Swarm Intelligence aided PTS-Based Framework for PAPR Reduction in Satellite Communication System

High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) signals remains a significant challenge in satellite communications, posing a threat to signal quality and adjacent channel interference. This paper investigates swarm intelligence (SI)-based approaches, known for their promise to achieve high performance with low computational complexity, alongside the partial transmit sequence (PTS) technique, both in discrete and continuous modes. Specifically, we explore three SI algorithms: particle swarm optimization (PSO), quantum particle swarm optimization (QPSO), and ant colony optimization (ACO), aiming to mitigate complexity introduced by the search for optimal phase rotation factors within PTS. We conduct a thorough complexity analysis, comparing the computational time and complexity of these SI schemes against the optimal exhaustive search method. Additionally, we evaluate the implementation of the PSO algorithm-assisted PTS framework in the presence of a traveling wave tube amplifier (TWTA) model, employing the 16-quadrature amplitude modulation (16-QAM). Simulation results show that the proposed SI-aided PTS framework based on PSO achieves performance closer to that of the optimal exhaustive search method in terms of cumulative distribution function (CCDF) while exhibiting lower computational complexity compared to both the optimal exhaustive search method and the ACO and QPSO schemes.