Spectrum Allocation And Power Control Algorithm In Cognitive Wireless Networks

With the development of the fifth generation of mobile communication,terminal devices are increasing dramatically and radio spectrum resources are becoming more and more scarce.Cognitive radio is generally considered to be an effective measure to solve the problem of spectrum scarcity.In cognitive wireless networks,spectrum allocation and power control are the key technologies to utilize spectrum resources efficiently and rationally.In this paper,a spectrum allocation algorithm and a power control algorithm are proposed respectively.The main research contents are as follows:(1)In this paper,we propose a double auction based on mixed graph for heterogeneous spectrums.The primary users(sellers)are encouraged to lease their idle spectrums,while secondary users(buyers)can access the sellers'spectrums by winning in the auction.The proposed algorithm constructs a mixed graph which can more accurately coordinate the interference among buyers for the spectrums,and buyers can negotiate and shrink their coverage areas,maximizing the number of spectrum transactions and significantly improving spectrum utilization.Furthermore,considering the heterogeneity of the spectrums,buyers are allowed to express their personalized preferences for heterogeneous spectrums and create different interference graph for different spectrums.It is proved that the algorithm achieves truthfulness,individual rationality and budget balance,the three key economic properties required for economic-robust auctions.Simulation results show that the proposed algorithm is superior to the algorithm based on undirected graph in terms of spatial efficiency,buyer satisfaction and auction's revenue.(2)In this paper,we propose a non-cooperative power control algorithm based on DQN with multiple secondary users.In the algorithm,the primary users and the secondary users work in a non-cooperative way.The primary users adjust their transmit power based on their own power control policy.We developed a DQN learning-based method for the secondary users to learn how to adjust their transmit power such that eventually both the primary users and the secondary users are able to transmit their respective data successfully with required qualities of service.Multiple secondary users are trained simultaneously through sensor nodes interaction with the environment.After training,secondary users can intelligently select the optimal hybrid power control strategy,and a goal state can be reached from any initial states within only a few number of steps.Simulation results show that the proposed algorithm obtains a lot of benefits in terms of effectiveness and robustness.