This paper proposes a novel Bayesian integrated tracking and communication (ITAC) framework. By consolidating the time of arrival (ToA) and angle of departure (AoD) measurement models, a robust Bayesian filtering framework is introduced for single-target tracking in dynamic and cluttered wireless environments. By incorporating random finite set(RFS) theory, the method jointly estimates the target’s existence probability and its kinematic state within a unified Bayesian recursion, effectively overcoming the limitations of conventional filters, which assume that the target can always be detected. Furthermore, the paper derives the signal-to-interference-plus noise ratio (SINR) for both communication and sensing links. The proposed RFS-based Bayesian filter is implemented using a sequential Monte Carlo (SMC) approach. Numerical simulations demonstrate the framework’s superior performance in maintaining tracking consistency and accuracy even under challenging propagation conditions. Moreover, the results reveal how different network parameters affect the overall communication and tracking performance.

This paper introduces delay-alignment modulation (DAM) for secure integrated sensing and communication (ISAC). Due to the broadcast nature of multi-user downlinks, communications are vulnerable to eavesdropping. DAM applies controlled per-path symbol delays at the transmitter to coherently align the multipath components at the intended user, enhancing the received signal power, while simultaneously creating delay misalignment at the eavesdropper (Eve). To mitigate sensing degradation caused by multipath propagation, we propose a two-stage protocol that first estimates the angle and then the delay of the line-of-sight (LoS) path after suppressing multipath interference. We derive the secrecy spectral efficiency (SSE) and the Cramer–Rao bound (CRB) of the target delay. Finally, we develop a path-based zero-forcing (ZF) precoding framework and formulate a max–min SSE design under CRB and power constraints. Simulation results show DAM significantly outperforms the strongest-path (SP) benchmark in terms of SSE, while meeting sensing requirements, since intentional delay alignment at legitimate users degrades Eve’s reception.

We propose a novel pinching-antenna-assisted integrated sensing and communication (PAA-ISAC) framework, where the dielectric waveguide enables the downlink transmission and the reflected signal is collected at the base station (BS) for radio sensing. Due to the asymmetrical propagation between the waveguide → sensing target (ST) and the ST → BS, the reflection law is exploited to model the radio sensing propagation. The sensing degree of freedom (SDoF) and Cramér–Rao Bound (CRB) of the estimated angle are first analyzed, where the qualitative relationship between the CRB and the antenna design is revealed. Capitalizing on this finding, a weighted CRB and transmit power minimization problem is formulated subject to dual-functional requirements. A low-complexity alternating optimization algorithm is proposed to jointly optimize the location of the activated pinching antenna (PA) and the transmit power towards the waveguide by invoking the Lagrange duality theory. Our simulation results showcase that: 1) the derived performance approximation is close to the exact CRB; 2) the SDoF is independent of the waveguide deployment; 3) the proposed algorithm outperforms the fixed antenna design in the context of radio sensing performance and the transmit power consumption.

In sixth-generation and beyond, space-air-ground integrated networks (SAGINs) extend network connectivity to space, thereby enabling broader service coverage. This paper proposes a topology-aware SAGIN framework to address the integrated sensing, communication, and wireless power transfer (ISCPT) problem, leveraging the distinctive visibility of satellite-terrestrial and satellite-satellite users as well as their constructing in-between channel strengths. By modeling the topology of the SAGIN as a bipartite graph, we formulate the ISCPT problem as a multi-objective joint optimization problem with specified topological structures to reflect connection relationships of satellite-terrestrial and satellite-satellite users. The ISCPT problem is then reformulated and carefully decomposed as several mixed-integer linear programs (MILPs) by leveraging the network topology to individually optimize sensing, communication, and power transfer. To reduce the computational complexity of the proposed method, a greedy algorithm deal with generalized multi-assignment problem (GMAP) is developed. Simulation results demonstrate superior performance in communication and sensing, with a tolerable trade-off in wireless power transfer.

Terahertz (THz) frequency band has been widely studied and is recognized as a promising candidate for centimeter-level localization. However, the limited coverage of THz networks may result in localization failures, while a heterogeneous deployment of millimeter-wave (mmWave) and THz radio units (RUs) offers a viable solution to mitigate this issue. This paper presents a theoretical framework for evaluating the performance limits of localization systems in mmWave and THz heterogeneous networks. In this architecture, the mmWave RUs serve as macro base stations (BSs), while the THz RUs function as micro BSs distributed around each mmWave RU. By leveraging the standard tools of stochastic geometry to model the spatial distributions of the RUs and ambient obstacles, the localizability of a target is computed to evaluate the probability of achieving sufficient signal-to-interference-plus-noise ratio for localization in both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. Furthermore, the Cram é r-Rao lower bounds in both LoS and NLoS scenarios are analytically derived to characterize the overall positioning performance. Numerical results demonstrate that the hybrid deployment strategy significantly improves both the network coverage and localization accuracy compared to mmWave-only and THz-only networks.

wireless communication environment may not experience direct line-of-sight propagation whereas the number of receivers (Rxs) is often limited. We propose the utilization of only non-line-of-sight (NLoS) received signal strength (RSS) measurements observed at a single Rx to locate a target, via a positioning algorithm accounting for data association ambiguity that may occur in a real-world scenario. Considering the stochastic nature of a network geometry, tractable expressions are derived for the probability of acquiring at leastLNLoS RSS measurements during localization. In light of the computational complexity of our solution, we investigate the minimum number of RSS samples required to meet the specified localization accuracy, thereby guiding system design. Furthermore, the probability distribution of the trace of the Cramér-Rao lower bound is obtained analytically, which offers a comprehensive understanding of the fundamental limits of the single-Rx localization scheme without resorting to intensive simulations.

Received signal strength (RSS) has been extensively studied for localization purposes, and the distributed nature of cell-free massive multiple-input multiple-output (CF-mMIMO) systems offers a new synergistic avenue for achieving high-precision localization. In this work, Open RAN and software-defined radio are used to realize the central and distributed units of a CF-mMIMO system to acquire the RSS measurements. By analyzing the experimental data, it is revealed that the RSS measured from the first-order reflection path can yield a sufficiently high signal-to-noise ratio for localization, enabling localization even without line-of-sight (LoS) paths. Inspired by this finding, a hybrid localization scheme, that can attain the best accuracy benchmarked by Cramér-Rao lower bound, is proposed to estimate the target position using both LoS and first-order non-line-of-sight RSS measurements. Furthermore, a theoretical framework is established to assess the fundamental limits of RSS-based localization in CF-mMIMO systems, offering a principled guideline for system designers to deploy and design localization systems in real-world scenarios.

Sensing, as an underlying function of integrated sensing and communication (ISAC), can, in theory, enable numerous applications, including detection, localization, navigation, etc. However, in sixth generation (6G) and beyond, sensing data could be used in a more effective manner, while environmental mapping is a promising candidate to enhance the sensing capacity. This paper augments the conventional ISAC framework by introducing the concept of integrated localization, mapping, and communication (LMAC), exploring the feasibility of providing mapping services while maintaining localization accuracy. Closedform expressions for the communication and localization signal-to-interference-plus-noise ratios (SINRs) are analytically derived to evaluate both the communication performance and the localizability of the localization user and scatterers. Furthermore, the Cramér-Rao lower bounds (CRLBs) for localization and mapping services are provided to characterize the fundamental limits of an LMAC system. Numerical results indicate that the proposed performance bounds effectively characterize the system performance and offer valuable insights into how different network configurations influence the performance and realizable potential of LMAC.

Compared to range- and angle-based localization, the importance of received signal strength (RSS)-based positioning is gradually diminishing in beyond 5G networks due to its limited localization accuracy, despite its simplicity. This paper explores the potential of utilizing non-line-of-sight (NLoS) RSS measurements to enhance the localization performance of RSS-based systems. Different from the conventional RSS-based localization, which relies solely on line-of-sight (LoS) RSS measurements, our findings reveal that the NLoS RSS also contains valuable locationrelated information for localization. A simple and efficient localization scheme that attains the Cramér-Rao lower bound (CRLB) performance is developed, accounting for measurement misalignment caused by different reflection paths. Furthermore, a tractable expression of the CRLB is analytically derived, which offers insights into how different network parameters, such as the path-loss decay and reflection loss, affect the fundamental limits of the proposed scheme.

This paper investigates the problem of localizing and associating multiple targets in an integrated sensing and communication (ISAC) system that employs a multi-static cell-free massive multiple-input multiple-output ($\mathbf{C F}-\mathbf{m M I M O}$) architecture. In this system, a large area is covered by a number of distributed access points (APs). The problem of simultaneously detecting and locating multiple targets is considered to be crucial and challenging, particularly in order to avoid interference in the communications functionalities. By using the virtual channel representation of both the sensing and communication channels and the angular estimation method, e.g., estimation of signal parameters via rotational invariance techniques (ESPRIT), we can first accurately identify specific angular directions from unidentified targets to each receiving (Rx)-AP. Then, we transform the association problem into a clustering problem. We propose a low-complexity approach based on the clustering algorithm to solve this association problem. The proposed method yields robust communication performance while simultaneously achieving outstanding association and localization performance.

Accurate prediction of mmWave time-varying channels is essential for mitigating the issue ofchannel agingin highly dynamic scenarios. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert knowledge, while emerging data-driven methods typically require substantial labeled data for effective training and often lack interpretability. To address these issues, this paper proposes a novel hybrid method that integrates a data-driven neural network into a conventional model-based workflow based on a state-space model (SSM), implicitly tracking complex channel dynamics from data without requiring precise expert knowledge. Additionally, a novel unsupervised learning strategy is developed to train the embedded neural network solely with unlabeled data. Theoretical analyses and ablation studies are conducted to interpret the enhanced benefits gained from the hybrid integration. Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven. Furthermore, extensive experiments validate its robustness against various challenging factors, including among others severe channel variations.

Subspace location estimators are a class of range-based source localization (SL) methods built upon the multidimensional similarity (MDS) theory. Since they are computationally lightweight while maintaining a reasonably good level of positioning accuracy, these techniques can be well-suited for the context of Internet of Things (IoT), where precise localization is necessary but the on-device computational resources turn out to be relatively limited. MDS analysis (MDSA), in signal processing terms, is the statistical process of disentangling the signal subspace components from their disturbance counterparts for an observed MDS matrix that measures the similarity among multiple source-sensor coordinate differences. A prominent drawback of traditional MDSA schemes devised under the assumption of Gaussian noise is their vulnerability to outliers in the available range-type data, which are frequently encountered in IoT SL applications due to adverse environmental factors like non line-of-sight signal propagation and interference. In this contribution, we use symmetric$\alpha $-stable$(S \alpha S)$distributions to systematically characterize the MDS matrix observation errors, thus accounting for the existence of outliers. To resist against$S \alpha S$disturbances, we cast MDSA as an$\ell _{p}$-norm-based robust low-rank approximation problem. We then develop a practical optimization solution by means of the alternating direction method of multipliers, for which we further conduct a theoretical analysis of convergence. Simulations and real-world experiments confirm the feasibility of our robust subspace positioning approach.

Passive multistatic localization (PML) refers to locating a signal-reflecting/relaying target using the bistatic range and direct range measurements acquired by employing multiple spatially-separated transmitters and receivers, where the transmitter positions are unknown. In real-world applications, one of the major technical challenges faced in PML is the non-lineof-sight (NLOS) propagation of signals, and recent studies have turned to the concept of robust statistics to tackle such an issue. Continuing to delve into this research direction, here we address the discrepancy arising from the fact that the conventional robust statistical PML schemes inherently assume zero-centered error samples, which may not hold true when the positive NLOS biases are present. In contrast to the existing PML solutions, our proposal is based on the maximum correntropy criterion with variable center (MCC-VC), thereby taking into consideration the potential non-zero-centrality of error samples in the estimator derivation. Subsequently, we develop an alternating minimization algorithm to handle the nonconvex MCC-VC optimization problem in a way that can strike a fine balance between accuracy and computational efficiency. The superiority of our PML approach over its competitors is demonstrated via computer simulations

Positioning based on received signal strength (RSS) is regarded as a promising candidate for localization purposes in wireless networks due to its feasibility and deployability. In general, multilateration and fingerprinting algorithms are the primary localization methods in RSS-based localization systems, which are assessed by the Cramér–Rao lower bound (CRLB), given fixed node locations, including the target and participating anchors. However, this methodology produces only definite values for the CRLB specific to the scenario of interest while does not provide insights into the fundamental limits of localization performance. Thus, we are motivated to analyze the RSS-based localization performance using stochastic geometry to allow for randomly distributed nodes and investigate how the nodes’ locations influence this performance. To characterize the localization performance of the multilateration method, a tractable expression of localizability is provided to indicate the probability that a target is localizable. Then, conditioned on the number of participating anchors$L$, we provide an accurate approximation of the CRLB using the$\lceil L/4 \rceil $th value of ordered distances to quantify the localization accuracy on a random network setting and examine how its performance is influenced under different propagation channels by utilizing$\kappa $–$\mu $shadowed fading. Next, the fingerprinting localization problem is regarded as a hypothesis testing problem, and thus, its performance can be evaluated based on the similarity analysis of the observed RSS fingerprints. A comprehensive analysis of these two methods is performed, and the derived calculations are compared with the experimental results to demonstrate that our unified framework can precisely reflect localization performance in real-world scenarios. Based on the analysis, we can develop an insight to optimally design an RSS-based localization system that achieves the specified localization requirements.