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.

正交频分复用 (OFDM) 是一种用于有线和无线通信系统的高速数据速率的卓越技术。然而，OFDM 信号的主要缺点之一是 5G 波形设计中固有的高峰均功率比 (PAPR) 问题。高 PAPR 会导致高功率放大器 (HPA) 的非线性区域中的 OFDM 信号失真，而信号失真会导致误码率 (BER) 降低。部分传输序列 (PTS) 技术是一种非常有吸引力的降低 PAPR 的技术。然而，为了匹配 PTS 降低 PAPR 的最佳条件，传统 PTS 策略的计算不可预测性和成本是巨大的，因此迫切需要提高计算效率以获得最优 PTS。在本文中，提出了一种改进的方案，称为基于连续无约束粒子群优化的PTS（CUPSO-PTS）技术，用于最佳相位旋转因子搜索。提出了一类连续相位PTS方案以获得全局最优相位因子，理论边界可以在连续无约束搜索空间中确定。相反，当相位因子值在连续无约束域时，等效无约束PTS优化可以大大加快收敛速度​​并降低总计算成本。在本文中，我们比较了基于二进制 PSO 的 PTS (BPSO-PTS) 方案和基于精英遗传算法的 PTS (EGA-PTS) 方案在 16-QAM 调制方案中的性能。^-3降低 PAPR。并且节省了 84.74% 的计算复杂度。