Enhanced chicken swarm optimization for channel estimation and low complexity hybrid beamforming over massive MIMO

Massive Multiple-Input Multiple-Output (MIMO) systems with several antennas at the base station (BS) achieve overspatial degrees of freedom. It significantly increases energy and spectrum efficiencies, making it crucial technology for future wireless communications. Even with these alluring benefits, using a lot of antennas results in significant power and hardware costs. Additionally, the efficacy of hybrid beamforming is lacking in the current system, which has an impact on both system and energy efficiency. Enhanced Chicken Swarm Optimisation (ECSO) and energy-efficient Hybrid Analog-Digital (HAD) beamforming using Analog-To-Digital Converter (ADC) are used in this study to address the aforementioned issues. This research work includes main steps are system model, channel estimation and energy efficient ADC on MIMO systems. Initially, the system model is constructed using antennas, Radio Frequency (RF) chains and antenna users. Then, channel estimation accuracy is ensured by using ECSO algorithm. It generates best fitness values in terms of best channel and sum rate using local and global optimal values. K users concurrent in antennas are acquired to guarantee that channel coefficient estimates between antennas and, low complexity heuristic based on channel estimations can be carried out. Lastly, beamforming is accomplished by HAD both digitally and analogously where pilot sequence optimizations, HAD, and ADC quantization bits distributions reduce Mean Square Error (MSE) by working in paraleel for estimating channels. The ECSO-HAD outperformed other approaches in terms of spectrum efficiencies, greater sum rates, lower energy consumptions, and better Normalised Mean Square Error (NMSE) rates in simulation results.