Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems

Channel estimation constitutes a pivotal concern within the realm of practical massive multiple-input multiple-output (MIMO) systems. Recently, numerous studies have been conducted to harness the power of deep neural networks for better channel estimation and feedback. However, they often overlook a crucial factor: the intrinsic correlation features present in downlink channel state information (CSI). As a consequence, in challenging environments, the performance of channel estimation and feedback frequently falls short of expectations. To achieve joint channel estimation and feedback, this paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. To efficiently capture and reconstruct correlation features, we propose a self-mask-attention coding mechanism, augmented by an active masking strategy aimed at enhancing operational efficiency. Besides, this paper employs a streamlined multilayer perceptron denoising module to achieve more precise estimations in the decoder part for channel estimation. Extensive experiments demonstrate that our method not only outperforms state-of-the-art channel estimation and feedback techniques in joint tasks but also achieves beneficial performance in individual tasks.