Inner Selection Learning Strategy with Chameleon Swarm Optimization Algorithm Based PAPR Reduction in MIMO-OFDM

The Multiple Input Multiple Output (MIMO) technique is a reliable method integrated with the Orthogonal Frequency Division Multiplexing (OFDM) scheme to deliver enhanced Quality of Service (QoS) for multimedia wireless communication.However, the high Peak-to-Average Power Ratio (PAPR) in OFDM signals causes signal distortion in the nonlinear region of the High-Power Amplifier (HPA), leading to an increase in the Bit Error Rate (BER).To address these issues, an Inner Selection Learning (ISL) strategy is employed alongside the Chameleon Swarm Optimization (CSO) algorithm for PAPR reduction in OFDM.The proposed ISL strategy helps minimize the search space for phase factor combinations, making the Partial Transmit Sequence (PTS) scheme more computationally feasible without compromising PAPR reduction performance.The CSO algorithm dynamically optimizes the phase factors and adapts to varying channel conditions and system environments.The integration of ISL with the CSO algorithm allows the system to select the best phase factors to minimize PAPR under diverse operating conditions.Experimental results show that the proposed method achieved a Bit Error Rate (BER) of 4.3 and a PAPR of 2 dB after 26 iterations, outperforming existing approaches such as Ant Colony Optimization (ACO) and the Osprey Optimization Algorithm (OOA).