Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems

SHEN Bin; ZHAO Shu-feng; HUANG Long-yang

doi:10.3969/j.issn.0372-2112.2018.11.023

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Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems

更新时间：2025-07-16

- Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems
- Acta Electronica Sinica Vol. 46, Issue 11, Pages: 2746-2752(2018)
- 作者机构：
  
  1. 重庆邮电大学,重庆,400065
  2. 中国民用航空飞行学院,四川,德阳,618300
  3. 重庆邮电大学,重庆,400065
  4. 中国民用航空飞行学院,四川,德阳,618300
- 作者简介：
- 基金信息：
  
  National Science and Technology Major Project of the Ministry of Science and Technology (No.2016ZX03001010-004)
- DOI：10.3969/j.issn.0372-2112.2018.11.023
  CLC： TN929.5
- Published Online：25 November 2018，
  
  Published：2018
- 稿件说明：
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SHEN Bin, ZHAO Shu-feng, HUANG Long-yang. Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems[J]. Acta Electronica Sinica, 2018, 46(11): 2746-2752.
DOI：

SHEN Bin, ZHAO Shu-feng, HUANG Long-yang. Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems[J]. Acta Electronica Sinica, 2018, 46(11): 2746-2752. DOI： 10.3969/j.issn.0372-2112.2018.11.023.

摘要

大规模MIMO系统上行链路中，最小均方误差（MMSE）算法能获得接近最优的线性检测性能，但是涉及复杂度较高的矩阵求逆运算.本文基于Kaczmarz迭代提出一种低复杂度软输出信号检测算法，在算法实现中避免了矩阵求逆运算，将实现复杂度由

（

）降为

（

）.同时，引入了最优松弛参数进一步加快算法收敛，最后给出了两种用于信道译码的LLR的近似计算方法.仿真结果表明：所提出的Kaczmarz迭代软输出信号检测算法经过两到三次简单的迭代即可较快地收敛，并达到接近MMSE检测算法的误码率性能的水平，其性能与复杂度均优于基于矩阵近似求逆的一类检测算法.

Abstract

For massive MIMO system uplink

conventional minimum mean square error (MMSE) linear detection algorithm can achieve nearly optimal performance

but it involves complicated matrix inversion. A low-complexity detection algorithm based on Kaczmarz method is proposed in this paper

which can circumvent the matrix inverse operation and hence reduce the complexity from

(

) to

(

). Meanwhile

an optimal relaxation parameter is introduced to further accelerate the algorithm convergence

and two approximate methods of log-likelihood ratios (LLR) estimation for channel decoding are g

iven as well. Simulation results verify that the proposed algorithm outperforms the Neumann series expansion algorithms on both bit error ratio (BER) and computational complexity. It converges rapidly and achieves its performance quite close to that of the MMSE algorithm with only a small number of iterations.

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