无监督学习CSI去噪与反馈
Learning the CSI Denoising and Feedback Without Supervision
AI总结：
针对频分双工系统中信道状态信息反馈开销大的问题，提出基于学习的联合去噪与反馈框架，仅用基站侧带噪上行数据训练可泛化下行的自编码器，将半部分部署到终端实现高效无训练反馈，性能优异。
摘要：
本研究针对频分双工系统中的信道状态信息，提出了一种联合去噪与反馈策略。在这类系统中，最大的挑战在于移动终端需向基站发送下行链路信道状态信息或对应的部分信息，以便基站后续恢复完整估计值，这一过程会产生巨大的开销。为此，我们提出了一种基于学习的新型框架，用于信道估计的去噪与压缩。与现有研究不同，我们拓展了近期提出的一种方法，并证明仅利用基站可获取的带噪上行链路数据，就能够学习出一个可泛化至下行链路数据的自编码器神经网络。随后，可将自编码器的一半部署到移动终端，使其能以尽可能高的效率生成信道反馈，且终端无需进行任何训练，也无需传输对应的训练数据。数值仿真结果表明，所提方法具备优异的性能。
英文摘要：
In this work, we develop a joint denoising and feedback strategy for channel state information in frequency division duplex systems. In such systems, the biggest challenge is the overhead incurred when the mobile terminal has to send the downlink channel state information or corresponding partial information to the base station, where the complete estimates can subsequently be restored. To this end, we propose a novel learning-based framework for denoising and compression of channel estimates. Unlike existing studies, we extend a recently proposed approach and show that based solely on noisy uplink data available at the base station, it is possible to learn an autoencoder neural network that generalizes to downlink data. Subsequently, half of the autoencoder can be offloaded to the mobile terminals to generate channel feedback there as efficiently as possible, without any training effort at the terminals or corresponding transfer of training data. Numerical simulations demonstrate the excellent performance of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
