DL-based CSI Feedback and Cooperative Recovery in Massive MIMO

In this paper, we exploit the correlation between nearby user equipment (UE) and develop a deep learning-based channel state information (CSI) feedback and cooperative recovery framework, CoCsiNet, to reduce the feedback overhead. The CSI information can be divided into two parts: shared by nearby UE and owned by individual UE. The key idea of exploiting the correlation is to reduce the overhead used to repeatedly feedback shared information. Unlike in the general autoencoder framework, an extra decoder and a combination network are added at the base station to recover the shared information from the feedback CSI of two nearby UE and combine the shared and individual information, respectively, but no modification is performed at the UEs. For a UE with multiple antennas, we also introduce a baseline neural network architecture with long short-term memory modules to extract the correlation of nearby antennas. Given that the CSI phase is not sparse, we propose two magnitude-dependent phase feedback strategies that introduce statistical and instant CSI magnitude information to the phase feedback process, respectively. Simulation results on two different channel datasets show the effectiveness of the proposed CoCsiNet.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Sparse Bayesian Learning Approach for Discrete Signal Reconstruction

This study addresses the problem of discrete signal reconstruction from the perspective of sparse Bayesian learning (SBL). Generally, it is intractable to perform the Bayesian inference with the ideal discretization prior under the SBL framework. To overcome this challenge, we introduce a novel discretization enforcing prior to exploit the knowledge of the discrete nature of the signal-of-interest. By integrating the discretization enforcing prior into the SBL framework and applying the variational Bayesian inference (VBI) methodology, we devise an alternating update algorithm to jointly characterize the finite alphabet feature and reconstruct the unknown signal. When the measurement matrix is i.i.d. Gaussian per component, we further embed the generalized approximate message passing (GAMP) into the VBI-based method, so as to directly adopt the ideal prior and significantly reduce the computational burden. Simulation results demonstrate substantial performance improvement of the two proposed methods over existing schemes. Moreover, the GAMP-based variant outperforms the VBI-based method with an i.i.d. Gaussian measurement matrix but it fails to work for non i.i.d. Gaussian matrices.Comment: 13 pages, 7 figure