave.

For traditional MIMO system, the signal is processed

at the baseband, where both the amplitudes and phases of

signals can be modiﬁed [3]. Then, the baseband signals

are converted to analog signals by radio frequency (RF)

chains before being transmitted by antennas. Hence, it

is necessary to allocate an RF chain for each antenna

element. For mmWave communication system with large

antenna arrays, the energy consumption and hardware

cost of required RF chains is too high. Therefore,

hybrid precoding architecture [4] is proposed to solve

the problem. By splitting the precoding precess into

digital precoding and analog precoding, the number

of RF chains can be reduced substantially, while the

performance loss of the system is not obvious. In analog

RF domain, the key component is phase shifter (PS),

which can only adjust the phase of signal.

In the work of [3], the hybrid precoding problem was

converted to the problem whose target is to minimize

the Euclidean distance between hybrid precoder and the

optimal precoder. In addition, spatially sparse precoding

via orthogonal matching pursuit (OMP) algorithm was

proposed as a solution. Based on the characteristic of

spatially sparseness, [5]–[7] also provided some other

algorithms. Besides, by introducing an extra constraint

for digital matrix, [8] proposed precoding designs for

single-user massive MIMO system and multi-user mas-

sive multiple-input single-output (MISO) system, respec-

tively. In [9], the author used alternating minimization

(AltMin) method to solve hybrid precoding problem and

proposed three algorithms for mmWave communication

systems. [10] proposed several low complexity algo-

rithms with different trade-off strategies between perfor-

mance and complexity. The work in [11] used alternate

minimization and matrix decomposition in combination,

and provided a near-optimal design for hybrid precoder.

In this paper, based on manifold optimization method

[9], we propose low complexity manifold optimization

(LCMO) as our hybrid precoder design. Based on the

framework of alternating minimization, the proposed

algorithm can solve hybrid precoding problem without

introducing extra constraints. To simplify the process of

solving the analog precoding matrix, we decompose the

non-convex problem into a series of subproblems which

can be solved easily. Then we assemble the solutions

of all the subproblems together to obtain the result of

complete analog precoding matrix. Besides, we give a

feasible and general step adjustment strategy to make the

algorithm adaptive for precoders with different numbers

of RF chains and data streams. Through simulation and

complexity analysis, we ﬁnd that LCMO achieves near-

optimal performance with much lower complexity.

The following notations are used in this paper: A is a

matrix; a is a column vector; a is a scalar; (A)

m,n

and

(A)

m,:

indicate the entry on the mth row and nth column

of A and the mth row of A respectively; A

∗

, A

and

represent the conjugate, transpose and conjugate

transpose of A respectively; abs(A), det(A) and kAk

are the elementwise absolute value, determinant and

Frobenius norm of A respectively; A

−1

and A

†

indicate

2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

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