Evolutionary Algorithm Based Capacity Maximization of 5G/B5G Hybrid Pre-Coding Systems

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.