In this work, we propose a joint adaptive codebook construction and feedback generation scheme in frequency division duplex (FDD) systems. Both unsupervised and supervised deep learning techniques are used for this purpose. Based on a recently discovered equivalence of uplink (UL) and downlink (DL) channel state information (CSI) in terms of neural network learning, the codebook and associated deep encoder for feedback signaling is based on UL data only. Subsequently, the feedback encoder can be offloaded to the mobile terminals (MTs) to generate channel feedback there as efficiently as possible, without any training effort at the terminals or corresponding transfer of training and codebook data. Numerical simulations demonstrate the promising performance of the proposed method.

翻译：在这项工作中,我们提议在频率司双面(DFD)系统中建立一个联合的适应性代码构建和反馈生成计划,为此目的使用未经监督和监督的深层学习技术,根据最近发现的上链和下链(DL)频道国家信息等同神经网络学习,用于反馈信号的代码和相关深度编码器仅以UL数据为基础,随后,反馈编码器可以卸载到移动终端(MTs),以便尽可能有效地生成频道反馈,而无需在终端进行任何培训或相应转让培训和代码簿数据。