The downlink channel state information (CSI) estimation and low overhead acquisition are the major challenges for massive MIMO systems in frequency division duplex to enable high MIMO gain. Recently, numerous studies have been conducted to harness the power of deep neural networks for better channel estimation and feedback. However, existing methods have yet to fully exploit the intrinsic correlation features present in CSI. As a consequence, distinct network structures are utilized for handling these two tasks separately. 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 effectively capture and restructure correlation features, a self-mask-attention coding is proposed, complemented by an active masking strategy designed to improve efficiency. The channel estimation is achieved through the decoder part, wherein a lightweight multilayer perceptron denoising module is utilized for further accurate 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.

翻译：下行链路信道状态信息（CSI）估计与低开销获取是频分双工大规模MIMO系统实现高MIMO增益的主要挑战。近年来，大量研究致力于利用深度神经网络提升信道估计与反馈性能。然而，现有方法尚未充分挖掘CSI中的内在相关性特征，导致这两个任务需分别采用不同的网络结构处理。为实现联合信道估计与反馈，本文提出一种基于编码器-解码器的网络，揭示了CSI矩阵中固有的频域相关性。整个编码器-解码器网络用于信道压缩。为有效捕获并重构相关性特征，本文提出一种自遮蔽注意力编码方法，并辅以提升效率的主动遮蔽策略。通过解码器部分实现信道估计，其中轻量化多层感知机去噪模块用于进一步提高估计精度。大量实验表明，本方法不仅在联合任务中优于现有最先进信道估计与反馈技术，在独立任务中同样能获得有益性能。