Continuous-unconstrained and global optimization for PSO-PTS based PAPR reduction of OFDM signals

Orthogonal frequency division multiplexing (OFDM) is a superior technology for the high-speed data rate of wire-line and wireless communication systems. However, one of the major drawbacks of OFDM signals is the high peak-to-average power ratio (PAPR) problem inherent in 5G waveform design. High PAPR causes OFDM signal distortion in the nonlinear region of the high power amplifier (HPA), and signal distortion leads to a decrease in bit error rate (BER). Partial transmit sequence (PTS) technique is a very attractive technique for PAPR reduction. However, to match the optimum condition on PTS for PAPR reduction, the computational unpredictability and cost of traditional PTS strategy are enormous, thus it is urgent to enhance computational efficiency to obtain the optimal PTS. In this paper, an improved scheme called Continuous-Unconstrained Particle Swarm Optimization based PTS (CUPSO-PTS) technique for optimum phase rotation factors searching is presented. A class of continuous-phase PTS schemes has been proposed to obtain the global optimal phase factor, and the theoretical boundaries can be determined in the continuous-unconstrained searching space. Conversely, when the phase factor values in continuous-unconstrained domain, the equivalent unconstrained PTS optimization can drastically accelerate convergence and reduce total calculation cost. In this paper, we compare the performance of Binary PSO based PTS (BPSO-PTS) scheme and Elitist Genetic Algorithm based PTS (EGA-PTS) scheme for 16-QAM modulation scheme. Theoretical analysis and simulations show that the proposed CUPSO-PTS scheme could provide a significant PAPR reduction in the OFDM system, which outperforms the OFDM systems with the traditional PTS scheme by 0.55 dB at CCDF of 10−3 in PAPR reduction. And 84.74% computational complexity is saved.