Multi-Cell Cooperative Resource Allocation and Performance Evaluation for Roadside-Assisted Automated Driving

Abstract

The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks.

Keywords

• vehicular network communications
• performance evaluation
• spectrum resource allocation
• multi-agent reinforcement learning
• option-critic architecture