AEPHORA: AI/ML-Based Energy-Efficient Proactive Handover and Resource Allocation

TL;DR

AEPHORA tackles the energy-efficiency challenge of proactive handover and resource allocation in V2X-enabled HetNets by leveraging AI/ML-based mobility prediction to guide frame-level PHO decisions and RB allocation. The approach casts the joint optimization as a stochastic integer problem, decomposed into PHO-frame subproblems and a prediction-informed GAP-based RA step, enabling efficient, near-optimal scheduling via EPHO and HRA components. Key contributions include a neural predictor for next-frame vehicle locations, an energy-aware PHO strategy with GAP-based assignment and LP relaxation, and a parallel RA scheme that preserves QoS while reducing total power. Simulation results demonstrate significant power savings and robust QoS under diverse traffic loads, highlighting AEPHORA’s practical potential for energy-efficient, reliable V2X communications, albeit with sensitivity to mobility-prediction accuracy.

Abstract

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 capabilities 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 the AEPHORA framework, which leverages AI/ML-based predictions of vehicular mobility to jointly optimize PHO and RA decisions. This framework aims to minimize the average system transmission 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.

Figures (4)

• Figure 1: The system model.
• Figure 2: The AI/ML-based mobility prediction model.
• Figure 3: Flowchart of the AEPHORA algorithm.
• Figure 4: Comparison between the proposed AEPHORA and other baseline schemes.