Research Article

Available Online: 09 Aug 2026

Performance Optimization of High-Speed Wireless Communication Systems Using Machine Learning

Volume 3

High-speed wireless communication systems underpin the data-intensive demands of contemporary 5G deployments and the emergent 6G paradigm, yet sustaining high throughput, low latency, and dependable connectivity in channels that shift rapidly remains an open engineering problem. In this paper, we present an ML-driven framework that combines a deep reinforcement learning (DRL) scheduler with a hybrid CNN-LSTM channel predictor to jointly optimize radio resource allocation, modulation order, transmit power, and bandwidth..