Self-Organizing Sustainable Spectrum Management Methodology in Cognitive Radio Vehicular Adhoc Network (CRAVENET) Environment: A Reinforcement Learning Approach

Abstract

The era of new and emerging technologies demand that the new challenges they bring about to be effectively tackled and resolved. One such key challenge is spectrum management, especially in Cognitive Radio Vehicular Adhoc Network (CRAVENET) environment. The large-scale deployment of multimedia and Internet of Things (IoT) applications generate the need to establish an efficient spectrum allocation mechanism. This paper proposes a centralized self-organizing spectrum management in the context of economic and social sustainability using reinforcement learning technique. The objective of the proposed approach facilitates economic and social justice. The social economic justice architecture is developed through a user demand level concepts. The spectrum management methodology has been developed in a CRAVENET environment for better quality of service (QoS) with low average latency. The proposed methodology is expected to be highly effective for its economic feasibility, social impact, user comfort, efficiency, and communication latency minimization requirements.