Ellipsoid-Based Learning for Robust Resource Allocation With Differentiated QoS in Massive Internet of Vehicles Networks

Abstract

In massive Internet of Vehicles (mIoV) networks, the substantial number of vehicles and the dense deployment of communication devices lead to extensive overlap in signal coverage, resulting in frequent co-channel interference (CCI) and adjacent-channel interference (ACI). This interference significantly impacts the Quality of Service (QoS) experienced by vehicles. Moreover, the QoS requirements for vehicles vary widely across different application scenarios, further complicating the design and optimization of resource allocation strategies. To this end, we propose a robust resource allocation method for differentiated QoS in mIoV, leveraging ellipsoid learning and joint interference management. First, a weighted interference hypergraph model is developed to effectively mitigate ACI and CCI experienced by vehicles, while simultaneously addressing the diverse QoS requirements across different vehicle applications. Then, a robust optimization model for vehicle-to-vehicle pair (VP) communications is proposed, and underlying spectrum sharing is adopted to improve spectrum utilization. Furthermore, an ellipsoid-based learning robust algorithm (ELRA) strategy is proposed to address dynamic uncertainties arising from imperfect channel state information (CSI), thereby enhancing the reliability and transmission rate of the communication link. Simulation results demonstrate that the proposed algorithm achieves superior network throughput and spectral efficiency performance in densely deployed mIoV.