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 one-for-all deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a 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 compression ratio. The one-sided one-for-all framework reduces model storage space, relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness and advantages of the proposed method.

翻译：为降低正交频分双工大规模多输入多输出（MIMO）系统中的多用户干扰并最大化频谱效率，基站（BS）需获取用户设备（UE）处估计的下行信道状态信息（CSI）。本文提出一种基于单侧通用深度学习框架的大规模MIMO CSI反馈新方法。该方法在UE端通过线性投影压缩CSI，并在BS端利用基于即插即用先验（PPP）的深度学习（DL）恢复CSI。所提出的CSI-PPPNet方法摒弃了无线信道响应的人工设计正则项，在交替优化框架中用基于深度学习的去噪器替代先验的近端算子。由此，一次训练完成的去噪DL模型可被重用至任意压缩比的CSI恢复任务。该单侧通用框架可减少模型存储空间、缓解联合模型训练与模型交付的负担，并适用于设备内存与计算能力受限的UE。在开放室内与城市宏场景下的广泛实验验证了所提方法的有效性与优越性。