模式缩放：基于波数域码本的近场宽带波束训练
Pattern Zooming: Near-Field Wideband Beam Training with Wavenumber-Domain Codebook
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中文总结 AI 辅助
针对极大规模MIMO系统近场波束训练开销高的问题，提出基于波数域码本的模式缩放方案，利用模式缩放效应开发TD辅助波束扫描策略，实现精准用户定位并提升波束成形增益、降低训练开销。
中文摘要 AI 辅助
近场波束训练对于挖掘极大规模多输入多输出（MIMO）系统的高增益潜力至关重要。为降低训练开销，现有研究大多利用波束斜视效应，采用极坐标域码本的时延（TD）波束成形，该技术可在特定距离上同时扫描多个角度。然而，这类方法仍因穷举式距离搜索而面临高开销问题。为应对这一挑战，我们提出一种基于模式缩放的近场宽带波束训练方案，采用波数域码本。具体而言，我们首先建立了改进的宽带平面波信道表示，在此基础上揭示了模式缩放效应：不同子载波上的波数域模式相对于中心频率呈现频率相关的缩放特性。利用该特性，我们开发了一种TD辅助的波束扫描策略，可同时探测多个波数方向，以快速获取完整的波数域模式。基于所获取的模式，我们进一步推导了精确的、无近似的闭式表达式，建立了接收机坐标与波数域模式之间的严格映射，仅需少量导频即可实现精准用户定位。最后，数值结果验证了所提方案在波束成形增益和训练开销两方面的优越性。
英文摘要
Near-field beam training is essential for harvesting the high-gain potential of extremely large-scale multiple input multiple output systems. To reduce training overhead, existing works have mostly leveraged the beam squint effect by utilizing time-delay (TD) beamforming with polar-domain codebook, which enables the simultaneous sweeping of multiple angles at a specific distance. However, such methods still suffer from high overhead due to the exhaustive distance searching. To address this challenge, we propose a pattern zooming based near-field wideband beam training with wavenumber-domain codebook. Specifically, we first establish a refined wideband Fourier planewave channel representation, based on which we reveal a pattern zooming effect, where the wavenumber-domain patterns across different subcarriers exhibit frequency-dependent scaling relative to the center frequency. Exploiting this property, we develop a TD-assisted beam sweeping strategy that simultaneously probes multiple wavenumber directions to rapidly acquire the complete wavenumber-domain pattern. Based on the acquired pattern, we further derive exact, approximation-free closed-form expressions that establish a rigorous mapping between the receiver coordinates and the wavenumber-domain pattern, enabling accurate user localization with only a few pilots. Finally, numerical results validate the superiority of our proposed scheme in terms of both beamforming gain and training overhead.
