A novel ant colony optimization algorithm for PAPR reduction of OFDM signals,International Journal of Communication Systems

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A novel ant colony optimization algorithm for PAPR reduction of OFDM signals
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-10-15 , DOI: 10.1002/dac.4648
Mehdi Hosseinzadeh Aghdam ₁ , Abbas Ali Sharifi ₂

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The high peak‐to‐average power ratio (PAPR) is the main challenge of orthogonal frequency division multiplexing (OFDM) systems. Partial transmit sequence (PTS) is a useful approach to diminish the PAPR. Although the PTS approach significantly decreases the PAPR, it requires to explore all possible sequences of phase weighting factors. Hence, the computational cost exponentially increases with the number of divided subblocks. This paper proposes a novel PTS technique based on ant colony optimization (ACO) to diminish the high PAPR and computational cost of OFDM systems. By the new representation of phase factors as a graph, the improved ACO algorithm is combined with the PTS method to explore the optimal compound of the phase rotation factors. Simulation results represent that the proposed ACO‐based PTS approach significantly reduces the PAPR and improves the computational cost at the same time. A comparative analysis of the other meta‐heuristics shows that the ACO‐PTS approach outperforms the genetic algorithm, particle swarm optimization, and gray wolf optimization in terms of reducing PAPR.

中文翻译：

一种新颖的蚁群优化算法，用于降低OFDM信号的PAPR

高峰均功率比（PAPR）是正交频分复用（OFDM）系统的主要挑战。部分传输序列（PTS）是减少PAPR的有用方法。尽管PTS方法显着降低了PAPR，但它需要探索所有可能的相位加权因子序列。因此，计算成本随着分割的子块的数量成指数增加。本文提出了一种基于蚁群优化（ACO）的新型PTS技术，以减少OFDM系统的高PAPR和计算成本。通过以图表形式表示相位因子，将改进的ACO算法与PTS方法结合使用，探索了相位旋转因子的最佳组合。仿真结果表明，所提出的基于ACO的PTS方法显着降低了PAPR，同时提高了计算成本。对其他元启发式算法的比较分析表明，在降低PAPR方面，ACO-PTS方法优于遗传算法，粒子群优化和灰狼优化。

更新日期：2020-12-03

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