Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

Abstract

ABSTRACTResearch in cognitive radio networks aims at maximized spectrum utilization by giving access to increased users with the help of dynamic spectrum allocation policy. The unknown and rapid dynamic nature of the radio environment makes the decision making and optimized resource allocation to be a challenging one. In order to support dynamic spectrum allocation, intelligence is needed to be incorporated in the cognitive system to study the environment parameters, internal state, and operating behaviour of the radio and based on which decisions need to be made for the allocation of under-utilized spectrum. A novel priority-based reserved allocation method with a multi-agent system is proposed for spectrum allocation. The multi-agent system performs the task of gathering environmental artefacts used for decision making to give the best of effort service in this adaptive communication.