An Adaptive Framework for Resource Allocation Management in 5G Vehicular Networks

Abstract

Vehicle-to-everything (V2X) communication is crucial in vehicular networks, for enhancing traffic safety by ensuring dependable and low latency services. However, interference has a significant impact on V2X communication when channel states are changed in a high mobility environment. Integration of next generation cellular networks such as 5G in V2X communication can solve this issue. Also, successful resource allocation among users achieves a better interference control in high mobility scenarios. This work proposes a novel resource allocation strategy for 5G cellular V2X communication based on clustering technique and Deep Reinforcement Learning (DRL) with the aim of maximizing systems energy efficiency and MVNO’s profit. DRL is used to distribute communication resources for the best interference control in high mobility scenarios. To reduce signalling overhead in DRL deployments, the proposed method adopted RRH grouping and vehicle clustering technique. The overall architecture is implemented in two phases. The first phase addresses the RRH grouping and vehicle clustering technique with the objective of maximising the energy efficiency of the system and the second phase addresses the technique of employing DRL in conjunction with bidding to optimise MVNO’s profit. Second phase addresses the resource allocation which is implemented in two level stage. First level addresses the bidding of resources to BS using bidding and DRL techniques and the second level addresses the resource allocation to users using Dueling DQN technique. Through simulations, the proposed algorithm's performance is compared with the existing algorithms and the results depicts the improved performance of the proposed system.