Joint Optimization of Received Signal Power and Signal Space Dimensions for MIMO Broadcast Channels

多输入多输出 衰退 计算机科学 发射机 干扰(通信) 频道(广播) 信噪比(成像) 收发机 信号(编程语言) 电子工程 噪音(视频) 最优化问题 算法 无线 电信 工程类 人工智能 图像(数学) 程序设计语言
作者
Yifei Zhang,Haixia Zhang,Dongfeng Yuan,Xiaotian Zhou
出处
期刊:IEEE Transactions on Vehicular Technology [Institute of Electrical and Electronics Engineers]
卷期号:71 (4): 3868-3882
标识
DOI:10.1109/tvt.2022.3143654
摘要

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR.
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