Hybrid Quantum Deep Convolutional Generative Adversarial Networks for Channel Prediction and Performance Enhancement in Large-Scale MIMO-OFDM Systems

Abstract

Multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems demand accurate channel prediction for optimal performance. This research presents an innovative approach employing a hybrid quantum deep convolutional generative adversarial network (HQDCGAN) to enhance channel prediction, minimize error vector magnitude, reduce peak power, and mitigate adjacent channel leakage ratio (HQDCGAN-MIMO-OFDM) proposed. This approach implements a peak-to-average power ratio (PAPR) reduction module utilizing HQDCGAN trained with lower PAPR data acquired through simplified clipping with filtering (SCF) technique. The proposed HQDCGAN architecture leverages pyramidal dilated convolutions and attention mechanisms to extract multi-scale features from OFDM channel data. By incorporating attention mechanisms, the model dynamically focuses on crucial information, refining the channel prediction process. The primary objective is to exploit the network's capability to learn complex spatial–temporal correlations within OFDM channel signals. These strategy goals are to significantly improve the accuracy and, robustness of channel prediction, leading to minimized error vector magnitude (EVM) and mitigated issues related to peak power and adjacent channel leakage ratio (ACLR). To validate the efficiency of the proposed HQDCGAN-MIMO-OFDM the evaluation metrics such as spectral efficiency, peak-to-average power ratio, BER, SNR, and throughput are quantitatively analyzed. The proposed method CP-LSMIMO-OFDM-HQDCGAN gives 20.67%, 12.78%, and 19.56% low bit error rate, 21.66%, 23.09%, and 25.11% low reduction in PAPR and 23.76%, 30.45% and 18.97% high throughput with existing methods like TOP-ADMM, RNN-DNN-MIMO-OFDM, and IA-MIMO-OFDM methods, respectively.