Implementation of Energy‐Efficient Resource Allocation Strategy for 5G User Equipment (UE)

ABSTRACT The rapid evolution of fifth‐generation (5G) networks has significantly increased the demand for high‐speed data transmission, ultra‐low latency, and massive device connectivity. However, these advancements introduce new challenges in resource allocation and energy efficiency, particularly for user equipment (UE) that must handle complex communication tasks under limited power budgets. Conventional resource management methods often suffer from inefficient spectrum utilization, static power allocation, and high interference, leading to degraded network performance and excessive energy consumption. Moreover, as 5G systems integrate features like carrier aggregation (CA) and multi‐node connectivity, maintaining a balance between throughput and energy efficiency becomes increasingly complex. These limitations motivate the need for a holistic approach that can adapt intelligently to varying network conditions while ensuring sustainable power usage. To overcome these challenges, this research proposes an energy‐efficient resource allocation strategy (EERAS), a unified framework designed to optimize spectrum utilization and minimize power consumption simultaneously. EERAS integrates three key components: the OptiGrabber (OPG) algorithm for dynamic allocation of radio resource blocks (RRBs), the ActiState Loop (ASL) algorithm for reinforcement learning–based power control according to signal‐to‐noise ratio (SNR) variations, and the PowerNap reception (PNRX) technique for intelligent idle‐state management. Collectively, these modules enable adaptive, context‐aware optimization, enhancing throughput while conserving energy. Simulation results validate the framework's effectiveness, achieving 3.5–4.5‐W power savings per UE, a 9.4% reduction in latency, and a throughput improvement of 90%–95%, marking a significant step toward sustainable, energy‐conscious 5G and future 6G networks.