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Machine Learning-Based Adaptive Codebook Design and Beamforming for Near-Field Communications
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DOI：10.1109/TCOMM.2025.3637068.png)
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Abstract
En 中文
Extremely large-scale antenna arrays (XL-arrays) and ultra-high frequencies are two fundamental technologies for future sixth-generation (6G) wireless networks, providing enhanced system capacity and substantial bandwidth expansion. To fully leverage these technological advancements, conventional far-field models must be replaced by more accurate near-field spherical-wave propagation models. This paper investigates a near-field communication system comprising a hybrid analog-digital beamforming base station (BS) and multiple mobile users, aiming to maximize system sum-rate through optimized codebook design, beam selection, and digital precoding. To accommodate dynamic user distributions, we propose two model-agnostic meta-learning (MAML)-based frameworks that enable prompt adaptation by learning well-initialized models for fine tuning. The first framework integrates the MAML method with a deep neural network (DNN) to design near-field codebooks tailored to the user distributions, addressing the limitations of conventional uniform codebooks. The second framework employs a joint neural network (NN) for beam selection and digital precoding, combining deep reinforcement learning (DRL) and deep unfolding. The DRL NN formulates beam selection as a Markov Decision Process, while the deep-unfolding NN approximates optimal digital precoding through a lightweight iterative algorithm without matrix inversion. Simulation results show that the proposed frameworks significantly outperform conventional methods, achieving superior generalization and overall performance in dynamic near-field scenarios.
Keywords:
Near-field
codebook design
meta-learning
deep reinforcement learning
deep-unfolding
Journal
IF:
8.3
Papers:
1.2W
Citations:
3.6W