Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches
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DOI：
10.1109/jsac.2023.3288262
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发表时间：
2023-08-01
影响因子：
16.4
通讯作者：
Zhang, Ping
中科院分区：
文献类型：
--
作者：
Chen, Yuanbin;Wang, Ying;Zhang, Ping
在本文中，我们研究了可重构智能表面（RIS）在车辆排中的应用，与基站（BS）协同工作以支持高精度位置跟踪。特别是，RIS 的使用会带来额外的结构化稀疏性，当与 BS 的初始稀疏视距 (LoS) 通道配合使用时，可促进有益的组稀疏性。由此产生的群体稀疏性显着丰富了原始直接通道的能量，使得来自相同车辆位置索引的视距通道能量更加集中。此外，通过将非视距（NLoS）通道表示为其稀疏副本，可以暴露突发稀疏性。这就构成了利益多元化的哲学。然后，定制多样化的动态分层结构化稀疏性（DiLuS）框架，用于捕获这对稀疏性的不同先验，基于此，位置跟踪问题被公式化为位置的最大后验（MAP）估计。然而，由于病态的传感矩阵、与 BS 和 RIS 相关的复杂耦合的潜在变量以及车辆排之间的时空相关性，跟踪问题非常棘手。为了克服这些障碍，我们提出了一种有效的算法，即 DiLuS 启用的时空排定位（DiLuS-STPL），它结合了变分贝叶斯推理（VBI）和消息传递技术，以类似涡轮的方式递归地实现参数更新。最后，我们通过大量的模拟结果证明，仅依赖 BS 和 RIS 的定位可以实现与两个单独的 BS 获得的可比较的精度性能，以及与各种基准方案相比，我们提出的算法的鲁棒性和优越性。
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 spatial-temporal 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.