Low complexity hybrid iterative algorithm based signal detection in massive MIMO system

Shufeng ZHAO; Bin SHEN; Furong YANG

doi:10.11959/j.issn.1000−0801.2017186

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Low complexity hybrid iterative algorithm based signal detection in massive MIMO system

research and development | 更新时间：2024-06-05

- Low complexity hybrid iterative algorithm based signal detection in massive MIMO system
- Telecommunications Science Vol. 33, Issue 7, Pages: 39-46(2017)
- 作者机构：
  
  1. 重庆邮电大学移动通信重点实验室，重庆 400065
  2. 四川省通信科研规划设计有限责任公司，四川成都 610041
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000−0801.2017186
  CLC： TN929.5
- Published Online：2017-07，
  
  Published：20 July 2017
- 稿件说明：
移动端阅览
Shufeng ZHAO, Bin SHEN, Furong YANG. Low complexity hybrid iterative algorithm based signal detection in massive MIMO system[J]. Telecommunications science, 2017, 33(7): 39-46.
DOI：

Shufeng ZHAO, Bin SHEN, Furong YANG. Low complexity hybrid iterative algorithm based signal detection in massive MIMO system[J]. Telecommunications science, 2017, 33(7): 39-46. DOI： 10.11959/j.issn.1000−0801.2017186.

摘要

在大规模MIMO系统上行链路信号检测算法中，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，传统的MMSE检测算法涉及高维矩阵求逆运算，由于复杂度过高而使其在实际应用中难以快速有效地实现。基于最速下降（steepest descent，SD）算法和高斯—赛德尔（Gauss-Seidel，GS）迭代的方法提出了一种低复杂度的混合迭代算法，利用SD算法为复杂度相对较低的GS迭代算法提供有效的搜索方向，以加快算法收敛的速度。同时，给出了一种用于信道译码的比特似然比（LLR）近似计算方法。仿真结果表明，通过几次迭代，给出的算法能够快速收敛并接近MMSE检测性能，并将算法复杂度降低一个数量级，保持在O(K

)。

Abstract

Among the uplink signal detection algorithms for massive MIMO systems

the minimum mean square error (MMSE) algorithm can achieve the near-optimal linear detection performance.However

conventional MMSE usually involves high complexity due to the required matrix inversion of large-size matrix

which makes it hard to implement in realistic applications.Based on joint steepest descent (SD) algorithm and Gauss-Seidel iteration

a low complexity hybrid iterative detection algorithm was proposed.The SD algorithm was employed to obtain an efficient searching direction for the following Gauss-Seidel to speed up convergence.Meanwhile

an approximated method was also proposed to compute the bit log-likelihood ratio (LLR) for soft channel decoding.Simulation results verify

that the proposed algorithm can converge rapidly and achieve its performance quite close to that of the MMSE algorithm with only a small number of iterations.Meanwhile

the complexity is reduced by an order of magnitude

which is kept consistently of O(K

关键词

Keywords

references

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