Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing

Yiyang HU; Lina QI

doi:10.11959/j.issn.2096-3750.2021.00227

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Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing

Theory and Technology | 更新时间：2024-06-05

- Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing
- Chinese Journal on Internet of Things Vol. 5, Issue 3, Pages: 78-85(2021)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.2096-3750.2021.00227
  CLC： TN929.5
- Published：30 September 2021，
  
  Published Online：2021-09，
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YIYANG HU, LINA QI. Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing. [J]. Chinese journal on internet of things, 2021, 5(3): 78-85.
DOI：

YIYANG HU, LINA QI. Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing. [J]. Chinese journal on internet of things, 2021, 5(3): 78-85. DOI： 10.11959/j.issn.2096-3750.2021.00227.

摘要

大规模多输入多输出（MIMO

multiple-input multiple-output）是物联网（IoT

Internet of things）中为多种机器类设备高效提供连接服务的解决方案，而高效的连接服务需要准确的信道估计。针对大规模MIMO系统的下行信道估计中导频开销大和估计归一化均方误差（NMSE

normalized mean square error）性能较差的问题，以压缩感知（CS

compressed sensing）理论为基础，在结合信道空间域共同稀疏性的同时，利用相邻时隙差分信道冲激响应（CIR

channel impulse response）的稀疏性更低的特点，大大减少了导频发送的数量。在重构算法上，提出了一种二阶段差分估计算法，将一组连续相关时隙内的信道估计分为两个阶段，并结合自适应压缩感知的思想以实现快速准确的CIR估计。仿真结果表明，所提出的二阶段差分信道估计算法不仅在估计的NMSE性能、数据传输速率上相比已有的基于CS的多重测量向量（MMV

multiple measurement vector）算法有显著的提高，而且在运行时间复杂度上也有一定的降低。

Abstract

Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT)

and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems

based on the compressed sensing (CS) theory

the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR)

which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm

a two-stage differential estimation algorithm

which divided the channel estimation in consecutive time slots with time correlation into two stages

was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm

but also show a certain reduction in runtime complexity.

关键词

物联网大规模多输入多输出压缩感知信道估计稀疏度自适应差分信道冲激响应

Keywords

Internet of thingsmassive MIMOcompressed sensingchannel estimationsparsity adaptivedifferential CIR

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