Allocation and sharing of spectrum resources based on cognitive mechanisms.

This paper provides an overview of existing research and applications in the field of magnetic spectrum channel allocation, and finds that the demand for spectrum resources is still growing rapidly, while the limited spectrum resources are not fully utilized under the traditional fixed allocation strategy. To address the problem of allocation and sharing of magnetic spectrum resources in cognitive wireless networks, we propose an exploration of the application of cognitive reinforcement learning in the allocation and sharing of magnetic spectrum cognitive wireless networks by combining the efficient decision-making capability of reinforcement learning, the representational capability of deep learning and the self-learning capability of cognitive learning. Simulation experimental results indicate that the method can make spectrum allocation more efficient and flexible, and achieve certain results in various scenarios, while verifying the possibility of applying cognitive reinforcement learning in the field of spectrum allocation, and laying the foundation for the final realization of efficient and high-performance spectrum allocation.