To reduce the peak-to-average power ratio (PAPR) in the orthogonal frequency division multiplexing (OFDM) transmission technique, several reduction approaches have been used. Among these is the selective mapping (SLM) scheme, and while having been highly adopted, its considerable computational complexity for optimum phase factors search is challenging for practical systems. To overcome this issue with SLM while still reducing the PAPR, a variety of optimization algorithms have been applied for optimal phase factors search. Limitations in all these algorithms include the need for specific parameters for peak performance and a decrease in effectiveness for complicated problems that have a significant number of variables. In this work, a novel optimization algorithm, called teaching-learning–based optimization (TLBO), featuring less computational effort and no algorithm-specific parameter requirement, is applied to reduce the PAPR of the OFDM signal. MATLAB simulation results demonstrate that the proposed TLBO-SLM method efficiently performs better than conventional SLM and previously applied optimization algorithms.

为了降低正交频分复用（OFDM）传输技术中的峰均功率比（PAPR），已经使用了几种降低方法。其中之一是选择性映射（SLM）方案，尽管已被广泛采用，但对于最佳相位因子搜索而言，其相当大的计算复杂度对实际系统而言是具有挑战性的。为了克服SLM的问题，同时仍降低PAPR，已将多种优化算法应用于最佳相位因子搜索。所有这些算法的局限性包括：对于峰值性能需要特定的参数，并且对于具有大量变量的复杂问题，有效性的降低。在这项工作中，一种新颖的优化算法，称为基于教学学习的优化（TLBO），具有较少的计算工作量并且没有特定于算法的参数要求的特征被应用于减小OFDM信号的PAPR。MATLAB仿真结果表明，所提出的TLBO-SLM方法比常规SLM和先前应用的优化算法具有更好的性能。