Adaptive resource allocation for multiuser OFDM system based on modified genetic algorithm and particle swarm optimization

Abstract

Like other wireless systems, Orthogonal Frequency Division Multiplexing (OFDM) requires the proper allocation of the limited resources, like total transmit power and available frequency bandwidth, among the users to meet the users' service requirements. As a matter of fact, adaptive resource allocation is one of the most challenging tas~s for multiuser OFDM systems. In this dissertation, two evolutionary approaches, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been applied for adaptive subcarrier and bit allocations to minimize the overall transmit power (margin adaptation) and to maximize the . throughput (rate adaptation) of a multiuser OFDM system. Each user will be assigned a number of subcarriers. This allocation of subcarriers may be done through unconstrained or I fairly scheduled approaches. The number of bits are then calculated according to channel state information and subcarrier arrangements. The transmit power level as well as bit rate for an OFDM symbol are evaluated through these subcarrier and bit information. Simulation results reveal that both the evolutionary approaches outperform the conventional static resource allocation schemes considerably both in unconstrained and constrained cases. The results further assert that both. the algorithms can handle large allocation I of subcarriers without significant performance degradation. However the performance of PSO has been found to be better than the GA in terms of execution time, simplicity and convergence. '. The original versions of GA and PSO have been modified in different manners to provide further improvements. All these modified versions perform relatively better than the original versions. Furthermore the modification of PSO has been done by three different manners where all of them perform relatively better than the original PSO as ~ell as the original and modified versions of GA. Finally all these modified versions have been compared with the existing algorithms. The comparison reveals the fact that the modified versions of PSO perform relatively much better results than the previously best algorithm for higher number of users.