基于GP的贝叶斯辅助两阶段稀疏相位恢复用于仅幅度近场波束训练
GP Bandit-Assisted Two-Stage Sparse Phase Retrieval for Amplitude-Only Near-Field Beam Training
AI总结：
本文提出了一种基于GP的贝叶斯辅助两阶段稀疏相位恢复方法，用于多径环境中仅幅度的近场波束训练，通过自适应支持发现和子空间优化提升波束成形性能。
摘要：
向极大规模天线阵列（ELAA）在6G中的过渡引入了显著的近场效应，需要在多径环境中具有鲁棒性的近场波束训练策略。由于信号相位经常受到硬件缺陷如相位噪声和频率偏移的影响，仅幅度的信道恢复是相干波束训练的关键替代方案。然而，现有的近场幅度基训练方法通常假设简单的视距条件。相反，远场相位恢复（PR）方法缺乏优化训练效率所需的传感灵活性，并且本质上受限于平面波模型，使其不适合近场传播。我们提出了一种用于多径信道中仅幅度近场波束训练的两阶段稀疏PR框架。第一阶段在标准2D DFT波束空间上执行自适应支持发现，利用由近场波束模式诱导的物理引导先验。第二阶段则通过限制传感和稀疏PR到所学子空间来细化信道估计。数值结果表明，所提出的自适应流程在低SNR下始终优于非自适应基线，将波束成形增益提高了超过70%。
英文摘要：
The transition to Extremely Large Antenna Arrays (ELAA) in 6G introduces significant near-field effects, necessitating robust near-field beam training strategies in multi-path environments. Because signal phases are frequently compromised by hardware impairments such as phase noise and frequency offsets, amplitude-only channel recovery is a critical alternative to coherent beam training. However, existing near-field amplitude-based training methods often assume simplistic line-of-sight conditions. Conversely, far-field phase retrieval (PR) methods lack the sensing flexibility required to optimize training efficiency and are fundamentally limited by plane-wave models, making them ill-suited for near-field propagation. We propose a two-stage sparse PR framework for amplitude-only near-field beam training in multipath channels. Stage I performs adaptive support discovery on the standard 2D DFT beamspace by exploiting a physics-guided prior induced by near-field beam patterns. Stage II then refines the channel estimate by restricting sensing and sparse PR to the learned subspace. Numerical results show that the proposed adaptive pipeline consistently outperforms non-adaptive baselines, improving beamforming gain by over 70% at low SNR.
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