Machine Learning Algorithms for Dynamic Spectrum Access in Wireless Networks

Dynamic Spectrum Access (DSA) is an important technological approach in wireless networks for efficient use of the radio frequency spectrum. In this work, they explore a new form of DSA by incorporating RL and CASS into the system. In the form of RL, we develop an autonomous agent, which learns its spectrum access behaviours through current and mature algorithms as well as through its environmental. In this system, the agent employs the context based system to aid the acquisition of information about the spectrum availablity, users demand and interference so as to improve information used for decision making. The proposed method improves the agent's capability to gain better spectrum access policies under dynamic environments to increase overall network bandwidth while limiting the delay that currently slows down communications. Results from simulation results show how the proposed system, involving RL, outperforms prior systems in DSA application and hence validates the system use to deal with spectrum limitation and resource utilization gap in wireless communication system. Through fresh insights on how to enhance adaptive spectrum management methods, more opportunistic wireless technology systems are provided with a basis for further improvement.