Intra-Net Cognitive Radio Intelligent Utility Maximization using Adaptive PSO-Gradient Algorithm

Artificial intelligence now days are mainly dependent on deep learning techniques as it is rapidly growing and capable to outperform other approaches and even human at various problems. Intelligently utilizing resources that meets the growing need of demanding services as well as user behavior is the future of wireless communication systems. Autonomous learning of wireless environment at run time by reconfiguring its operating mode that maximize its utility, cognitive radio (CR) can be programmed and configured dynamically and their utility maximization inside a building is a challenging task. Re-configurability and perception are the key features of cognitive radio while latest machine learning techniques like deep learning is used for system adaptation. In this paper an adaptive model to enhanced cognitive radio utilization to be maximized is proposed, that is, Particle swarm optimization (PSO) in combination with Gradient-method and intends to maximize the utility of CR. For this purpose the primary objective is allocation of optimum powers to base stations (BSs), which is achieved in an iterative manner keeping in view power constraints. A novel Distributed power PSOGradient Algorithm (DPPGA) is introduced, which assures utility maximization under network power constraints. The information regarding utility and interference of an individual BS is available to all of BSs, which is a key parameter, exploited in the proposed algorithm. Simulations are carried out by considering different scenarios and results are compared with existing algorithms. The performance of proposed algorithm is remarkable.