AI 中文总结
该研究针对6G近场波束跟踪难题，提出结合MLE与TS的自适应有效载荷辅助框架，通过轨迹建模与自适应机制实现高增益、低方差的稳健跟踪。
AI 中文摘要
6G网络中高频下的极大天线阵列大幅扩展了辐射近场区域，使得移动波束对齐同时依赖用户的角度和距离。新增的距离维度扩大了波束搜索空间，导致在移动场景下重复基于导频的扫描成本高昂，而感知辅助跟踪则取决于传播条件、回波质量和目标反射率。我们提出一种基于最大似然估计（MLE）和汤普森采样（TS）的自适应有效载荷辅助近场波束跟踪框架，将选定的接收有效载荷样本反馈并复用为跟踪观测，避免跟踪过程中使用专用波束扫描符号。在每个滑动窗口内，用低阶多项式建模局部角度和距离轨迹以捕捉速度、加速度及高阶运动变化，并利用球面波信道模型通过MLE进行估计；以MLE为中心的局部高斯近似（协方差来自观测费希尔信息的逆）表示轨迹不确定性。对于选定反馈的有效载荷传输，TS采样轨迹假设并将其映射为指向采样状态的有效载荷波束，其余传输则使用MLE预测的波束。为处理非平稳移动性，窗口内估计风险的渐近卡方特性推动观测窗口长度和多项式阶数的联合自适应，同时在线残差统计调整更新间隔和反馈比例。该框架还扩展到带有仰角跟踪的均匀平面阵列。在平滑和急转弯轨迹下的仿真显示，该框架具有高有效载荷平均归一化波束成形增益、低归一化增益方差和高有效符号可靠性，且自适应机制在非平稳急转弯移动性下提供了显著的鲁棒性。
英文摘要
Extremely large antenna arrays at high frequencies substantially extend the radiative near-field region in 6G networks, making mobile beam alignment depend jointly on user angle and range. The added range dimension enlarges the beam-search space, making repeated pilot-based sweeping costly under mobility, while sensing-assisted tracking depends on propagation conditions, echo quality, and target reflectivity. We propose an adaptive payload-aided near-field beam-tracking framework based on maximum likelihood estimation (MLE) and Thompson sampling (TS). Selected received payload samples are fed back and reused as tracking observations, avoiding dedicated beam-sweeping symbols during tracking. Within each sliding window, local angle and range trajectories are modeled by low-order polynomials to capture velocity, acceleration, and higher-order motion variations, and are estimated by MLE using the spherical-wave channel model. A local Gaussian approximation centered at the MLE, with covariance from the inverse observed Fisher information, represents trajectory uncertainty. For payload transmissions selected for feedback, TS samples a trajectory hypothesis and maps it to a payload beam toward the sampled state, while the remaining transmissions use the MLE-predicted beam. To handle nonstationary mobility, an asymptotic chi-square characterization of in-window estimation risk motivates joint adaptation of observation-window length and polynomial degree, while online residual statistics adjust the update interval and feedback ratio. The framework is also extended to uniform planar arrays with elevation tracking. Simulations under smooth and sharp-turn trajectories show high payload-accounted mean normalized beamforming gain, low normalized-gain variance, and high effective-symbol reliability, while the adaptive mechanism provides substantial robustness under nonstationary sharp-turn mobility.
CommentsAbstract shortened in the arXiv metadata due to the 1,920-character limit; the full abstract appears in the manuscript
