Neural Codebook Design for Network Beam Management

TL;DR

This paper introduces a neural network-based codebook design method for network beam management that significantly improves beam alignment accuracy and spectral efficiency in large antenna systems.

Contribution

It proposes an end-to-end learned codebook design algorithm, network beamspace learning (NBL), for optimized interference mitigation and performance enhancement in beam management.

Findings

Over 10dB improvement in beam alignment accuracy.

More than 25% increase in network spectral efficiency.

Effective in large hybrid antenna arrays.

Abstract

Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large antenna systems. Mobile systems like 5G use a beam management framework that joins the initial access, beamforming, CSI acquisition, and data transmission. The design of codebooks for these stages, however, is challenging due to their interrelationships, varying array sizes, and site-specific channel and user distributions. Furthermore, beam management is often focused on single-sector operations while ignoring the overarching network- and system-level optimization. In this paper, we proposed an end-to-end learned codebook design algorithm, network beamspace learning (NBL), that captures and optimizes codebooks to mitigate interference while maximizing the achievable performance with extremely large hybrid arrays. The proposed algorithm requires limited shared information yet designs codebooks that outperform traditional codebooks by over 10dB in beam alignment and achieve more than 25% improvements in network spectral efficiency.