论文标题
1位基于CS的融合学习与双向频道互惠的叠加CSI反馈
Fusion Learning for 1-Bit CS-based Superimposed CSI Feedback with Bi-Directional Channel Reciprocity
论文作者
论文摘要
由于丢弃了下行链路通道状态信息（CSI）幅度以及使用迭代重建算法的使用，因此1位压缩感测（CS）基于叠加的CSI反馈受到低恢复精度和较大的处理延迟的挑战。为了克服这些弊端，这封信通过利用双向渠道互惠来提出融合学习方案。具体而言，常规下行链路CSI重建的简化版本用于提取下行链路CSI的初始特征，并且设计了一个基于隐藏的层的振幅学习网络（AMPL-NET），旨在学习下链路CSI振幅的辅助功能。然后，基于提取和学习的振幅特征，开发了一个简单但有效的振幅融合网络（AMPF-NET），以执行下行链接CSI的幅度融合，从而提高了基于1位CS的CS叠加CSI反馈的重建精度，同时减少处理延迟。仿真结果显示了提出的反馈方案的有效性以及针对参数变化的鲁棒性。
Due to the discarding of downlink channel state information (CSI) amplitude and the employing of iteration reconstruction algorithms, 1-bit compressed sensing (CS)-based superimposed CSI feedback is challenged by low recovery accuracy and large processing delay. To overcome these drawbacks, this letter proposes a fusion learning scheme by exploiting the bi-directional channel reciprocity. Specifically, a simplified version of the conventional downlink CSI reconstruction is utilized to extract the initial feature of downlink CSI, and a single hidden layer-based amplitude-learning network (AMPL-NET) is designed to learn the auxiliary feature of the downlink CSI amplitude. Then, based on the extracted and learned amplitude features, a simple but effective amplitude-fusion network (AMPF-NET) is developed to perform the amplitude fusion of downlink CSI and thus improves the reconstruction accuracy for 1-bit CS-based superimposed CSI feedback while reducing the processing delay. Simulation results show the effectiveness of the proposed feedback scheme and the robustness against parameter variations.
