Intelligent Performance Analysis and Optimization of MIMO Systems in 5G Wireless Networks Using Machine Learning Techniques

The rapid evolution of fifth-generation (5G) communication systems has intensified the need for high data rates, enhanced capacity, and ultra-reliable low-latency connectivity. Multiple-Input Multiple-Output (MIMO) technology plays a pivotal role in achieving these objectives through spatial diversity and multiplexing gains. However, the practical deployment of MIMO in 5G environments faces persistent challenges, including interference, channel fading, and hardware complexities. This study presents an intelligent performance analysis and optimization framework for 5G MIMO systems using machine learning (ML) techniques. A hybrid MIMO architecture was simulated in MATLAB under realistic millimeter-wave conditions, and the generated dataset was used to train and evaluate four ML models—Random Forest, Linear Regression, Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). Performance evaluation was based on key metrics such as throughput and bit error rate (BER). The results revealed that the ensemble learning models, particularly XGBoost and Random Forest, outperformed conventional methods by achieving superior accuracy and minimal error margins. These findings highlight the effectiveness of machine learning-driven optimization to improve spectral efficiency, reduce latency, and improve overall reliability in 5G MIMO networks.