Abstract

The advent of 5G communication technology has revolutionized wireless communication with its high bandwidth, ultra-low latency, and massive connectivity features. However, the dynamic nature of user behavior and environmental changes poses significant challenges in optimizing signal processing for context awareness. Adaptive signal processing (ASP) offers a promising solution, but traditional methods struggle to effectively handle real-time, context-sensitive demands. In this research, we propose a novel reinforcement learning (RL)-based framework for adaptive signal processing that enhances context awareness in 5G networks. The problem addressed involves the optimization of signal parameters, such as power, frequency, and modulation schemes, to meet varying user demands and environmental conditions without compromising Quality of Service (QoS). The proposed method employs RL to adaptively optimize these parameters in real time. Specifically, a Q-learning algorithm is applied to learn the optimal policies for signal adaptation based on feedback from the environment, such as user mobility, interference levels, and network traffic. Simulation results demonstrate that the RL-based approach outperforms traditional static models, achieving up to a 30% reduction in latency and a 20% improvement in overall network throughput, while maintaining a 95% success rate in meeting user QoS requirements. This demonstrates the potential of RL for enhancing ASP in 5G systems.