DRL-Based Energy-Efficient Group Paging for Robust HTD Access Control in 5G Networks

In 5G networks, the Legacy Paging mechanism for device connections faces challenges due to limited resources and increasing demands from human-type communication (HTC) and massive machine-type communications (mMTCs). This results in higher connection setup time (CST) under high traffic. Group paging, developed for mMTC, allows multiple devices to respond to a single paging message, but increases energy consumption for HTC due to irregular data transmission patterns. To address this, we propose the deep reinforcement learning-based group configuration control (DRL-GCC) mechanism. DRL-GCC combines legacy and group paging methods to optimize paging for HTC by dynamically adjusting group configurations, using the proximal policy optimization algorithm to reduce energy consumption and satisfy the required CST. Simulation results demonstrate that DRL-GCC significantly reduces energy consumption in the random access channel (RACH) procedure and the overall energy usage of devices while ensuring compliance with the CST constraint compared to its alternative methods. Notably, DRL-GCC achieves up to a 43% reduction in energy used during the RACH procedure and a 9% decrease in total energy consumption compared to the existing benchmarks. These improvements imply DRL-GCC’s ability to balance energy efficiency with operational performance, providing a robust solution for efficient HTC-focused service provisioning in 5G networks.