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

TL;DR

This paper introduces a novel codebook design for FD-MIMO systems with multi-panel arrays, leveraging physical properties and reinforcement learning to enhance beam precision and feedback efficiency.

Contribution

It proposes new codebooks tailored for multi-panel FD-MIMO systems, exploiting antenna correlation and dynamic feedback bit allocation via reinforcement learning.

Findings

Improved beamforming accuracy with fewer feedback bits.

Enhanced system performance demonstrated through numerical simulations.

Reduced computational complexity compared to traditional methods.

Abstract

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.