A novel LOS/NLOS channel learning approach based on PSO algorithm

Particle swarm optimization (PSO) is a swarm intelligence based computer algorithm that is used to find a solution in the search space of an optimization problem. Adding frequency diversity, through subcarrier redundancy, in orthogonal frequency-division multiplexing (OFDM) is a popular approach to improve the robustness of the system. However, frequency redundant OFDM system is prone to high peak-to-average power ratio (PAPR), due to the fact that the same source information is transmitted on multiple subcarriers. Existing schemes such as Selective Mapping (SLM) and partial transmit sequence (PTS) are effective but difficult to implement due to the high computation complexity. In this paper, we propose a two stage PAPR reduction method. We analyze the computational complexity and extensive simulations on the PAPR and show that our scheme considerably reduces the computational complexity while achieving similar PAPR reduction as SLM and better PAPR reduction than PTS.