Low-Complexity Large MIMO Detection Based on Beam-Domain Local LMMSE Filters
Low-Complexity Large MIMO Detection Based on Beam-Domain Local LMMSE Filters
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DOI：
10.1109/icc45855.2022.9838343
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
T. Yoshida;Daichi Shirase;Takumi Takahashi;S. Ibi;S. Sampei
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文献类型：
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作者：
T. Yoshida;Daichi Shirase;Takumi Takahashi;S. Ibi;S. Sampei
在上行链路大型多输入多输出（MIMO）系统中，通常使用线性最小均方误差（LMMSE）滤波器来实现低复杂度的多用户检测（MUD）。然而，随着MIMO系统的规模和密度朝着真正的大规模设置增长，需要高维矩阵求逆运算的LMMSE检测在计算上变得昂贵。作为一个有前途的方法来解决这个问题，本地LMMSE（LLMMSE）检测器被提出，其中一个连续的波束块可以被选择为每个用户来构建减少波束域信道，假设在基站（BS）使用数字波束形成。一个主要的问题是根据接收信号的角度扩展的性能下降，由于过度的降维引起的信息损失，旨在减少计算。为了缓解这个问题，本文提出了选择性地结合联合收割机的信息重叠LLMMSE滤波器在对数似然比（LLR）域。此外，该方法被扩展到基于概率数据关联（PDA）的迭代检测方案，以进一步提高通信的可靠性。仿真结果表明，所提出的方法的有效性的误码率（BER）的性能和计算成本。
Linear minimum mean square error (LMMSE) filters are often utilized to achieve low-complexity multi-user detection (MUD) in uplink large multi-input multi-output (MIMO) systems. As the scale and density of MIMO systems grow towards truly massive setups, however, the LMMSE detection requiring high-dimensional matrix inversion operations becomes computationally expensive. As a promising approach to tackle this issue, the local LMMSE (LLMMSE) detector was proposed, where a contiguous block of beams can be selected for each user to construct the reduced beam-domain channels, assuming the use of digital beamforming at a base station (BS). A main issue is the performance degradation according to the angular spread of the received signal, due to the information loss induced by an excessive dimensionality reduction aiming at the computational reduction. To alleviate this issue, this paper proposes to selectively combine the information from the overlapped LLMMSE filters in the log-likelihood ratio (LLR) domain. In addition, this method is extended to probabilistic data association (PDA)-based iterative detection scheme for further enhancement of communication reliability. The efficacy of the proposed methods is demonstrated by simulation results in terms of the bit error rate (BER) performance and the computational cost.