A Novel Quantization Method for Deep LearningBased Massive MIMO CSI Feedback In massive multipleinput multipleoutput (MIMO) systems, channel state information (CSI) needs feeding back to the base station (BS) by user equipment (UE) to attain the potential benefits of massive MIMO. But the large number of antennas at the BS causes a huge feedback overhead, thereby making it prohibitive to realize CSI feedback in massive MIMO. Deep leaningbased (DL) compressive sensing methods for CSI feedback can potentially reduce the overhead significantly. However, without quantization, a databearing bitstream for transmission cannot be produced at the UE. In this paper, we propose a novel quantization framework and training strategy for DLbased CSI feedback, which not only makes the current CSI feedback network applicable in real communication systems but also minimizes the introduced quantization distortion to improve the reconstruction quality. Experimental results demonstrate that the proposed quantization method performs well and is robust to quantization errors.

Energy and SpectralEfficiency Tradeoff in Beam Domain Massive MIMO Downlink with Statistical CSIT We investigate energy and spectralefficiency tradeoff by optimizing resource efficiency (RE) for singlecell massive multipleinput multipleoutput (MIMO) downlink transmission with only statistical channel state information available at the transmitter (CSIT). We first show that beam domain transmission is favorable for RE optimization in massive MIMO downlink, by deriving a closedform solution for the eigenvectors of the optimal transmit covariance matrices. With this conclusion, the RE optimization precoding design is reduced to a realvalued power allocation problem. Exploiting sequential optimization and random matrix theory, we further propose a lowcomplexity twolayer waterfillingstructured power allocation algorithm. Numerical results demonstrate the effectiveness of our proposed statistical CSI aided RE optimization approach.