  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.
