To reduce multiuser interference and maximize the spectrum efficiency in 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. 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, in comparison to the two-sided autoencoder-based CSI feedback architecture, 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. This opens new perspectives in the field of DL-based CSI feedback. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness of the proposed method.
翻译:为了减少多用户干扰和最大限度地提高频率分化的频度分化,使大量多投入多产出(MIMO)系统出现重叠,基地台需要使用用户设备估算的下链频道状态信息(CSI),本文介绍了通过单向深层学习框架大规模MIMO CSI反馈的新方法。 CSI通过在UE的线性预测压缩,并在BS使用深插插插插插插子前端(PPPP)恢复。拟议办法,即CSI-PPPNet,而不是使用手制的无线频道响应规范器,而是利用基于深层次学习(DL)的默认状态信息,以取代先前的准操作器,采用交替优化办法。这样,一个经过过一次培训的DL模式模式可以重新用于CSI恢复任务,同时进行任意的线性预测。除了一对一切特性外,与基于双向自动coder的CSI反馈结构相比,拟议的单向框架,即CSI-PPNet,是利用基于深层次框架来减轻基于深度学习的深度学习(DL)基建模培训和模型交付的深层空间,并在CUI 模拟实地试验中采用新的模拟模型,可以应用。