A novel ant colony optimization algorithm for PAPR reduction of OFDM signals

Abstract

SummaryThe 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.