可重构智能表面辅助车辆编队的位置跟踪：多样稀疏性启发的方法
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches
AI总结：
针对车辆编队高精度位置跟踪需求，提出基于多样稀疏性的DiLuS框架与DiLuS-STPL算法，结合变分贝叶斯推理与消息传递技术，实现单基站加单RIS的高精度定位，性能媲美双基站方案且优于多种基准。
摘要：
本文研究了可重构智能表面（reconfigurable intelligent surfaces, RISs）在车辆编队中的应用，其与基站（base station, BS）协同工作以支持高精度位置跟踪。具体而言，RIS的使用引入了额外的结构化稀疏性，当这种稀疏性与基站初始的稀疏视距（line-of-sight, LoS）信道相结合时，可形成有益的组稀疏性。由此产生的组稀疏性显著丰富了原有纯直接信道的能量，使得来自同一车辆位置索引的视距信道能量更加集中。此外，通过将非视距（non-line-of-sight, NLoS）信道表示为其稀疏副本，揭示了突发稀疏性。这便构成了本文所关注的多样稀疏性的核心原理。随后，本文定制了一个多样动态分层结构化稀疏性（diverse dynamic layered structured sparsity, DiLuS）框架，用于捕捉这两类稀疏性的不同先验信息，并在此基础上将位置跟踪问题建模为位置的最大后验（maximum a posterior, MAP）估计问题。然而，由于感知矩阵病态、与基站和RIS相关的潜在变量复杂耦合，以及车辆编队之间的时空相关性，该跟踪问题极难求解。为克服这些障碍，本文提出了一种高效算法，即支持DiLuS的时空编队定位（DiLuS enabled spatial-temporal platoon localization, DiLuS-STPL）算法，该算法结合变分贝叶斯推理（variational Bayesian inference, VBI）和消息传递技术，以类turbo的方式递归实现参数更新。最后，本文通过大量仿真结果表明，仅依靠一个基站和一个RIS的定位可达到与两个独立基站相当的精度性能，同时验证了所提算法相较于多种基准方案的鲁棒性与优越性。
英文摘要：
In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatialtemporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.
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