Joint user activity and signal detection for massive multiple-input multiple-output

Xiaoqun SONG; Ming JIN; Zhongjie JIA

doi:10.11959/j.issn.1000-0801.2021002

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Joint user activity and signal detection for massive multiple-input multiple-output

Research and Development | 更新时间：2024-06-05

- Joint user activity and signal detection for massive multiple-input multiple-output
- Telecommunications Science Vol. 37, Issue 5, Pages: 113-123(2021)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2021002
  CLC： TP929.5
- Published Online：2021-05，
  
  Published：20 May 2021
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Xiaoqun SONG, Ming JIN, Zhongjie JIA. Joint user activity and signal detection for massive multiple-input multiple-output[J]. Telecommunications science, 2021, 37(5): 113-123.
DOI：

Xiaoqun SONG, Ming JIN, Zhongjie JIA. Joint user activity and signal detection for massive multiple-input multiple-output[J]. Telecommunications science, 2021, 37(5): 113-123. DOI： 10.11959/j.issn.1000-0801.2021002.

摘要

上行免调度大规模多输入多输出（mMIMO）系统中，当接收天线相关性增加或活跃设备数增大时，传统的联合用户活跃性和信号检测方法性能急剧下降。另外，传统方法需要知道噪声功率，而实际中往往难以获得准确信息。针对上述问题，提出了一种基于酉变换近似消息传递和期望最大化的联合用户活跃性和信号检测方法。与传统的近似消息传递算法不同，所提的酉变换近似消息传递算法假设噪声功率未知。首先，利用酉变换近似消息传递算法将mMIMO基站侧接收信号进行解耦处理，同时估计噪声功率。然后，利用解耦信号，基于期望最大化算法实现用户活跃性检测。最后，通过计算解耦信号所属星座点的后验概率得到信号检测结果。仿真结果表明，所提方法在联合用户活跃性和信号检测方面优于传统方法。

Abstract

In uplink grant-free massive multiple-input multiple-output (mMIMO) systems

the performance of available methods for joint user activity and signal detection deteriorates when the correlation of receiving antennas or the number of active devices increases.Moreover

the available methods require the knowledge of noise power

which is often practically unknown.To address the above issues

combining approximate message passing with unitary transformation and expectation maximization algorithm to jointly implement user activity and signal detection was proposed.Different from the conventional approximate message passing algorithm

the proposed one assumes that the noise power was unknown.Firstly

by exploiting the approximate message passing algorithm with unitary transform

the distribution of transmitted symbols together with the distribution of noise power was obtained.Secondly

expectation maximization algorithm was applied to estimate the user activity.Finally

the signal detection was implemented by deriving the posterior distribution of the decoupled signal belongs.Simulation results show that the proposed method is better than the traditional method in joint user activity and signal detection.

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references

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