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

Research 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.