不完善定位下的稳健近场波束聚焦
Robust Near-Field Beam Focusing Under Imperfect Localization
AI总结：
针对6G近场波束聚焦受定位误差影响的问题，提出基于一阶泰勒近似不确定性模型的稳健设计，经半定松弛优化后可显著提升高功率、大误差场景下的最坏用户速率。
AI中文摘要：
向具备大规模天线阵列和高频部署的6G及后续无线系统的过渡，显著扩展了近场区域，该区域的信道对用户位置表现出强依赖性。虽然这使基于位置的波束聚焦成为传统信道估计的低开销替代方案，但其性能对定位误差高度敏感。本文研究不完善用户定位下的稳健近场波束聚焦，通过一阶泰勒近似推导可处理的不确定性模型，明确表征定位误差对以视距为主的信道的影响，该模型捕捉近场传播中距离与角度的耦合效应。基于此模型，构建最大化最小信干噪比优化问题，以保证有界定位误差导致的最坏信道实现下的性能。该问题通过半定松弛重构为可行性问题。数值结果表明，所提稳健设计与非稳健波束聚焦相比，显著提升了最坏用户速率，尤其在高总发射功率水平和大定位误差范围下表现突出。
英文摘要：
The transition to 6G-and-beyond wireless systems with large-scale antenna arrays and high-frequency deployments significantly extends the near-field region, where channels exhibit a strong dependence on user location. While this enables location-based beam focusing as a low-overhead alternative to conventional channel estimation, its performance is highly sensitive to localization errors. In this paper, we study robust near-field beam focusing under imperfect user localization. We explicitly characterize the impact of localization errors on the line-of-sight-dominated channel by deriving a tractable uncertainty model via a first-order Taylor approximation, which captures the coupled effects of distance and angle in near-field propagation. Building on this model, we formulate a max-min signal-to-interference-plus-noise ratio optimization problem that guarantees performance under worst-case channel realizations induced by bounded localization errors. The resulting problem is reformulated into a feasibility problem using semidefinite relaxation. Numerical results demonstrate that the proposed robust design significantly improves the worst-user rate compared to non-robust beam focusing, particularly under high total transmit power levels and large localization error ranges.
