Performance of particle swarm optimization techniques on papr reduction for OFDM systems

Abstract

Partial transmit sequence (PTS) technique requires an exhaustive search over all combinations of allowed phase weighting factors, the search complexity increases exponentially with the number of sub-blocks. In this paper, a novel combing strategy that employs sub-block partition scheme and phase factors for PTS in orthogonal frequency division multiplexing (OFDM) system is proposed. We present an OFDM system, which through the use of sub-optimal PTS based on particle swarm optimization (PSO) algorithm, achieves low computation complexity to find optimum phase weighting factors and reduce the peak-to-average power ratio (PAPR). Furthermore, to work around potentially computational intractability, the proposed scheme exploits heuristics in consideration of both global and local exploration partial transmit sequence. Simulation results show that the proposed PSO-based PTS can efficiently find the optimum phase weighting factor, and can achieve the lower PAPR with moderate complexity.

A PAPR Reduction Method Based on Artificial Bee Colony Algorithm for OFDM Signals

“…But from the analysis in section IV-C, we can know that the search complexity of the ABC-PTS with K = 40 is only SK/W (M−1) = 1200/32768 = 3.66% of that by the OPTS. For the same or almost same search complexity, the OPTS TS−− [17] MDGA,P=30, [22], [23] ABC−PTS,S=30 ABC−PTS,S=40 PSO−PTS,S=30, [20], [21] RS−900, [15] RS−1200, [15] GD−− [16] performance of the ABC-PTS with K = 40 is also better than that of RS and GD. Table. I shows comparison of computational complexity among different methods for M = 16 subblocks, where the size of population for PSO-PTS [20], [21], MDGA [22], [23] and ABC-PTS are S = P = 30, the number of maximal generations or iterations are G = K = 30.…”

Reducing the Peak to Average Power Ratio of MIMO-OFDM Systems

“…The PSO-PTS is a scrambling technique that was proposed in the literature to reduce the PAPR in OFDM systems [8], [9]. Both research works solved the phase factors optimization problem by using PSO, which is an evolutionary method used to determine an optimum solution for any optimization problem such as PTS.…”

“…Both research works solved the phase factors optimization problem by using PSO, which is an evolutionary method used to determine an optimum solution for any optimization problem such as PTS. Hung et al [9] enhanced the performance of PSO based PTS by increasing the number of particles (Generations) when the phase weight factor was chosen from a small set. However, the relationship between the number of particles and the reduction of PAPR was not clearly investigated.…”

Optimal design of fractional-order PID controller for five bar linkage robot using a new particle swarm optimization algorithm

“…First, PSO has memory which utilizes the knowledge of good solutions retained by all particles, whereas in GA, previous knowledge of the problem is destroyed once the population is updated. Second, PSO has constructive cooperation between particles and particles in the swarm share their information (Hung et al 2008).…”