Energy-Efficient Resource Allocation for NOMA-Enabled Vehicular Networks

Abstract

Vehicular networks face significant challenges in achieving high energy efficiency (EE) while guaranteeing diverse quality of service (QoS) requirements of users, especially under limited bandwidth and power budgets in highly dynamic and dense topologies. To address these challenges, this study formulates a joint resource optimization problem to maximize the average EE of cellular users (CUs) and vehicle-to-vehicle (V2V) users by jointly optimizing subchannel assignment, frequency reuse patterns, and power allocation while ensuring the required QoS of both users. To solve the non-convex optimization problem, we propose a semi-persistent scheduling (SPS)-based energy-efficient resource allocation scheme that integrates non-orthogonal multiple access (NOMA) with network slicing (NS). Specifically, during the frequency reservation phase of SPS periods, CUs are assigned to network slices using the proposed NS grouping strategy, and V2V users are clustered into V2V NOMA clusters using the proposed clustering optimization algorithm. Frequency reuse patterns are then determined for network slices and V2V NOMA clusters. In the subsequent data transmission phase, a centralized energy-efficient iterative power control algorithm is introduced to enhance the average CU EE, and a distributed heuristic power control method is leveraged to improve the average V2V EE. Simulation results demonstrate that the proposed scheme outperforms the baseline methods in improving EE and satisfying the required QoS of both CUs and V2V users while avoiding over-allocation of frequency resources.