发表机构
Hunan University（湖南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对周期扫描导致宽带信道观测不全的问题，提出CP-PBCD在线跟踪器，通过增量更新和低秩表示，在亚毫秒内重建完整信道，并在多种条件下优于所有基线。
AI 中文摘要
周期性的资源块（RB）扫描使得当前大部分宽带信道未被观测，并且混合了不同时间的测量数据。我们开发了一种带有近端块更新的在线典型多面体跟踪器（CP-PBCD），在每次窄带RB采集后重建完整信道。年龄加权有限历史、频率和时间正则化以及有界分量管理维持了自适应的低秩表示。两次热启动共轭梯度迭代和并行频移求解提供了固定的每帧更新预算。训练用户空间投影在低导频信噪比下增强了噪声抑制。实验覆盖了十个测试用户、三种速度、四种信噪比，以及完整的1000帧轨迹，RB采集间隔为1毫秒。直接首帧CP-PBCD在所有十二种条件下均实现了比全部六个基线更低的平均归一化均方误差（NMSE）。在3.6公里/小时和20分贝条件下，它比周期性物理重拟合改善了3.91分贝，运行速度快35.1倍，平均每次在线更新耗时0.717毫秒。可选的分散启动导频改善了早期采集，而受控初始化研究在导频预算范围内产生了最后100帧NMSE范围为0.427分贝的结果。这些结果表明，通过增量更新和亚毫秒级平均计算，能够实现准确的当前信道重建。
英文摘要
Periodic resource-block (RB) scanning leaves most of the current wideband channel unobserved and mixes measurements of different ages. We develop an online canonical polyadic tracker with proximal block updates (CP-PBCD) that reconstructs the full channel after each narrow-RB acquisition. Age-weighted finite histories, frequency and temporal regularization, and bounded component management maintain an adaptive low-rank representation. Two warm-started conjugate-gradient iterations and parallel shifted frequency solves provide a fixed per-frame update budget. Training-user spatial projection strengthens noise suppression at low pilot SNR. Experiments cover ten test users, three speeds, four SNRs, and complete 1000-frame trajectories with 1-ms RB acquisition. Direct first-RB CP-PBCD achieves lower mean normalized mean squared error (NMSE) than all six baselines in all twelve conditions. At 3.6 km/h and 20 dB, it improves on periodic physical refitting by 3.91 dB and runs 35.1 times faster, averaging 0.717 ms per online update. Optional dispersed startup pilots improve early acquisition, while a controlled initialization study yields a last-100-frame NMSE range of 0.427 dB across pilot budgets. These results demonstrate accurate current-channel reconstruction through incremental updates with submillisecond average computation.
Comments13 pages, 7 figures
