A Codebook Design for FD-MIMO Systems with Multi-Panel Array

In this work, we study codebook designs for full-dimension multiple-input multiple-output (FD-MIMO) systems with a multi-panel array (MPA). We propose novel codebooks which allow precise beam structures for MPA FD-MIMO systems by investigating the physical properties and alignments of the panels. We specifically exploit the characteristic that a group of antennas in a vertical direction exhibit more correlation than those in a horizontal direction. This enables an economical use of feedback bits while constructing finer beams compared to conventional codebooks. The codebook is further improved by dynamically allocating the feedback bits on multiple parts such as beam amplitude and co-phasing coefficients using reinforcement learning. The numerical results confirm the effectiveness of the proposed approach in terms of both performance and computational complexity.

Deep Learning-based Limited Feedback Designs for MIMO Systems

We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.