OPTIMIZED DEEP LEARNING-BASED 5G MIMO SYSTEM DESIGN FOR HIGH-PERFORMANCE WIRELESS COMMUNICATION

Abstract

The fifth-generation (5G) wireless communication system demands high data rates, low latency, and efficient spectrum utilization. Multiple-input multiple-output (MIMO) technology is a key enabler to meet these requirements. However, designing optimal MIMO systems is challenging due to dynamic channel conditions and hardware constraints. This paper proposes a deep learning-based optimization framework for 5G MIMO systems. The framework leverages neural networks to adaptively configure antenna arrays, beamforming vectors, and resource allocation. Simulation and experimental evaluations demonstrate improved spectral efficiency, reduced bit error rates, and enhanced system reliability compared to conventional methods. The proposed approach integrates real-time channel estimation and adaptive learning for robust performance under varying network conditions. The framework provides a scalable solution for future high-capacity 5G deployments.