Autonomous optimization techniques for cognitive dynamic wireless systems

This paper shows autonomous optimization techniques for distributed cognitive radio architecture. Based on the context information exchange mechanism standardized in IEEE1900.4, we assume that the terminals can obtain various types of context information of radio access networks (RANs) and they can make optimal decisions by themselves using the shared information. In our research, we apply the neural network based algorithms, the learning algorithms and the distributed optimization algorithms, to realize autonomous optimization of the cognitive networks. We apply the proposed schemes to the heterogeneous type cognitive radio, whose radio resource usage can be optimized by appropriate RAN selection. First, we apply learning algorithms to appropriate parameter selection for each RAN to maximize the aggregated throughput using multiple RANs. We show that our developed autonomous learning system can autonomously improve the throughput. We also apply a neural network based optimization technique to optimize radio resource usage of the entire network. We show that the proposed scheme can distributively optimize the entire network as an autonomous cognitive dynamic wireless system.