Resource Allocation Using Reinforcement Learning Technique For Applications In 5g Network

Abstract

The rapid evolution of wireless networks into fifth-generation (5G) and soon-tobe sixth-generation (6G) environments has introduced new challenges in radio newlineresource management. Unlike legacy networks, 5G is designed to support diverse newlineservice categories such as enhanced mobile broadband, ultra-reliable low-latency newlinecommunications, and massive machine-type communications. This diversity newlinenecessitates dynamic, intelligent, and context-aware scheduling of resources such newlineas spectrum, bandwidth, and computational capacity. Traditional resource newlineallocation methods, which often rely on fixed rules or static optimization newlinealgorithms, struggle to keep up with the real-time demands of such heterogeneous newlinetraffic. The emergence of machine learning, and more specifically, reinforcement newlinelearning (RL), deep learning (DL), and nature-inspired optimization algorithms, newlinepresents a powerful opportunity to create systems capable of learning, adapting, and newlineoptimizing under constantly changing network conditions. These techniques allow newlinenetwork components to evolve their decision-making strategies based on newlineenvironmental feedback, traffic variability, and system objectives. The proposed newlinework spans three novel methodologies across three papers, each solving a key newlineaspect of 5G scheduling, focusing on optimization, learning capability, and realtime allocation under realistic constraints newline