A Comparative study of Genetic and Particle Swarm Optimization Algorithms for Minimizing the High PAPR in OFDM Systems, By Mehdi Hosseinzadeh Aghdam

Research

Title	A Comparative study of Genetic and Particle Swarm Optimization Algorithms for Minimizing the High PAPR in OFDM Systems
Type	Presentation
Keywords	Orthogonal frequency division multiplexing (OFDM); Peak-to-average power ratio (PAPR); Partial transmit sequence (PTS); Genetic algorithm (GA); Particle swarm optimization (PSO).
Researchers	Abbas Ali Sharifi، Mehdi Hosseinzadeh Aghdam

Abstract

Orthogonal frequency division multiplexing (OFDM) has been widely used in many wireless communication systems due to its high data transmission, spectral efficiency and its robustness against the frequency selective fading channels. But, unfortunately, the high peak-to-average power ratio (PAPR) is one of the most important limitations of an OFDM system. As a result of the high PAPR, the hardware complexity of RF high power amplifier (RF-HPA) will be increased. Partial transmit sequence (PTS) technique is one of the best solutions to reduce the high PAPR. The PTS technique usually suffers from exhaustive search complexity, especially when the number of sub-blocks is increased. In this study, we investigate genetic algorithm (GA) and particle swarm optimization (PSO) method in PTS based OFDM for 16-QAM modulation scheme. We compare the performance of GA-PTS and PSO-PTS in PAPR reduction ability. Simulation results indicate that the PSO-PTS is slightly better than GA-PTS with less computational complexity.