To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a deep learning (DL) based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. In this way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary linear projections. In addition to the one-for-all property, the one-sided framework relieves the burden of joint model training and model delivery and could be applied at UEs with limited device memories and computation power, in comparison to the two-sided autoencoder-based CSI feedback architecture. This opens new perspectives for DL-based CSI feedback. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness of the proposed method.

翻译：摘要：在正交频分双工大规模多输入多输出（MIMO）系统中，为降低多用户干扰并最大化频谱效率，基站（BS）需获取用户设备（UE）估计的下行信道状态信息（CSI）。本文提出一种基于单侧深度学习框架的大规模MIMO CSI反馈新方法。在UE端，CSI通过线性投影进行压缩；在BS端，则利用深度即插即用先验（PPP）实现恢复。所提方法CSI-PPPNet摒弃了为无线信道响应手工设计正则化项的传统思路，在交替优化方案中采用基于深度学习（DL）的去噪器替代先验的近端算子。由此，针对去噪任务仅需训练一次的DL模型可被重新用于任意线性投影下的CSI恢复任务。除“一劳永逸”特性外，与基于双侧自编码器的CSI反馈架构相比，该单侧框架减轻了联合模型训练与模型交付的负担，且可应用于设备内存与计算能力有限的UE。这为基于DL的CSI反馈开辟了新视角。在开放室内与城市宏小区场景上的大量实验证明了所提方法的有效性。