A Novel DRT-Assisted Deep Learning-Based Channel Feedback Mechanism for Massive MIMO

The reduction of channel state information feedback overhead is crucial in fifth generation and emerging sixth generation technologies involving massive multiple input multiple output (MIMO) systems. While compressive sensing (CS) has been proposed to address this challenge, the involvement of discrete Fourier transform (DFT) in CS result into higher computational complexity. This paper introduces a novel discrete Rajan transform (DRT) based deep learning (DL) to enhance channel feedback in massive MIMO systems. Through simulations, we show that the proposed mechanism outperforms the DFT based DL algorithm by 126.19% and 81.81% for cosine correlation and normalized mean square error in channel estimation, respectively, across varying signal to noise ratio (SNR) scenarios. Notably, the proposed DRT based DL algorithm excels in low SNR conditions, showing its potential for practical implementation in real-world communication systems.