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

Intelligently utilizing resources to 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 considered key features of CR for system adaptation. In this paper an adaptive model to enhanced CR utilization to be maximized is proposed, i.e., 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 PSO-Gradient Algorithm (DPPGA) is introduced, which assures utility maximization under network power constraints. Simulations are carried out by considering different scenarios; Low power BSs deployed over 2 meter height are used to serve Mobile Stations (MSs) in triple story building, whereas size of building is 100 m * 50 m, size of room is 10 m * 10 m and corridor covering width of 5m. Omni directional antenna with 2.6 GHz is used in the simulation scenarios. Results are compared with existing algorithms; cooperative and non-cooperative schemes. The performance of proposed algorithm is remarkable.