8 research outputs found
Deep Learning Approach to Channel Sensing and Hybrid Precoding for TDD Massive MIMO Systems
This paper proposes a deep learning approach to channel sensing and downlink
hybrid analog and digital beamforming for massive multiple-input
multiple-output systems with a limited number of radio-frequency chains
operating in the time-division duplex mode at millimeter frequency. The
conventional downlink precoding design hinges on the two-step process of first
estimating the high-dimensional channel based on the uplink pilots received
through the channel sensing matrices, then designing the precoding matrices
based on the estimated channel. This two-step process is, however, not
necessarily optimal, especially when the pilot length is short. This paper
shows that by designing the analog sensing and the downlink precoding matrices
directly from the received pilots without the intermediate channel estimation
step, the overall system performance can be significantly improved.
Specifically, we propose a channel sensing and hybrid precoding methodology
that divides the pilot phase into an analog and a digital training phase. A
deep neural network is utilized in the first phase to design the uplink channel
sensing and the downlink analog beamformer. Subsequently, we fix the analog
beamformers and design the digital precoder based on the equivalent
low-dimensional channel. A key feature of the proposed deep learning
architecture is that it decomposes into parallel independent single-user DNNs
so that the overall design is generalizable to systems with an arbitrary number
of users. Numerical comparisons reveal that the proposed methodology requires
significantly less training overhead than the channel recovery based
counterparts, and can approach the performance of systems with full channel
state information with relatively few pilots.Comment: 6 Pages, 4 figures, to appear in IEEE GLOBECOM 2020 Open Workshop on
Machine Learning in Communications (OpenMLC