DL-Based MIMO-OTFS With Hardware Impairments

Orthogonal time frequency space (OTFS) modulation is positioned to be a potential waveform for 6G communications, owing to its capability to multiplex data in the delay-Doppler (DD) domain, making it robust against doubly-selective wireless communications channels. OTFS leverages a 2D transformation that effectively transform a doubly-dispersive channel into one with minimal fading characteristics. In this paper, we develop a deep learning-based signal detector for a MIMO-based OTFS system (termed as DL-MOTFS) with hardware impairments (HIs). We assess the performance of the proposed DL-MOTFS for a range of system parameters, including those related to HIs, user velocity, OTFS frame size, and the number of transmit and receive antennas. Through comprehensive comparisons against the conventional MMSE (minimum mean square error) signal detector, we find that the proposed DL-MOTFS consistently outperforms the MMSE detector, exhibiting a performance gain of around 3–5 dB.