提高无小区大规模MIMO下行链路频谱效率的联合预编码方案

Yufei Qiao, Xu Qiao, Run Luo, Zhencong Dai, Longxiang Yang
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摘要

无小区大规模多输入多输出(MIMO)系统是一项很有前途的B5G和6G技术,它具有很高的频谱和功率效率。然而,预编码方案对系统下行频谱效率有明显的影响。最大比传输(MRT)是无小区大规模MIMO系统中最常用的预编码方案,它能最大限度地提高期望信号的功率,但不能抑制干扰。集中式强制零(CZF)是一种干扰消除能力强的预编码方案,但由于计算复杂度高,前传容量有限,无法支持过多的用户。为了抑制干扰和满足系统可扩展性的要求,提出了一种提高频谱效率的联合预编码方案——可扩展联合强制零预编码(SJZF),该方案根据ap间干扰的程度部署不同的强制零预编码方案。在独立瑞利衰落信道和导频污染的假设下,导出了SJZF的可实现谱效率(SE)的封闭表达式。根据SE表达式的特点,提出了一种新的随机导频分配方法和最大最小公平功率优化算法,明显提高了系统的SE。数值结果表明,我们的SJZF的SE优于其他传统的预编码方案,接近CZF,但具有合理的计算复杂度。
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Joint Precoding Scheme with Enhanced Spectral Efficiency for Cell-Free Massive MIMO Downlink
The cell-free massive multiple-input multiple-output (MIMO) system is a promising technology for B5G and 6G, it owns large spectral and power efficiency. However, the precoding scheme can affect the system downlink spectral efficiency obviously. Maximum-ratio transmission (MRT) is the most common precoding scheme used in cell-free massive MIMO systems, which maximizes the power of the desired signal, but is unable to suppress interference. Centralized zero-forcing (CZF) is a precoding scheme with strong interference cancellation ability, but can not support too many users because of the high computational complexity and limited front-haul capacity. In order to suppress interference and satisfy the requirement of system scalability, we propose a joint precoding scheme with enhanced spectral efficiency, named Scalable Joint Zero-Forcing Precoding (SJZF), which deploys different zero-forcing precoding schemes with the degree of inter-APs interference. We derive a closed-form expression of the achievable spectral efficiency (SE) for SJZF, under the assumption of independent rayleigh fading channel and pilot contamination. Based on the characteristics of the SE expression, we propose a novel random pilot assignment method, and a max-min fairness power optimization algorithm, to improve the system's SE obviously. Numerical results show that the SE of our SJZF outperforms other conventional precoding schemes, approaching CZF, but have affordable computation complexity.
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