论文标题
CSI-PPPNET：大量MIMO CSI反馈的一面一侧的一侧深度学习框架
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback
论文作者
论文摘要
为了减少多源干扰并最大程度地提高正交频划分的频谱效率，双层大量多输入多数输出（MIMO）系统，基本站（BS）需要在用户设备（UE）估算的下行链路通道状态信息（CSI）（CSI）。本文通过一个单方面的一对一的深度学习框架提出了一种新颖的方法，用于大量MIMO CSI反馈。 CSI通过UE处的线性投影压缩，并使用插件较高的先验（PPP）在BS上恢复BS。所提出的方法（即CSI-PPPNET）在交替的Opptricization方案中，不使用手工制作的正规化器进行无线通道响应，而是利用基于DL的DENOISOR来代替先前的近端操作员。这样，可以重新使用经过训练的DL训练的DL模型，以使用任意压缩比的CSI恢复任务。单方面的一侧框架减少了模型存储空间，减轻了联合模型培训和模型交付的负担，并且可以在设备记忆力和计算功率有限的UES上应用。对开放室内和城市宏观场景进行的广泛实验表明了该方法的有效性和优势。
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.
