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General Recursive Least Square Algorithm for Distributed Detection in Massive MIMO
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DOI：10.1109/TVT.2024.3370611.png)
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摘要
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In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output (MIMO) to alleviate the bottlenecks in both computational complexity and data bandwidth for interconnection. Different from the existing recursive least square (RLS) detection algorithm which only supports a single antenna in each distributed unit (DU), the proposed GRLS allows for multiple antennas in each DU, rendering it adaptable to a variety of practical scenarios. Moreover, among the total C DUs and with an integer parameter C-0, the computational complexity of C-C-0 DUs in GRLS can be significantly reduced by leveraging the channel hardening property. Through analysis, we demonstrate that the convergence of the GRLS algorithm is guaranteed if C-0 >= [ ( root B / 2 + root K )(2) / B ] holds, where K and B denote the numbers of antennas at the user side and each DU, respectively. Furthermore, based on the daisy-chain architecture, the proposed GRLS algorithm also enjoys excellent scalability, which can be easily extended with extra DUs for further improvement. Finally, the detection complexity and data bandwidth analysis are also provided to unveil the superiority of GRLS compared to other distributed detection schemes for massive MIMO.
Keyword:
Complexity theory
Massive MIMO
Detection algorithms
Computer architecture
Bandwidth
Antennas
Antenna arrays
Daisy-chain
decentralized signal detection
distributed MIMO detection
RLS
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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引用论文
Decentralized Baseband Processing With Gaussian Message Passing Detection for Uplink Massive MU-MIMO Systems具有高斯消息传递检测的上行链路大规模mu-mimo系统分散基带处理
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