PAPR Reduction in MIMO-OFDM System Using Artificial Neural Network

MIMO (multiple input multiple output) with OFDM (orthogonal frequency di- vision multiplexing) had started off as a viable option for saving bandwidth, as well as increasing the system's variety in wireless communication. The prominent drawback in multi-carrier signals is significantly large envelope fluctuations corresponding to high PAPR (peak-to-average power ratio). The major goal of this work is to suggest and concentrate on an effective hybrid technique for lowering PAPR while maintaining BER performance. A neural network-based module with AGP (active gradient project) and PTS (partial transmit sequence) has been proposed for this.