Gaussian AMP Aided Model-Driven Learning for OTFS System

Orthogonal time frequency space (OTFS) modulation, which converts the fast time-varying channel in the time-frequency domain into a time-invariant channel in the delay-Doppler(DD) domain, can mitigate the fading caused by the dynamic change of the channel. Nevertheless, due to the influence of the Doppler effect, it is challenging for traditional algorithms to acquire the desired detection results. To this end, this letter proposes a Gaussian approximate message passing (GAMP) aided model-driven signal detection algorithm. It first expands the existing GAMP algorithm into a corresponding deep neural network. Then, a unique set of trainable parameters, including one damping factor and two stepping factors, are constructed to improve the algorithm’s convergence. Simulation results demonstrate that the designed model-driven signal detection algorithm outperforms the original GAMP by about 2 dB at the bit error ratio (BER) of $1\times 10^{-4}$ .