Enhancing IoV Communication: RCAN-based Deep Learning Signal Detection Algorithm for OTFS System

The internet of vehicles (IoV) is becoming the key scenario in the current and future intelligent applications, pro-pelled by the advancements of communication technology and integration of artificial intelligence and the autonomous systems. Realizing the reliable data transmission in channel variability and estimation inaccuracies situation in such high-mobility vehicular networks is urgent. The orthogonal time frequency space (OTFS) can be utilized to achieve this destination. Therefore, we introduce an OTFS signal detection method using a residual channel attention network (RCAN), applied in a data-driven paradigm, recovers input signals from received information in an end-to-end manner, reducing the need for explicit channel estimation. The RCAN we designed substantially enhances the network's learning capabilities and optimizes its overall performance. Differentiate from the classic deep learning network-based OTFS signal detection algorithms, the simulation results reveal that the proposed RCAN-based OTFS signal detection scheme has a lower bit error rate (BER) under the same SNR situation, and also has lower space complexity. Furthermore, our method exhibits robustness when the transmission channel changes.