## 2 Related Work  
More and more schemes are being designed to improve the effectiveness of CSI feedback and thus the signal transmission quality of massive MIMO systems. Firstly, CS methods commonly used in signal processing is introduced. However, the prerequisite for utilizing CS is that the channel is strongly sparse, which cannot be fully satisfied in practice [13]. In addition, CS methods do not fully exploit channel characteristics as random projection is performed and iterative CS methods may lead to slow reconstruction [14]. Machine learning, as an indispensable tool, has ushered in a revolution across various research domains, and it is no exception in CSI feedback [15]. Its branches, such as DL, reinforcement learning, and federated learning, are applied to different tasks [16]. As for CSI feedback, DL has gained widespread adoption. A convolutional neural network (CNN) named CsiNet is proposed in [14], demonstrating superior performance over traditional CS methods. Subsequent studies [12; 17; 18; 19], such as CRNet [12] and CLNet [18], have been conducted to improve feedback performance with computational overhead reduced. CsiNet+ [20] incorporates refinement theory in DL, thereby improving the original CsiNet at the expense of higher floating point operations (FLOPs). Numerous works focus on introducing new structures to exploit channel properties. For instance, CsiNet-LSTM [21] and Attention-CsiNet [22] utilize the long short-time memory (LSTM) module and the attention mechanism, respectively. These two modules are synergistically combined in [8] to capture the temporal correlation of channels. Algorithm SALDR [23] leverages self-attention learning in conjunction with a dense RefineNet. Despite optimized performance, these approaches result in a substantial increase in FLOPs. Multi-domain correlations have been adopted to leverage the geometrical wireless propagation information embedded within the CSI. For instance, the prediction neural network in [24] generates the current CSI based on the previous CSI sequence capitalizing on time correlation, which is shared by both the UE and the BS. Furthermore, the DualNetMAG architecture in [25] exploits the correlation between the magnitudes of the bidirectional channels, with the uplink channel serving as a feedback channel for the quantized CSI phase and a neural network-based encoder compressing the CSI magnitude. The framework SampleDL [26] extracts the correlation between channels over adjacent subcarriers to further reduce the feedback cost. In addition, some recent works [27] make the most of the correlation between neighboring UEs. Some methods concentrate on reducing the computational cost and optimizing the occupation of uplink bandwidth resources. MarkovNet [28] develops a deep CNN architecture based on the Markov chain with spherical normalization of the CSI, effectively lowering the computational complexity compared to other over-parameterized approaches. CAnet, which conducts downlink channel acquisition with the help of the uplink data, is designed in [29]. The approach encompasses pilot design, channel estimation, and feedback within a unified framework and saves notable feedback bits. In pursuit of reduced overhead and enhanced deployment compatibility, the authors of [30] propose an implicit feedback scheme named ImCsiNet. The UE spreads and superimposes the downlink CSI over the uplink UE data sequence in [31], which introduces DL to superimposed coding-based CSI feedback. Additionally, the BS extracts the downlink CSI and UE data from the received signal [32]. The remaining works focus on empowering the practical deployment of DL-based CSI feedback models. Given that the complete channel acquisition involves other modules such as channel estimation, joint training of multiple modules can not only simulate real communication scenarios but also maximize performance gains. Two joint channel estimation and feedback architecture named PFnet and CEFnet are introduced in [33]. Moreover, ref.