AEPHORA: AI/ML-Based Energy-Efficient Proactive Handover and Resource Allocation
Bowen Xie, Sheng Zhou, Zhisheng Niu, Hao Wu, Cong Shi · 2025
Future Vehicle-to-Everything (V2X) scenarios require high-speed, low-latency, and ultra-reliable communication services, particularly for applications such as autonomous driving and in-vehicle infotainment. Dense heterogeneous cellular networks, which incorporate both macro and micro base stations, can effectively address these demands. However, they introduce more frequent handovers and higher energy consumption. Proactive handover (PHO) mechanisms can significantly reduce handover delays and failure rates caused by frequent handovers, especially with the mobility prediction capability enhanced by artificial intelligence and machine learning (AI/ML) technologies. Nonetheless, the energy-efficient joint optimization of PHO and resource allocation (RA) remains underexplored. In this paper, we propose an AI/ML-based energy-efficient PHO and RA (AEPHORA) framework, which leverages AI/ML-based predictions of vehicular mobility to jointly optimize PHO and RA decisions. AEPHORA aims to minimize the time-averaged system transmit power while satisfying quality of service (QoS) constraints on communication delay and reliability. Simulation results demonstrate the effectiveness of the AEPHORA framework in balancing energy efficiency with QoS requirements in high-demand V2X environments.