Meta-Learning-Driven Adaptive Codebook Design for Near-Field Communications

TL;DR

This paper introduces a meta-learning framework for adaptive near-field codebook design in 6G XL-array systems, enabling rapid adaptation to changing user distributions and channel conditions, thereby enhancing system sum-rate performance.

Contribution

It proposes a novel meta-learning-based approach combining MAML and deep neural networks for near-field codebook optimization in hybrid beamforming systems.

Findings

Outperforms conventional algorithms in simulations

Provides improved generalization to various channel conditions

Enables rapid adaptation to user distribution changes

Abstract

Extremely large-scale arrays (XL-arrays) and ultra-high frequencies are two key technologies for sixth-generation (6G) networks, offering higher system capacity and expanded bandwidth resources. To effectively combine these technologies, it is necessary to consider the near-field spherical-wave propagation model, rather than the traditional far-field planar-wave model. In this paper, we explore a near-field communication system comprising a base station (BS) with hybrid analog-digital beamforming and multiple mobile users. Our goal is to maximize the system's sum-rate by optimizing the near-field codebook design for hybrid precoding. To enable fast adaptation to varying user distributions, we propose a meta-learning-based framework that integrates the model-agnostic meta-learning (MAML) algorithm with a codebook learning network. Specifically, we first design a deep neural network (DNN) to learn the near-field codebook. Then, we combine the MAML algorithm with the DNN to allow rapid adaptation to different channel conditions by leveraging a well-initialized model from the outer network. Simulation results demonstrate that our proposed framework outperforms conventional algorithms, offering improved generalization and better overall performance.