RORA: Reinforcement learning based optimal distributed resource allocation strategies in vehicular cognitive radio networks for 6G

Abstract

The next generation (5G/B5G) vehicular cognitive radio networks (VCRNs) flag the track to intelligence-based autonomous driving in the initiation of future wireless networking and make daily vehicular operation more convenient, greener, efficient, and safer. However, with the continuous evolution of vehicles, the vehicular network becomes large-scale, dynamic, and heterogeneous, making it tough to fulfill the strict necessities, such as high security, resource allocation, massive connectivity, and ultralow latency. The combination of cognitive radio (CR) networks (different network coexistence) and machine learning (ML) has arisen as an influential artificial intelligence (AI) approach to make both the communication system and vehicle more adaptable and efficient. Naturally, applying ML to VCRNs has become an active research area and is being extensively considered in industry and academia. In this work, a reinforcement learning (RL) based optimal resource allocation (RORA) technique is proposed to solve the myopic decision-making problem by an autonomous vehicle (RL agent) takes its action to select the power level and optimal sub-band and maximize long-term rewards with a maximum payoff in VCRNs. The aim of this work is to design and implement an intelligent, resource allocation framework that ensures efficient and adaptive spectrum utilization while minimizing communication latency, energy consumption, and transmission cost in VCRNs. As a schema for the realization and capabilities evaluations, the CR networks consisting of LTE cellular network inter-working with Wi-Fi network with constant inter-space between Wi-Fi access points (APs) installed along the pathway is analysed. This framework is further analysed with variable inter-space between Wi-Fi APs. The key research problem addressed in this work is the challenge of optimizing spectrum and power allocation in highly dynamic vehicular environments characterized by rapid mobility, fluctuating network conditions, and interference from multiple vehicular CR nodes. The results show that the proposed RORA technique is more operative and outperforms other resource allocation schemes in terms of prediction accuracy and throughput.

Impact Statement

In this work, we proposed a machine learning-based technique applied to vehicular networks and opportunistic spectrum access parts in cognitive radio networks. The proposed technique envisions ways of enabling AI toward a future intelligent transportation system (ITS), including network intelligentization and the development of intelligent radio (IR).