Adaptive Communication for Capacity Enhancement: A Hybrid Intelligent Approach

Adaptive/dynamic Resource allocation plays a vital role in Wireless Communication Systems (WCS). This paper deals with hybridization of the two evolutionary computing techniques namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) investigated for adaptive resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA). It involves adaptive subchannel and power allocation. OFDM is the most widely used in almost every communication network ranging from DAB, DVB, 3G–5G mobile, WiMAX and WiFi etc. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best possible gain by proposed hybrid optimization technique coined as Sequential Particle Swarm Optimization-Genetic Algorithm (SPSO-GA) and Sequential Genetic Algorithm-Particle Swarm Optimization (SGA-PSO). The intention of the work is to improve the total capacity under the total power and fairness constraints. The sum capacity is increased and the computational time is reduced, compared the existing techniques. The simulation results reveal a notable enhancement in the performance of the algorithm with increase in number of users supported. The capacity obtained by SPSO-GA is found better than existing algorithms.