Channel Estimation for IRS-Aided OTFS System Using Dilated Attention GAN

This paper presents a channel estimation method for an intelligent reflecting surface (IRS)-aided orthogonal time-frequency spacing (OTFS) system in a dynamic scenario. Current channel estimation techniques for IRS-aided OTFS systems are built upon explicit channel model assumptions, which can constrain their adaptability in intricate environments. Furthermore, their reliance on pilot signals introduces significant pilot overhead in high-speed scenarios. To address these issues, we propose a dilated attention generative adversarial network (DAGAN) that has a novel architecture for capturing long-range dependency among data symbols separated in the delay-Doppler (DD) domain for estimating channels. Furthermore, the DAGAN includes an attention block to extract essential features from data symbols for channel information generation. This mechanism is guided by least square (LS) estimates of specific DD paths, serving as additional information for the DAGAN. Experimental results illustrate that the DAGAN method performs the best with the least NMSE with limited pilot overhead in comparison to other methods.