一种基于1 bit压缩感知的毫米波信道估计算法

李心安; 余开文

doi:10.13756/j.gtxyj.2021.06.012

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一种基于1 bit压缩感知的毫米波信道估计算法

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- 一种基于1 bit压缩感知的毫米波信道估计算法
- Channel Estimation in mmWave System with One-bit Compressed Sensing
- 光通信研究 2021年第6期页码：68-71,76
- 作者机构：
  
  a.重庆邮电大学　新一代宽带移动通信重点实验室，重庆　400065
  b.重庆邮电大学　通信与信息工程学院，重庆　400065
- 作者简介：
  
  李心安，硕士。E-mail：1329077548@qq.com
- 基金信息：
  
  国家科技重大专项资助项目(2018ZX03001026-002)
- DOI：10.13756/j.gtxyj.2021.06.012
  中图分类号： TN911
- 纸质出版日期：2021-12-10，
  
  收稿日期：2021-01-13，
扫描看全文
李心安, 余开文. 一种基于1 bit压缩感知的毫米波信道估计算法[J]. 光通信研究, 2021,(6):68-71,76.

Li Xin-an, Yu Kai-wen. Channel Estimation in mmWave System with One-bit Compressed Sensing[J]. Study on Optical Communications, 2021,(6):68-71,76.
李心安, 余开文. 一种基于1 bit压缩感知的毫米波信道估计算法[J]. 光通信研究, 2021,(6):68-71,76. DOI： 10.13756/j.gtxyj.2021.06.012.

Li Xin-an, Yu Kai-wen. Channel Estimation in mmWave System with One-bit Compressed Sensing[J]. Study on Optical Communications, 2021,(6):68-71,76. DOI： 10.13756/j.gtxyj.2021.06.012.

摘要

针对毫米波大规模多输入多输出（MIMO）系统在接收端使用高精度模/数转换器的成本和功耗高等问题，文章提出了一种采用1 bit量化器的信道估计算法。该算法首先根据量化后的接收信号计算条件期望估计量化前的接收信号，然后利用毫米波信道在角度域的稀疏性，使用正交匹配追踪算法，每次迭代选取与当前残差最大相关的原子进行最小二乘估计。为了提升算法的有效性，文章同时提出了每次迭代选取多个原子的解决方案。仿真结果表明，文章所提算法的平均归一化均方误差是期望最大（EM）算法的1/10，且每次迭代运行时间为EM算法的1/20，其在有效性和可靠性等方面均有较大提升，在毫米波大规模MIMO系统信道估计应用中有一定的实用价值。

Abstract

Considering the cost and large power consumption of high-resolution analog-to-digital converters in mmWave massive Multiple Input Multiple Output (MIMO) system

we develop a channel estimation algorithm with one-bit quantizer. Given the quantized received signal

the conditional expectation is first calculated to estimate the unquantized received signal. Based on the sparsity of mmWave channel in the angular domain

the column most correlative to the current residual is selected for least square estimation in each iteration. We also propose a solution to select multiple atoms in each iteration for the efficiency improvement. The simulation results show that the average normalized mean square error of the proposed algorithm and the running time of each iteration are 1/10 and 1/20 of the Expectation Maximization (EM) algorithm. It can be seen that the algorithm has improved performance and higher reliability

with certain practical value in the channel estimation application of mm-Wave large-scale MIMO systems.

关键词

毫米波1 bit量化器信道估计正交匹配追踪

Keywords

mmWaveone-bit quantizerchannel estimationorthogonal matching pursuit

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.

Rangan S, Rappaport T S, Erkip E.Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges[J].Proceedings of the IEEE, 2014, 102(3): 366-385.

Roh W, Seol J Y, Park J, et al. Millimeter-wave Beamforming as an Enabling Technology for 5G Cellular Communications: Theoretical Feasibility and Prototype Results[J].IEEE Communications Magazine, 2014, 52(2): 106-113.

Heath R W, González-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.

Mo J, Schniter P, González-Prelcic N, et al. Channel Estimation in Millimeter Wave MIMO Systems with One-bit Quantization[C]//2014 48th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, USA: IEEE, 2014: 957-961.

Mo J, Alkhateeb A, Abu-Surra S, et al. Hybrid Architectures with Few-Bit ADC Receivers: Achievable Rates and Energy-Rate Tradeoffs[J].IEEE Transactions on Wireless Communications, 2017, 16(4): 2274-2287.

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 Wire less Communications, 2017, 16(12): 8131-8146.

Alkhateeb A, Ayach O E, Leus G, et al. Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems[J].IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5): 831-846.

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

Lok T M, Wei V K W.Channel Estimation with Quantized Observations[C]//1998 IEEE International Symposiumon Information Theory. Cambridge, MA, USA: IEEE, 1998: 333-338.

Ivrlac M T, Nossek J.On MIMO Channel Estimation with Single-bit Signal-quantization[C]//ITG Smart Antenna Workshop. Vienna, Austria: IEEE, 2007: 563-568.

Molazadeh A, Ghasimi M, Alizade P, et al. Low Complexity Channel Estimation for mmWave Hybrid MIMO Systems[C]//2020 28th Iranian Conference on Electrical Engineering (ICEE). Tabriz, Iran: IEEE, 2020: 9260828.

Shirazinia A, Chatterjee S, Skoglund M.Analysis-by-Synthesis Quantization for Compressed Sensing Measurements[J].IEEE Transactions on Signal Processing, 2013, 61(22): 5789-5800.

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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