Channel state information (CSI) plays a crucial role in the capacity of multiple-input and multiple-output systems, but CSI feedback occupies substantial precious transmission resources in frequency-division duplexing (FDD) systems. In this work, we propose a data hiding-based CSI feedback framework, namely, EliCsiNet, to eliminate the CSI feedback overhead in FDD systems through deep learning. The key idea is to hide/superimpose CSI in transmitted messages (e.g., images) with no transmission resource occupation and few effects on message semantics. Concretely, we introduce a novel neural network framework in which the user extracts and hides CSI features in images, and the base station recovers the CSI from the transmitted images. However, the essential source coding (e.g., JPEG compression) before data transmission causes two problems in the proposed EliCsiNet framework when applied in practical systems. First, the compression inevitably disturbs the information of the hidden CSI in images and affects the CSI reconstruction accuracy. Therefore, a two-stage separable training strategy, which includes coding-free end-to-end and coding-aware decoder-only training, is adopted to reduce these effects. Second, the bit length of the images coded via JPEG is unpredictable and uncontrollable, and CSI superimposition may lead to an increase in the bit length of the coded images. To avoid this issue, we divide a full image into several sub-blocks and select the one with the smallest length increment. Image entropy is also introduced to accelerate block selection. Simulation results demonstrate that the proposed EliCsiNet framework can eliminate the CSI feedback overhead with few effects on the features properties of transmitted images, including image quality and bit length.

中文翻译：

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通过基于深度学习的数据隐藏消除 CSI 反馈开销

信道状态信息(CSI)在多输入多输出系统的容量中起着至关重要的作用，但CSI反馈在频分双工(FDD)系统中占用了大量宝贵的传输资源。在这项工作中，我们提出了一种基于数据隐藏的 CSI 反馈框架，即 EliCsiNet，以通过深度学习消除 FDD 系统中的 CSI 反馈开销。关键思想是在传输的消息（例如，图像）中隐藏/叠加CSI，不占用传输资源，对消息语义影响很小。具体来说，我们引入了一种新颖的神经网络框架，其中用户提取和隐藏图像中的 CSI 特征，基站从传输的图像中恢复 CSI。然而，基本的源代码（例如，JPEG 压缩）在数据传输之前在所提出的 EliCsiNet 框架中在实际系统中应用时会导致两个问题。首先，压缩不可避免地会干扰图像中隐藏的 CSI 信息，影响 CSI 重建精度。因此，采用了两阶段的可分离训练策略，包括无编码端到端训练和仅编码感知解码器训练，以减少这些影响。其次，JPEG编码图像的位长是不可预测和不可控的，CSI叠加可能导致编码图像的位长增加。为了避免这个问题，我们将一个完整的图像分成几个子块，并选择长度增量最小的一个。还引入了图像熵来加速块选择。