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：

---

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，不占用传输资源并且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，其中用户提取并隐藏图像中的 CSI 特征，基站从传输的图像中恢复 CSI。然而，当在实际系统中应用时，数据传输之前的基本源编码（例如，JPEG压缩）在所提出的EliCsiNet框架中导致了两个问题。首先，压缩不可避免地扰乱图像中隐藏的CSI信息，影响CSI重建精度。因此，采用两阶段可分离训练策略来减少这些影响，其中包括无编码端到端训练和仅编码感知解码器训练。其次，JPEG编码的图像的比特长度是不可预测和不可控的，CSI叠加可能会导致编码图像的比特长度增加。为了避免这个问题，我们将完整图像分为几个子块，并选择长度增量最小的子块。还引入图像熵来加速块选择。仿真结果表明，所提出的 EliCsiNet 框架可以消除 CSI 反馈开销，并且对传输图像的特征属性（包括图像质量和位长度）影响很小。