Hybrid pre-coding strategies are considered as a potential solution for combating path loss experienced by Massive MIMO systems operating at millimeter wave frequencies. The partially connected structure is preferred over the fully connected structure due to smaller computational complexity. In order to improve the spectral efficiency of a partially connected hybrid pre-coding architecture, which is one of the requirements of future 5G/B5G systems, this work proposes the application of evolutionary algorithms for joint computation of RF and the digital pre-coder. The evolutionary algorithm based scheme jointly evaluates the RF and digital pre-coder for a partially connected hybrid structure by taking into account the current RF chain for computations and therefore it is not based on interference cancellation from all other RF chains as in the case of successive interference cancellation (SIC). The evolutionary algorithm, i.e., Artificial Bee Colony (BEE) based pre-coding scheme outperforms other popular evolutionary algorithms as well as the SIC based pre-coding scheme in terms of spectral efficiency. In addition, the proposed algorithm is not overly sensitive to variations in channel conditions.

中文翻译：

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基于进化算法的5G / B5G混合预编码系统容量最大化。

混合预编码策略被认为是解决以毫米波频率运行的Massive MIMO系统所经历的路径损耗的潜在解决方案。由于较小的计算复杂度，部分连接的结构优于完全连接的结构。为了提高部分连接的混合预编码架构的频谱效率，这是未来5G / B5G系统的要求之一，这项工作提出了将进化算法用于RF和数字预编码器的联合计算的应用。基于进化算法的方案通过考虑当前的RF链进行计算，从而共同评估部分连接的混合结构的RF和数字预编码器，因此，与连续的情况不同，它不基于来自所有其他RF链的干扰消除干扰消除（SIC）。进化算法，即基于人工蜂群（BEE）的预编码方案在频谱效率方面优于其他流行的进化算法以及基于SIC的预编码方案。另外，所提出的算法对信道条件的变化不是过于敏感。在频谱效率方面，基于人工蜂群（BEE）的预编码方案优于其他流行的进化算法以及基于SIC的预编码方案。另外，所提出的算法对信道条件的变化不是过于敏感。在频谱效率方面，基于人工蜂群（BEE）的预编码方案优于其他流行的进化算法以及基于SIC的预编码方案。另外，所提出的算法对信道条件的变化不是过于敏感。