标题： 神经波束成形与多普勒感知稀疏注意力用于高移动性环境 显示英文标题

标题： Neural Beamforming with Doppler-Aware Sparse Attention for High Mobility Environments

摘要： 波束成形在多天线无线系统中对提高频谱效率和减轻干扰具有重要意义，促进了密集和高移动性场景下的空间复用和多样性。 传统的波束成形技术，如零强迫波束成形（ZFBF）和最小均方误差（MMSE）波束成形，在恶劣信道条件下会经历性能退化。 基于深度学习的波束成形通过从信道状态信息（CSI）到波束成形权重的非线性映射提供了一种替代方案，提高了对动态信道环境的鲁棒性。 基于Transformer的模型特别有效，因为它们能够建模时间与频率之间的长距离依赖关系。 然而，其二次注意力复杂度限制了在大型OFDM网格中的可扩展性。 最近的研究通过稀疏注意力机制解决了这个问题，从而降低了复杂度同时保持了表达能力，但通常采用的模式忽略了信道动态性，因为它们并非专门为无线通信场景设计。 在本工作中，我们提出了一种多用户单输入多输出（MU-SIMO）设置下的多普勒感知稀疏神经网络波束成形（Doppler-aware Sparse NNBF）模型。 所提出的稀疏结构可以根据信道动态性沿二维时频轴进行配置，并且理论上证明可以确保在p跳内保持完全连通性，其中p是注意力头的数量。 在城市宏小区（UMa）信道条件下的仿真结果表明，多普勒感知稀疏NNBF在高移动性场景中显著优于固定模式基线，即标准稀疏NNBF，以及传统的ZFBF和MMSE波束成形技术，同时保持结构化的稀疏性，并控制每个查询的注意键数量。 显示英文摘要

摘要： Beamforming has significance for enhancing spectral efficiency and mitigating interference in multi-antenna wireless systems, facilitating spatial multiplexing and diversity in dense and high mobility scenarios. Traditional beamforming techniques such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming experience performance deterioration under adverse channel conditions. Deep learning-based beamforming offers an alternative with nonlinear mappings from channel state information (CSI) to beamforming weights by improving robustness against dynamic channel environments. Transformer-based models are particularly effective due to their ability to model long-range dependencies across time and frequency. However, their quadratic attention complexity limits scalability in large OFDM grids. Recent studies address this issue through sparse attention mechanisms that reduce complexity while maintaining expressiveness, yet often employ patterns that disregard channel dynamics, as they are not specifically designed for wireless communication scenarios. In this work, we propose a Doppler-aware Sparse Neural Network Beamforming (Doppler-aware Sparse NNBF) model that incorporates a channel-adaptive sparse attention mechanism in a multi-user single-input multiple-output (MU-SIMO) setting. The proposed sparsity structure is configurable along 2D time-frequency axes based on channel dynamics and is theoretically proven to ensure full connectivity within p hops, where p is the number of attention heads. Simulation results under urban macro (UMa) channel conditions show that Doppler-aware Sparse NNBF significantly outperforms both a fixed-pattern baseline, referred to as Standard Sparse NNBF, and conventional beamforming techniques ZFBF and MMSE beamforming in high mobility scenarios, while maintaining structured sparsity with a controlled number of attended keys per query.