Artificial Intelligence in 5G Systems: Management of Resources in High-Altitude Infrastructures

Abstract

The emergence of the 5G generation has considerably advanced wireless communication systems, with higher data rates and increased connectivity. Massive Multiple Input Multiple Output (mMIMO) structures, utilizing numerous antennas, improve spectral efficiency. High-Altitude Platform Stations (HAPS) provide promising deployment structures for 5G networks. However, it faces challenges including useful resource allocation, interference mitigation, and dynamic beamforming adaptation. This study proposes an efficient method for optimizing communication systems through the use of HAPS through aggregate of game theory and dynamic optimization strategies. The model introduces a novel method known as Dynamic Levysalp Fusion Optimization (DLSFO), which integrates the Levy Flight Algorithm (LFA) and Improved Slap Swarm Optimization (ISSO) to enhance exploration and avoid local optima in mMIMO systems. The findings demonstrate the effectiveness of the proposed method with a system latency (SL), bit error rate (BER), and sum rate, showcasing its potential to increase overall system performance for multi-person, multi-beam conversation systems on HAPS.