Enhancing mMIMO-SEFDM System through Deep Learning-Based Channel Estimation and Adaptive Parameter Selection

This paper presents a massive multiple-inputmultiple-output (mMIMO) system that utilizes spectrally efficient frequency division multiplexing (SEFDM) and integrates deep learning (DL) for improved channel estimation in the mMIMO-SEFDM. In contrast to the literature, this study utilizes the estimated channel feedback to dynamically adapt the SEFDM signal characteristics at the transmitter, which improves the system's agility. This enables the selection of the SEFDM signal's compression value and modulation order depending on the channel conditions, thus enhancing the symbol error rate (SER). The simulation findings demonstrate that using DLbased channel estimation and adaptive parameter selection for SEFDM leads to a better symbol error rate and improved spectral efficiency compared to orthogonal frequency division multiplexing (OFDM).