Power Optimization in a Non-Coordinated Secondary Infrastructure in a Heterogeneous Cognitive Radio Network
In this paper we describe a novel approach that combines dynamic spectrum allocation and transmission power optimization for the secondary network users in an heterogeneous cognitive radio network. The proposed approach builds upon reinforcement learning and convex optimization procedures. Furthermore, the several key components, i.e. inter-cell interference, path loss, and fading have been considered when designing the power optimization algorithm. Simulation results show that the proposed approach improves the QoS of the system by up to 10 dB in terms of SINR and by up to 4% in terms of spectral efficiency while maintaining the average dissatisfaction probability close to that of the non-optimized approach.