In the future generation of mobile communication systems, many scenarios will require high-resolution range-based positioning. However, the accuracy of time-of-arrival (TOA) estimation algorithms for single wideband systems is generally limited due to the constraints of available bandwidth. Currently, leveraging multiple available frequency bands and carrier frequency switching to obtain multi-band channel state information (CSI) for TOA estimation is gaining popularity, as it effectively constructs an equivalent wideband signal. In this paper, we proposed a subspace-based TOA estimation algorithm using multi-band CSI by constructing a correlation matrix and then exploiting its mathematical properties. The proposed algorithm eliminates the need for grid search and thus has lower computational complexity. We analyze the Cramér-Rao Bound (CRB) for the multi-band data model and derive a tighter lower bound for our algorithm. Simulation results show that our algorithm converges to the CRB at high SNR and closely follows the proposed lower bound across all SNR levels. Additionally, our algorithm demonstrates superior performance compared to single-band algorithms and offers advantages in either accuracy or complexity when compared to other multi-band algorithms.

In this letter, we introduce a novel carrier-phase-based frequency syntonization method for multi-band systems. Different from other methods, by leveraging the wide bandwidth characteristics of multi-band signals, our approach shows superior clock-skew estimation performance compared to single-band signals. We proposed a bidirectional communication protocol to collect multi-band phase difference of arrival (PDOA) measurements. Then, we derive an approximate maximum-likelihood estimator for clock-skew estimation. Finally, link-level simulations demonstrate that the proposed method's estimation results closely approach the Cramér-Rao bound (CRB) for high signal-to-noise ratio (SNR), validating the effectiveness of our estimator. The comparison with other methods further highlights the superiority of our algorithm.

By leveraging geometric and probabilistic information contained in multipath components (MPCs), multipath-based simultaneous localization and mapping (SLAM) enables the localization of both mobile agents and a varying number of map features (MFs). Traditional solutions assume that each MPC is associated with a single MF, while focusing only on MFs’ positions. However, advancements in communication technologies provide higher-resolution multipath parameters (MPPs), resulting in large MFs generating multiple MPCs. This challenges the existing association assumptions and provides opportunities to estimate the extents and shapes of MFs. In this paper, we first integrate the many-for-one association relationship and random matrix-based extent modeling into the existing Bayesian SLAM framework. We then categorize MFs by shape, developing multiple shape and measurement models for each category. By exploring these models, we derive the joint posterior distribution and represent it using a factor graph, which serves as the foundation for our proposed message passing algorithm. Numerical results demonstrate that the proposed algorithm achieves superior localization and mapping performance, successfully classifying different types of MFs while estimating their orientations and sizes.

Loop closure detection is an important aspect of simultaneous localization and mapping (SLAM) to correct long-term drift by finding overlapping trajectories. However, in multipath SLAM using radio signals, due to the limitation of low-resolution sensors and few features, most of the existing works have not fully explored loop closure. In this paper, we propose a loop closure detection algorithm for multipath SLAM, Wi-Loop SLAM, that can reduce cumulative errors. To recognize previously visited places, we use the multipath parameters extracted from received signals as matching features without introducing new information. We use a delayed selection strategy to ensure that the most appropriate pairing is selected, and a confirmation of location drift is required to minimize unnecessary computation. Finally, we derive a Bayesian model to perform loop optimization with the particle-based sum-product algorithm (SPA). Simulation results show that the proposed Wi-Loop SLAM can correct the state estimate drift in the environment where propagation paths are often obscured.

Abstract In this work, the signal recovery problem regarding incomplete and noisy spatio‐temporal signals is studied. A spatio‐temporal signal is considered as a time‐varying graph signal and a diffusion‐induced first‐order Markov signal model is developed to incorporate both the spatial structure and temporal correlation into the underlying graph. With this model, prior knowledge on spatial smoothness and temporal correlation is revisited, and the connections between the graph structure and differential temporal smoothness are revealed. The authors then accordingly formulate a spatio‐temporal signal recovery method by jointly exploiting the spatial smoothness, low rank and refined differential temporal smoothness. The formulated recovery problem is solved by a block coordinate descent‐based algorithm, which iteratively optimises the recovery accuracy and temporal correlation matrix. The experiments on three real‐world datasets reveal the high signal recovery accuracy of the proposed algorithm.

In this paper the topology learning for time-varying graph signals with incremental data is studied. In order to learn the topology which is slowly time-varying during data collection, we separate the data of observation into multiple groups by time, and for each group we model the topology learning as a sparse optimization problem, in which the penalty function is designed to consider both the incremental data and previous topology information for graph learning. Moreover, a correction function for dynamic topology is developed by considering a priori information of topology changes. Based on that, by solving the optimization problem we then propose a dynamic topology learning and tracking algorithm to learn as well as track the varying graph topology. Simulations on synthetic and real-world dataset are performed to reveal the performance gain of the proposed algorithm.

In this paper the massive access problem in IoT networks is studied from the perspective of graph signal processing (GSP). First, we reveal the connections of massive access in IoT networks and the sampling of a graph signal, and model the massive access problem as a graph-based random sampling problem. Second, inspired by the restricted isometry property (RIP) condition in compressed sensing, we derive the RIP condition for random sampling on band-limited graph signals, showing at the first time that band-limited graph signals can be recovered from randomly-selected noisy samples in a given probability. Based on the proposed RIP condition, the sampling probability of each sensing device is optimized through minimizing the Chebyshev or Gaussian approximations of mean square error between the original and the recovered signals. Experiments on the Bunny and Community graphs verify the stability of random sampling, and show the performance gain of the proposed random sampling solutions.