一种基于优化的毫米波信道估计算法研究

谢广成; 苏宇; 曾妍; 程瑛颖

doi:10.13756/j.gtxyj.2022.03.003

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一种基于优化的毫米波信道估计算法研究

光通信系统与网络技术 | 更新时间：2022-06-15

- 一种基于优化的毫米波信道估计算法研究
- Optimization based Millimeter Wave Channel Estimation with Low-resolution Analog-to-digital Converters
- 光通信研究 2022年第3期页码：11-16
- 作者机构：
  
  国家电网重庆市电力公司，重庆　401121
- 作者简介：
  
  谢广成，工程师。E-mail：xieguangcheng1973@163.com
- 基金信息：
  
  国家电网科学技术研究资助项目(SGCQKHOOJSJS2000053)
- DOI：10.13756/j.gtxyj.2022.03.003
  中图分类号： TN911
- 纸质出版日期：2022-06-10，
  
  收稿日期：2021-06-20，
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谢广成, 苏宇, 曾妍, 等. 一种基于优化的毫米波信道估计算法研究[J]. 光通信研究, 2022,(3):11-16.

XIE Guang-cheng, SU Yu, ZENG Yan, et al. Optimization based Millimeter Wave Channel Estimation with Low-resolution Analog-to-digital Converters[J]. Study on Optical Communications, 2022,(3):11-16.
谢广成, 苏宇, 曾妍, 等. 一种基于优化的毫米波信道估计算法研究[J]. 光通信研究, 2022,(3):11-16. DOI： 10.13756/j.gtxyj.2022.03.003.

XIE Guang-cheng, SU Yu, ZENG Yan, et al. Optimization based Millimeter Wave Channel Estimation with Low-resolution Analog-to-digital Converters[J]. Study on Optical Communications, 2022,(3):11-16. DOI： 10.13756/j.gtxyj.2022.03.003.

摘要

毫米波大规模多输入多输出(MIMO)系统具有更多的射频链路，带来更大的功耗，接收端使用低分辨率模数转换器(ADC)可有效解决功耗高的问题。针对低分辨率ADC引起接收信号幅度失真，进而影响信道估计准确性的问题，文章提出了一种基于优化的两阶段毫米波信道估计算法。第1阶段，根据量化后的接收信号确定采样后的接收信号取值范围，并将其作为约束条件构造优化问题，利用

范数衡量向量稀疏度估计波达方向；第2阶段，根据估计的波达方向，使用Bussgang分解模型近似非线性量化过程，得到信道复增益的最小二乘估计。仿真结果表明，所提算法准确性比目前最先进的算法提升了约5 dB，且算法复杂度降为线性。由此可见，所提算法在有效性和可靠性方面均有较大提升，具有实用价值。

Abstract

Millimeter wave large-scale Multiple Input Multiple Output (MIMO) system has more radio frequency links that bringing more power consumption

which could be efficiently tackled by using low-resolution Analog-to-Digital Converters (ADCs) in receiver. Considering the influence of the received signal amplitude distortion caused by low-resolution ADCs on the performance of channel estimation

a two-stage millimeter wave channel estimation algorithm is proposed after optimization. In the first stage

we construct the optimization problem which inequality constraints originating from the received signal determined by the quantized received signal and estimate the direction of arrival by using

-norm to measure the sparsity. In the second stage

according to the estimated direction

Bussgang decomposition has been used to approximate the nonlinear quantization process

and the least square estimation of the complex channel gain is obtained. Simulation results show that the performance of the proposed algorithm is improved by about 5 dB compared with the current state-of-the-art algorithm

and the complexity is reduced to be linear. It can be seen that the proposed algorithm is greatly improved in terms of effectiveness and reliability

and has certain practical value.

关键词

毫米波通信大规模多输入多输出信道估计低分辨率模数转换器凸优化

Keywords

millimeter wave communicationmassive MIMOchannel estimationlow-resolution ADCconvex optimization

references

Rappaport T S, Sun S, Mayzus R, et al. Millimeter Wave Mobile Communications for 5G Cellular: It Will Work！[J]. IEEE Access, 2013, 1: 335-349.

申敏, 余开文. 一种新的基于压缩感知的毫米波信道估计算法[J]. 电讯技术, 2020, 60(12): 1437-1441.

Wang S, Li Y, Wang J. Multiuser Detection in Massive Spatial Modulation MIMO with Low-resolution ADCs[J]. IEEE Transactions on Wireless Communications, 2015, 14(4): 2156-2168.

Abbas W B, Gomez-Cuba F, Zorzi M. Millimeter Wave Receiver Efficiency: A Comprehensive Comparison of Beamforming Schemes with Low Resolution ADCs[J]. IEEE Transactions on Wireless Communications, 2017, 16(12): 8131-8146.

Mo J, Schniter P, Heath R W. Channel Estimation in Broadband Millimeter Wave MIMO Systems with Few-bit ADCs[J]. IEEE Transactions on Signal Processing, 2018, 66(5): 1141-1154.

Risi C, Persson D, Larsson E G. Massive MIMO with 1-bit ADC[DB/OL]. (2014-04-30)[2021-06-20]. https://arxiv.org/abs/1404.7736https://arxiv.org/abs/1404.7736.

Wang Y, Xu W, Zhang H, et al. Wideband mmWave Channel Estimation for Hybrid Massive MIMO with Low-precision ADCs[J]. IEEE Wireless Communications Letters, 2018, 8(1): 285-288.

Heath R W, Gonzalez-Prelcic N, Rangan S, et al. An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2016, 10(3): 436-453.

Choi J W, Shim B, Ding Y, et al. Compressed Sensing for Wireless Communications: Useful Tips and Tricks[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1527-1550.

Boufounos P T, Baraniuk R G. 1-bit Compressive Sensing[C]//2008 42nd Annual Conference on Information Sciences and Systems. Princeton, NJ, USA: IEEE, 2008: 4558487.

Stöckle C, Munir J, Mezghani A, et al. Channel Estimation in Massive MIMO Systems Using 1-bit Quantization[C]//2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Edinburgh, UK: IEEE, 2016: 7536730.

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