Beamforming Optimization in Massive MIMO Networks Using Improved Subset Optimization Algorithm with Hybrid Beamforming

In recent years, the Beamforming optimization in Massive Multiple Input Multiple Output (MIMO) networks has increased the spectral efficiency and signal quality by directing beams toward specific users. However, the digital, analog, and hybrid beamforming techniques face difficulties in computational complexity and energy inefficiency with real-time adaptability. To overcome these drawbacks this research Improved Subset Optimization Algorithm-Hybrid Beamforming (ISOA-HBF) is proposed to minimize the inter-user interference in massive MIMO systems. Initially, A multiple-users massive MIMO system model assisted by an Intelligent Reflecting Surface (IRS) is designed to evaluate beamforming performance in terms of spectral efficiency and signal quality the obstacles between the base station and user sets. Further, ISOA-HBF enhances system capacity and energy efficiency in Massive MIMO Networks. whereas, ISOA, on the other inter-user interference by grouping users into subsets based on channel correlation. Then, HBF then combines analog beamforming and digital precoding to optimize signal direction and improve spectral efficiency in Massive MIMO systems. The proposed ISOA-HBF achieved a sum-rate that is 9.1 % to 69.1% higher compared to existing HBF schemes across different numbers of users and Signal-to-Noise Ratio (SNR).