Energy-Effcient Optimization For Spectrum Management In Cognitive Radio Sensor Networks

Cognition based spectrum management leads to the high utilization rate of spectrum while increase the energy consumption of network nodes. Hence, how to extend the lifetime of networks has become the primary challenge for the resource-constrained Cognitive Radio Sensor Networks. Aimed at keeping trade-offs between the efficiency of spectrum and energy, research on spectrum sensing and decision in the Cognitive Radio Sensor Networks are conducted in this work.With sleeping schedule and censoring scheme integrated, a model for cooperative spectrum sensing with multi-objective optimization is established, through analyzing the global rate of detection and false alarm. And a fast multi-objective differential evolution algorithm is proposed to solve the multi-objective problem, which takes advantage of the opposition-based learning for initializing the population and the tournament scheme in mutation step. To accelerate the convergence rate and maintain the diversity, a dynamical adjustment scheme with a crossover parameter and a new population selection scheme are proposed, which could provide accurate information to assist the spectrum decision process.By analyzing the channel characteristics and the energy efficiency of networks, an adaptive spectrum decision scheme is proposed on the basis of spectrum sensing with multi-objective optimization. Then through establishing the learning model and designing the learning strategy selection scheme, a joint channel and power decision algorithm based on the distributed strategy estimation Q-learning is proposed to lower the communication overhead and energy consumption significantly. For speeding up the convergence rate, a method to adjust the learning rate dynamically according to the times of Q-value being visited is proposed, which could maximize the network performance and extend the lifetime of networks.Extensive Matlab simulation results have demonstrated the effectiveness of proposed schemes, i.e., cooperative spectrum sensing with multi-objective optimization and spectrum decision with optimized energy consumption.