The PAPR Reduction Algorithm Based On Segmentation Replacement And Particle Swarm In PTS

Orthogonal frequency division multiplexing(OFDM) is a multichannel modulation technology with multiple orthogonal subcarriers, and it can overcome frequency selective fading in multipath. However, OFDM system has a high peak-to-average power radio(PAPR) causing a hard requirement of linear dynamic range of the device, while it has a more compact spectral utilization. In this way, how to reduce PAPR in the OFDM system becomes one of the major issues in this filed.This paper introduces the basic principle of OFDM technology, and the definition, the causes and the influence of a high PAPR in OFDM are analyzed. Then, combining Genetic Algorithm(GA) and Particle Swarm Optimization Algorithm(PSO) on the basis of the research of PTS, two improved algorithms are proposed.The first improved algorithm is proposed to overcome high computational complexity of PTS in the traditional GA. A threshold and segmentation replacement strategy was used in the algorithm. The advisable threshold for terminating optimization is introduced to reduce the complexity without reducing the optimize performance. There is also a segment replacement scheme combing clone population with member population to improve the utilization of fine species and overcome premature convergence.The second improved algorithm is proposed to improve low utilization ratio of population in PSO and high complexity in PTS and to overcome shortcomings where the PSO algorithm is easy to fall into local optimal solution with low convergence speed. First, the statistics of the particles to adapt to rules was used to classify the particle swarm. Then, different categories of particles evolved with different model. Let the worst particles evolution with "social model", which can improve its convergence speed. The best particles evolved with "cognitive model", which can improve the convergence precision. The others evolution with "complete model" and adjust the learning factor dynamically, thus can improve the optimize performance greatly.