The growing relevance of multi-agent systems has drawn increasing focus on communication-efficient filters for collaborative perception to alleviate the system's communication burden. While the event-triggered (ET) mechanism can improve communication efficiency in collaborative state estimation, an inevitable trade-off exists between estimation accuracy and communication cost in ET filters. This paper proposes a fast and accurate ET diffusion-based filter for real-time multi-agent collaborative target tracking, aiming to reduce the system's data transmission without compromise in tracking performance. The proposed filter achieves improved tracking accuracy, reduced data transmission, and accelerated convergence using an error-minimized ET cubature information filter (CIF) for local estimation, and a correlation-aware diffusion strategy for global fusion. The experimental results confirm the scalability of the proposed EDC-CIF algorithm and demonstrate its efficacy in simultaneously reducing estimation error and computation time while significantly enhancing communication efficiency.

This paper presents FlowRadar, a novel unsupervised radar detection framework that leverages Flownet2-based optical flow analysis for target detection in sea clutter environments. Unlike traditional radar detection methods that struggle with the non-linearity and non-Gaussian nature of sea clutter, FlowRadar eliminates the need for clutter filtering and labeled datasets by directly processing raw radar echoes through a spatiotemporal optical flow representation. Experimental results on Xband solid-state coherent radar data demonstrate that FlowRadar achieves a 72.8% accuracy improvement over existing Tri-feature-based methods, showcasing its potential for robust and scalable maritime surveillance. This research marks a significant advancement in radar signal processing by bridging computer vision techniques with radar physics, offering a new paradigm for target detection in complex sea environments.

Filtering refers to the methods for inferring time-varying parameters and is a crucial task in cyber-physical systems. An important category of filtering is distributed filtering, where sensor nodes transmit observations via communication links to inference nodes that estimate the unknown states. Distributed filtering is challenging in the sense that the communication constraint of the sensor nodes limits the amount of information available to the inference node, calling for the co-design of communication and computing. This paper establishes a theoretical framework for the co-design of communication and computing in distributed filtering, building on an information-theoretic view of the Kalman–Bucy filtering. In particular, this paper considers a networked system consisting of two nodes, where each node aims to infer its own time-varying state in continuous-time scenarios. The two nodes are connected by a Gaussian feedback channel. Via the feedback link, one of the nodes can obtain the sensor observations and received signals of the other node. This paper develops an optimal linear strategy, namely the information difference encoding strategy, for generating signals transmitted via the Gaussian feedback channel. This paper also presents an inequality that relates Shannon information with Fisher information in distributed filtering. The inference accuracy and power efficiency of the information difference encoding strategy are quantified via simulations.

We propose a physics-informed diffusion framework for radar sea clutter generation with two key innovations: a novel component consistency mechanism that preserves intrinsic statistical relationships between real and imaginary parts of complex signals, and a comprehensive physics-guided diffusion approach that systematically integrates domain-specific priors. Our framework represents complex-valued signals through a two-channel diffusion process with a KL divergence-based constraint ensuring proper distributional alignment between real and imaginary components. To enhance physical fidelity, we develop a multi-prior guidance scheme that enforces essential domain knowledge throughout the diffusion process: Doppler spectral characteristics, power spectral density, local signal smoothness, and reference pattern matching. These physics-based priors continuously guide the generation process to maintain physical correctness while ensuring numerical stability. This systematic incorporation of physical properties and constraints into the diffusion model for sea clutter generation presents a novel contribution to physics-informed signal synthesis, enabling more realistic sea clutter generation for advanced radar applications.

Uncrewed aerial vehicle-mounted base stations (UAV-MBSs) provide flexible wireless connectivity, extending communication coverage in underserved areas. Recently, multi-agent reinforcement learning (MARL) has shown great potential for cooperative UAV swarm control to support efficient communication coverage in dynamic and complex environments. However, existing MARL-based methods often suffer from low sample efficiency due to its trial-and-error training characteristics, limiting its ability to control large UAV swarms with continuous state-action space and partial observation. We notice that UAV swarm systems in communication coverage tasks exhibit a spatial symmetry property, e.g., a rotation in the spatial observation of a UAV results in a same rotation in its optimal action. Exploiting this property, we formulate the task as a symmetric decentralized partially observable Markov decision process and introduce symmetry-informed MARL, featuring a novel network called the symmetry-informed graph neural network (SiGNN) to serve as the policy/value networks. SiGNN leverages the inherent symmetry in multi-UAV systems by embedding the symmetry into the network structure, thereby enhancing the training efficiency to handle large swarms with continuous control. Theoretical analysis shows that the SiGNN strictly preserves symmetry properties, which guarantees the effectiveness of the approach. Experiments in simulation were conducted to handle communication coverage using up to 20 UAVs with continuous control. Experimental results demonstrate that SiGNN-based MARL outperforms advanced baselines, verifying its superior sample efficiency, scalability and robustness.

Terahertz (THz) waves possess great properties such as ultra-large bandwidth, good penetration, strong reflectivity to metallic materials, and low photon energy, making them widely applicable in fields such as optical communication, optical networks, and optical imaging. Therefore, THz optical imaging, as a novel non-destructive testing technology, demonstrates significant application value in areas such as body security scanning. However, existing THz imaging systems require that targets be close to the detector in order to avoid issues like visible stripes, increased noise, reduced contrast, and blurred edges, which can significantly reduce the quality of imaging results. To solve this problem, this study proposes an AM-Res2Net model to achieve accurate THz optical image recognition. The proposed model, compared with the Res2Net model, uses multiple small kernel convolutions to replace a single large kernel convolution. Furthermore, the proposed model also replaces the activation function and introduces the attention mechanism in the residual structure. The AM-Res2Net model tested on the THz optical image set constructed in this study achieved a recognition accuracy of 94.3%, which is higher than the ResNet and Res2Net models.

Integrated sensing and communication (ISAC) is a key technology of next generation wireless communication. Backscatter communication (BackCom) plays an important role for internet of things (IoT). Then the integration of ISAC with BackCom technology enables low-power data transmission while enhancing the system sensing ability, which is expected to provide a potentially revolutionary solution for IoT applications. In this paper, we propose a novel backscatter-ISAC (B-ISAC) system and focus on the joint beamforming design for the system. We formulate the communication and sensing model of the B-ISAC system and derive the metrics of communication and sensing performance respectively, i.e., communication rate and detection probability. We propose a joint beamforming scheme aiming to optimize the communication rate under sensing constraint and power budget. A successive convex approximation (SCA) based algorithm and an iterative algorithm are developed for solving the complicated non-convex optimization problem. Numerical results validate the effectiveness of the proposed scheme and associated algorithms. The proposed B-ISAC system has broad application prospect in IoT scenarios.

Entangled quantum states serve as important resources in quantum communication, quantum computing, and quantum sensing. Creating entangled states between remote nodes is referred to as remote entanglement establishment (REE). REE typically consists of three types of quantum operations: entanglement generation, distillation, and swapping. By carefully designing the sequence describing the order of these operations, this paper investigates REE in a repeater chain under the requirement that the fidelity of the established entanglements be above a desired threshold. Specifically, the paper derives an asymptotically achievable upper bound on the maximum REE rate.

Integrated localization and communication (ILC) will be a key enabler for providing accurate location information and high data rate in next generation networks. This paper proposes a transmission frame structure and a soft information (SI)-based localization algorithm for position-assisted communications. The proposed ILC achieves improved localization accuracy and enhanced communication rate simultaneously by accounting for the statistical characteristics of the wireless environment. Results in 3rd Generation Partnership Project (3GPP) industrial scenarios show that the SI-based localization algorithm can achieve decimeter-level accuracy. Moreover, the position-assisted communication enhances the achievable rate, especially in scenarios with high mobility.

Entanglement is crucial for many applications such as quantum computing, quantum sensing, and quantum communication. Establishment of entanglement between remote nodes, referred to as remote entanglement establishment (REE), is a key element of the quantum internet. This paper develops a theoretical framework for establishing high-fidelity entanglement between two remote nodes of a quantum repeater chain via entanglement generation, distillation, and swapping operations. In particular, an upper bound on the optimal REE rate under minimum fidelity requirements is established, and an REE policy that achieves such a bound asymptotically is presented. Results in this paper provide guidelines for protocol design in the quantum internet.

Multi-agent sensing for transportation systems is receiving widespread attention due to its dynamic flexibility and collaborative capabilities, where the target sensing error is limited by the spatio-temporal error caused by agent localization and formation steps. This paper considers the sensing problem of non-cooperative targets (UAVs or vehicles) by cooperative asynchronous agents (UAVs). This paper develops a framework where the formation of agents and the allocation of resources are jointly optimized. In particular, we reveal the error coupling of measurement and motion noises on target sensing accuracy by Fisher information analysis. Then we propose bandwidth allocation and agent activation strategies in the localization step, which simultaneously improve the position accuracy of agents and the quality of sensing signals. In the formation step, we design motion planning algorithms to increase sensing information about targets. Simulation results demonstrate the significant performance improvements achieved by our proposed algorithms that minimize the effects of localization and control errors on target sensing.

Location awareness is crucial for a variety of emerging applications. The accuracy of localization depends heavily on the spatial topology of the network, especially in complex and infrastructure-limited wireless environments. In these environments, assisting nodes can be deployed to achieve desirable localization performance. This paper presents efficient strategies for deploying assisting nodes to improve the localization accuracy of a target agent. Specifically, it provides a methodology to determine a finite set of candidate positions for the assisting nodes. Based on this methodology, we present a convex relaxation method to select near-optimal positions for the assisting nodes and establish a theoretical limit on the localization accuracy provided by assisting nodes. We also propose an approximate dynamic programming algorithm to deploy assisting nodes with amenable complexity. A case study validates the proposed strategies and shows the benefits of deploying assisting nodes for accurate localization.

Integrated localization and communication (ILC) at millimeter wave (mmWave MMWAVEinit) frequencies will be a key enabler for providing accurate location information and high data rate communication in beyond fifth generation (B5G) networks. This paper proposes a transmission frame structure and a soft information (SI)-based localization algorithm for position-assisted communications. In accordance with B5G specifications, we consider multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) networks. Theoretical limits are also derived to serve both as performance benchmark and as input for algorithm design. The proposed method enables cooperative ILC with improved localization accuracy and enhanced communication rate simultaneously. In particular, position-assisted communication at mmWave MMWAVEinit frequencies is explored accounting for the statistical characteristics of the wireless environment. Localization accuracy and communication rate are quantified in 3rd Generation Partnership Project (3GPP) network scenarios. Results show that the SI-based localization algorithm achieves decimeter-level accuracy, approaching the theoretical limit. Moreover, the position-assisted communication can provide higher communication rate with reduced overhead compared to existing techniques, especially in scenarios with high mobility.

Distributed learning is an important task in emerging applications such as localization and navigation, Internet-of-Things, and autonomous vehicles. This paper establishes a theoretical framework for learning states that evolve in real time over networks. Specifically, each agent node in the network aims to infer a time-varying state in a decentralized manner by using the node’s local observations and the messages received from other nodes within its communication range. As a result, the inference accuracy of a node is significantly affected by the quality of its received messages. This calls for carefully designed strategies for generating messages that are able to provide sufficient information for the receiver and are robust to channel impairments. This paper presents communication-efficient encoding strategies for generating transmitted messages and derives a sufficient condition for the boundedness of the distributed inference error of all the agent nodes over time. The findings of this paper provide guidelines for the design of communication-efficient distributed learning in complex networked systems.

Distributed learning is crucial for many applications such as localization and tracking, autonomy, and crowd sensing. This paper investigates communication-efficient distributed learning of time-varying states over networks. Specifically, the paper considers a network of nodes that infer their current states in a decentralized manner using observations obtained via local sensing and messages obtained via noisy inter-node communications. The paper derives a necessary condition in terms of the sensing and communication capabilities of the network for the boundedness of the learning error over time. The necessary condition is compared with the sufficient condition established in a companion paper and the gap between the two conditions is discussed. The paper provides guidelines for efficient management of the sensing and communication resources for distributed learning in complex networked systems.

Network localization performance depends on the network geometry and, therefore, node deployment methods are critical for high-accuracy localization. Optimal node deployment is challenging in practical problems due to various uncertainties present in the position knowledge of the deployed nodes. In this paper, we propose a node-deployment method for network localization that accounts for such uncertainties. We develop a framework for the optimal deployment of location-aware networks under bounded disturbances in the positions of the sensing nodes. More specifically, by considering bounded discrepancies in the network geometry, we characterize the optimal deployment according to the D-optimality criterion and assert its implications for the A-optimality and E-optimality criteria. Results show that the proposed optimization-based design achieves a significative improvement according to the D-optimality criterion.

Source localization is essential for a wide range of applications. The efficiency of source localization in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). In this paper, we investigate the information inequality of RIS-aided localization systems. We propose a general signal model for RIS-aided localization valid for both near-field and far-field scenarios. Based on the proposed model, we perform Fisher information analyses of localization performance in networks with RISs. Numerical results show that optimal-configured RISs can improve the localization accuracy significantly.

The wideband system plays a important role in high-accuracy location awareness. In beyond 5G networks, reconfigurable intelligent surfaces (RISs) are proposed to control the complex wireless environments. However, existing works related to RISs mainly focus on narrow-band systems due to the frequency-selectivity of conventional metasurfaces. This paper first presents the general signal model of wideband systems with RISs, and then performs Fisher information analysis to determine the theoretical limits of wideband localization with RISs. Furthermore, special scenarios including complete coupling scenarios and complete decoupling scenarios are discussed and evaluated. The simulation result shows that RISs in complete coupling scenarios provided more improvement, in terms of squared position error bound (SPEB), compared with that in complete decoupling scenarios and random configuration.

Achieving accurate location-awareness in wireless networks requires integrated sensing and communication (ISAC), where optimization, signal processing, and data fusion are performed under a common framework. The efficiency of ISAC in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). This paper introduces the concept of continuous intelligent surface (CIS) and establishes the fundamental limits of RIS-aided ISAC systems, specifically, an RIS-aided localization and communication system. In particular, this paper considers two types of RISs, namely CISs and discrete intelligent surfaces (DISs). First, this paper proposes a general signal model for RIS-aided localization and communication valid for both near-field and far-field scenarios, and then theoretical limits on the localization and communication performance are derived. Based on the proposed model, Fisher information analyses of the localization performance in networks with RISs are performed. Numerical results show that RISs with optimized phase responses can improve the received signal-to-noise ratio (SNR) and spectral efficiency of communication, and the localization accuracy significantly.

Location-aware networks enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. Network localization and navigation (NLN) is a recently proposed paradigm for accurate ubiquitous localization without the need for extensive infrastructure. In NLN, devices form an interconnected network for the purpose of cooperatively localizing one another. This paper introduces Peregrine, a system that combines real-time distributed NLN algorithms with ultra-wideband (UWB) sensing and communication. The Peregrine software integrates three NLN algorithms to jointly perform 3-D localization and network operation in a technology agnostic manner, leveraging both spatial and temporal cooperation. Peregrine hardware is composed of compact low-cost devices that comprise a microprocessor and a UWB radio. The contribution of each algorithmic component is characterized through indoor network experimentation. Results show that Peregrine is robust, scalable, and capable of sub-meter accuracy in challenging wireless environments.

Location aware networks will introduce new applications and services for modern convenience, the military, and public safety. In this paper, we introduce a Bayesian method for network localization and navigation in the presence of measurement-origin uncertainty (MOU). In the envisioned cooperative scenario, the agents in a dynamic network aim to better localize themselves by performing pairwise observations with other agents in their environment and sharing their location information. Since pairwise observations suffer from MOU, a data association problem has to be solved before an agent can update its location information. In our approach, joint inference is performed through a factor graph formulation of the entire, network-wide estimation problem. Performing the loopy sum-product algorithm on the derived factor graph results in a distributed and scalable inference algorithm. Simulation results demonstrate that cooperation among agents can significantly improve the localization accuracy even in the presence of MOU.

The Internet of Things (IoT) will seamlessly integrate a large number of densely deployed heterogeneous devices and will enable new location-aware services. However, fine-grained localization of IoT devices is challenging as their computation and communication resources are typically limited and different devices may have different qualities of internal clocks and different mobility patterns. To address these challenges, we propose a cooperative, scalable, and time-recursive algorithm for network localization and synchronization (NLS). Our algorithm is based on time measurements and supports heterogeneous devices with limited computation and communication resources, time-varying clock and location parameters, arbitrary state-evolution models, and time-varying network connectivity. These attributes make the proposed algorithm attractive for IoT-related applications. The algorithm is furthermore able to incorporate measurements from additional sensors for positioning, navigation, and timing such as receivers for global navigation satellite systems. Based on a factor graph representation of the underlying spatiotemporal Bayesian sequential estimation problem, the algorithm uses belief propagation (BP) for an efficient marginalization of the joint posterior distribution. To account for the nonlinear measurement model and nonlinear state-evolution models while keeping the communication and computation requirements low, we develop an efficient second-order implementation of the BP rules by means of the recently introduced sigma point belief propagation technique. Simulation results demonstrate the high synchronization and localization accuracy as well as the low computational complexity of the proposed algorithm. In particular, in sufficiently dense networks, the proposed algorithm outperforms the state-of-the-art BP-based algorithm for NLS in terms of both estimation accuracy and computational complexity.

Location-awareness enables a variety of emerging applications on mobile devices. For indoor applications, a desirable way of obtaining real-time locations is by combining different sources of positional information, such as the inertial measurements, ranging measurements, and map information with an infrastructure-free system that does not rely on any customized hardware. These sources of information can be incorporated into the paradigm of network localization and navigation (NLN). However, there still lacks an infrastructure-free localization system that applies the insights of NLN to effectively fuse different types of information. In this paper, we present the Mercury system, which realizes the key ideas of NLN, including the exploitation of spatiotemporal cooperation and the use of environmental knowledge. We design a real-time belief propagation algorithm to fuse inertial measurements as well as range measurements among different users with map information. We implement this algorithm in the Mercury system formed by a network of smartphones, and evaluate its localization accuracy through experimentation. Results show that Mercury provides reliable location information and that combining spatiotemporal cooperation with environmental knowledge remarkably reduces the location uncertainty of users. Moreover, the performance of Mercury is more robust to imperfect initial positional knowledge compared with that of existing systems.

Wireless network localization (WNL) is a paradigm proposed recently for providing reliable location services. The localization performance is highly dependent on the placement of nodes in the network. In this paper, we study the node placement problem for localization networks. We first propose a mathematical formulation of the node placement problem. Such a formulation takes into account the uncertainty in the position of the placed nodes as well as the constraints on the region where the nodes can be deployed. Based on this formulation, we propose a near-optimal node placement algorithm, and present an upper bound on the gap between its performance with that of the optimal placement strategy. The proposed algorithm is validated by numerical results.