Joint radar and communication (JRC)-enabled multiple unmanned aerial vehicles (UAVs) are gaining attention due to their potential to improve spectrum utilization and provide enhanced flexibility in target detection and tracking. However, most existing studies focus solely on a single UAV or static environment, neglecting the benefits of UAV cooperation and the complexity of the dynamic environment. In this paper, we consider a JRCenabled multi-UAV cooperative dynamic target detection and tracking scenario, in which each UAV simultaneously performs both detection and communication. Given the constraints of JRC power and spectrum, a trade-off between detection and communication performance is required such that sensing power, communication channel allocation, and UAV trajectories should be jointly optimized. Considering the challenge of integer and non-convex constraints, we propose a reinforcement learningbased cooperative target detection and tracking (RLCTDT) algorithm in which the optimal strategy of each UAV is iteratively derived through the interaction between UAV swarm and environment. In particular, the reward function, mainly consisting of sensing rewards and guidance rewards, is newly designed to guide UAVs in exploring and exploiting environment information efficiently. Recurrent neural networks (RNNs) are introduced into RLCTDT to abstract history observations for improving estimation accuracy of state value and utilize history actions for better decision-making. Numerical experiments indicate that our approach achieves a 16% higher stabilized system reward than the best baseline, RLCTDT-KL, which reflects improvements in target tracking and environment sensing

Real-time digital twin of network functionalities and behaviors at different levels, namely network digital twin (NDT), is essential for efficient next-generation (xG) network and service management. To accurately represent and emulate the network behavior, NDT requires real-time situation awareness. This letter introduces a microservice-based architecture (MSA) for exposing situation awareness in NDT. Multimodal localization and sensing provide situational information of connected devices and unconnected targets employing heterogeneous technologies, namely xG, ultra-wideband, and radar. We propose a data model to describe generic assets incorporating situational information for its integration within NDT. A case study shows the functionalities of the proposed architecture in an industrial network considering a use case of mobile robots.

Integrated sensing and communication (ISAC) is a headline feature for the forthcoming IMT-2030 and 6G releases, yet a concrete solution that fits within the established orthogonal frequency division multiplexing (OFDM) family remains an open problem. Specifically, Doppler-induced inter-carrier interference (ICI) destroys subcarrier orthogonality of OFDM sensing signals, blurring range-velocity maps and severely degrading sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) OFDM systems, this paper develops a model-driven ISAC framework that jointly optimizes transmit and receive beamforming to maximize multi-user communication sum-rate under sensing-performance constraints, while mitigating Doppler ICI. To this end, we propose a Doppler-correction filter network (DCFNet), an AI-native ISAC model that achieves fine-grained range-velocity estimation precision with minimal complexity and without altering the legacy frame structure. A bank of DCFs derived from the Doppler physics first shifts and suppresses dominant ICI components, and a subsequent deep neural network cancels the residual interference to yield a clean radar sensing image. To further enhance the range and velocity estimation precision, we propose DCFNet with local refinement (DCFNet-LR), which applies a generalized likelihood ratio test (GLRT) to refine target estimates of DCFNet to sub-cell precision. Simulation results show that DCFNet-LR runs 143 times faster than a maximum-likelihood search and achieves significantly superior performance, reducing the range and velocity RMSE by factors of 2.7 x 10(-4) and 6.7 x 10(-4) compared to conventional detection methods.

Counterfactual quantum communication (CQC) is an intriguing paradigm originating from quantum mechanics, enabling spatially separated parties to achieve communication tasks without the need to transmit any physical particles across the channel. Conventional quantum communication typically relies on particle transmission or utilizes entanglement-assisted protocols with local operations and classical communication, such as quantum teleportation and superdense coding, to transfer information. As the research area of quantum communication is being rapidly developed, significant progress has been made in the development of CQC. In this paper, we present a comprehensive tutorial on CQC for transmitting both classical and quantum information, noting that no physical particles are found in the channel during successful information transmission. We begin by studying the origin of CQC, followed by a detailed examination of counterfactual protocols for classical and quantum information transmission. This paper highlights the applications of CQC and outlines future research directions.

With the advent of cooperative intelligent transport systems (C-ITS) and vehicle-to-everything (V2X) communications, cooperative positioning based on V2X sharing of location information has been emerging as a promising augmentation system for conventional satellite navigation. An example is implicit cooperative positioning (ICP) which relies on Bayesian filtering for cooperative sensing of targets that are used as reference points for improving vehicle positioning. ICP methods, however, rely on pre-determined models which makes them sub-optimal in case of non-Gaussian non-linear models or complex cooperation graphs. To address these limitations, the paper proposes a decentralized-partially observable Markov decision process (Dec-POMDP) framework, paired with deep multi-agent reinforcement learning (MARL) algorithms. We introduce a novel ICP-multi-agent proximal policy optimization (MAPPO) algorithm where distributed agents (i.e., vehicles) dynamically activate/deactivate the radio links for cooperation with the neighbors to optimize the communication efficiency, still guaranteeing accurate positioning. We reproduce a realistic C-ITS scenario with CARLA simulator, where vehicles move according to real-world dynamics and communicate with each other to cooperatively sense their locations. Results show that the proposed ICP-MAPPO algorithm, with its dynamic-decentralized-execution and centralized-training schemes, outperforms state-of-the-art ICP methods by 21% in terms of positioning accuracy, and it can reduce the communication overhead by following the optimal learned policy.

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.

Given an unknown quantum state described by one of two possible density operators, the Helstrom bound provides the minimum discrimination error probability (DEP) by optimizing over all possible quantum measurements. However, it is unrealistic to implement arbitrary measurements in practice due to physical limitations of measurement apparatuses. This paper considers a quantum state discrimination scenario where a fixed measurement apparatus is available. In this setting, we advocate the use of quantum pre-processing (QPP) to realize effectively different measurements from that of the fixed apparatus. Applying optimal QPP prior to measurement with the fixed apparatus allows one to minimize the DEP. This paper derives the minimum DEP, determines the QPP required to achieve it, and provides necessary and sufficient conditions for this minimum DEP with optimal QPP to coincide with the Helstrom bound.

As a means to provide ubiquitous connectivity across the ground-air-space 3D network, low Earth orbit (LEO) satellite mega-constellation systems comprising thousands of LEO satellites have attracted significant interest from both academia and industry recently. One major issue of LEO mega-constellation systems is the frequent handovers between satellites and beams, causing an increase in communication latency and deterioration of quality of service (QoS). In this paper, we propose a user-centric cooperative communication framework for next generation (xG) LEO satellite mega-constellation systems. In the proposed framework, a group of LEO satellites simultaneously serve all the user equipments (UEs) using the same timefrequency resources. By dynamically organizing the clusters of serving satellites and coordinating their joint transmission based on statistical channel state information (CSI), the handover frequency and inter-satellite interference can be reduced effectively, thereby achieving significant enhancements in the spectral efficiency and coverage probability. From the achievable rate analysis and extensive simulations on realistic xG LEO satellite communication environments, we show that the proposed scheme substantially improves the spectral efficiency and coverage over the conventional beam-centric systems.

Ensuring the integrity of range-based localization systems is crucial, particularly due to their pervasive use in critical civilian and military applications and their vulnerability to sophisticated spoofing attacks. Among these, multispoofer attacks, involving coordinated and strategically positioned spoofers capable of manipulating signals to evade detection, represent a significant operational threat. This article investigates filtering approaches to enhance the resilience of vehicles equipped with directional receivers, such as antenna or hydrophone arrays, against such advanced threats. To address this challenge, it introduces the adaptive resilience navigation filter (ARNF), designed to detect ongoing attacks, identify compromised signals, and mitigate their effects. Leveraging statistical hypothesis testing and single phase differences from antenna array measurements, the ARNF dynamically adapts to spoofed and nonspoofed signals to estimate biases introduced by spoofers and restore navigation accuracy. Validation through simulations under realistic global navigation satellite system (GNSS) conditions demonstrates the efficacy of the ARNF, with performance compared to the 2-stage extended Kalman filter and the ideal clairvoyant extended Kalman filter.

Recently, wideband beamforming using extremely large-scale antenna array (ELAA) systems have gained much interest as a means to boost throughput in next generation (xG) networks. However, conventional phase shifter (PS)-based beamforming methods face challenges in wideband ELAA systems due to the beam squint effect, where beams at different frequencies become misaligned. Although the use of true time delay (TTD) can address this by creating frequency-dependent beamforming vectors, traditional TTD-based methods still experience considerable sidelobe leakage due to the mismatch between intended and generated beams. In this paper, we introduce a novel wideband beamforming architecture that dynamically configures connections between TTDs and PS subarrays using a switching network. By jointly optimizing subarray connections, TTD time delays, and PS phase shifts, wideband dynamic array-of-subarrays (WDAoSA) minimizes sidelobe gain and maximizes array gain in wideband ELAA systems. Numerical results show significant improvements in both array gain and data rate compared to conventional TTD-based methods.

Satellite imagery plays a crucial role in integrated satellite-ground remote sensing (SGRS), particularly in applications such as disaster management and military intelligence, where real-time monitoring and forecasting are essential for effective decision-making. However, narrow artificial intelligence (AI) models often face challenges in processing large-scale high-dimensional data efficiently while maintaining the required accuracy and speed, limiting their effectiveness in time-sensitive scenarios. To address these challenges, we explore the integration of satellite remote sensing with large AI, quantum computing, and quantum communication technologies, focusing on enhancing computational efficiency and data security in integrated SGRS systems. Specifically, we put forth an integrated quantum SGRS framework, which combines quantum fusion intelligence (QFI) with quantum anonymous communication (QAC). By integrating quantum and large AI, the QFI models enhance the efficiency, accuracy, and security of satellite imagery analysis while ensuring that the extracted information is transmitted to ground stations in a privacy-preserving manner using QAC. This approach is particularly effective in time- and privacy-sensitive scenarios. To demonstrate the effectiveness of QFI computing, we present case studies in disaster detection and environmental monitoring. This research highlights the transformative potential of quantum-large AI integration in SGRS and its implications for nonterrestrial-terrestrial quantum networks.

Accurate location awareness is essential for various context-based applications. This calls for efficient methodologies to collect, communicate and process position-dependent measurements, especially in situations with limited computational resources. The soft information (SI) approach has recently shown significant improvements in accuracy over conventional localization methods. By developing efficient SI-based techniques, it is possible to achieve higher precision also in case of stringent computational constraints. This paper proposes new SI-based localization techniques that utilize belief condensation and maximum entropy methods to reduce both communication burden and computational complexity. In addition, the techniques presented enable the use of generic sensing measurements, including those taking discrete and categorical values. Through two case studies involving time and angle measurements, we demonstrate how the proposed approach can significantly improve localization accuracy and computational efficiency.

Federated learning (FL) is a distributed learning framework designed for large-scale applications. The core advantage of FL is that each participant is not required to share the local data with a central server. This inherent privacy-preserving capability is well suited to the increasingly popular large-scale generative AI models. However, some studies have shown that FL is susceptible to reconstruction attacks, in which an attacker leverages the acquired gradients or model parameters to reconstruct a victim's data. In this article, we propose a novel reconstruction attack scheme based on generative adversarial networks (GANs) in an asynchronous FL scenario that can reconstruct a victim's dataset without an auxiliary dataset. This adversarial scheme demonstrates a significant ability to reconstruct a dataset of victims accurately, thereby posing a substantial threat to user privacy, particularly in the context of large-scale models. Furthermore, we explore various defense mechanisms based on the characteristics of asynchronous FL and ultimately establish a viable defense scheme based on homomorphic encryption and an intermediate server. The proposed defense framework successfully and flawlessly defends against reconstruction attacks from the server side in an asynchronous setting without degradation of the model performance.

In the shift toward the quantum computing era, the foundational principles of classical cybersecurity, particularly in the realm of cryptographic algorithms, are facing unprecedented challenges. This demands comprehensive reevaluation and redesign of cryptographic infrastructures to withstand quantum adversarial attacks. With the emergence of the quantum Internet, a new approach to secure communication is possible, utilizing quantum properties that have no counterpart in classical systems. As the quantum Internet facilitates the exchange of quantum information, data publication protocols become essential in anonymizing and protecting privacy-sensitive data in quantum communication networks. This paper proposes two controlled quantum anonymous communication (QAC) protocols for publishing classical and quantum information on an Internet server (IS) with the assistance of a communication service provider. The first protocol allows for the controlled publication of classical information without revealing the publisher’s identity such that an adversary, even with access to all network resources, cannot trace the publication source—i.e., achieving perfect untraceability. The second protocol enables anonymous publication of quantum information on an IS in a controlled and untraceable manner. These protocols serve as essential building blocks for advancing the quantum Internet, which has the potential to transform communication and information exchange methods. We provide a detailed anonymity analysis of these QAC protocols for data publication, ensuring that the published symbol or qudit information remains untraceable to its publisher. Moreover, the performance analysis in terms of publication error probability, fidelity, and degree of anonymity in noisy environments demonstrates the robustness of the protocols against noise and adversarial attacks.

QDE is an emerging paradigm in which an ancillary quantum system is used to remove entropy from a primary quantum system of interest. As a means for entropy reduction, quantum dissipation engineering (QDE) is useful across the domains of quantum sensing, computing, communication, and networking. Whereas most existing approaches concern QDE over an infinite time horizon, this paper studies the use of engineered environments to prepare the primary system in a pure state within finite time. This corresponds to completely transferring the primary system's initial entropy to the engineered environment in finite time. Necessary and sufficient conditions for performing this task are derived. These conditions elucidate the potential of finite-time QDE using real-world quantum systems.

Quantum systems for sensing, communication, control, and computing are pivotal for applications involving quantum networks. Such systems can perform quadrature measurements to extract information of interest inherent in the quantum states. Therefore, the design of quantum states is crucial to achieving high accuracy of the quadrature measurement. The widely used Gaussian states lack some relevant non-classical properties, thus calling for the design of quantum systems using non-Gaussian states. This paper characterizes the quadrature measurement accuracy for the photon-varied Gaussian states (PVGSs), which are a class of non-Gaussian states that can be generated using current technologies and possess relevant non-classical properties. First, we derive the wavefunctions of single-mode PVGSs. Then, we characterize the quadrature measurement accuracy and compare it with that for Gaussian states. The findings of this paper provide insights into the design of enhanced quantum systems and networks using single-mode PVGSs.

This paper proposes a grid-free positioning algorithm for near-field (NF) communications based on machine learning. Due to the NF effects of large-aperture array antenna, beam training measurements are affected by the angle and the distance of the user, which makes it possible to be used for user positioning. Therefore, unlike conventional beam management, the proposed algorithm is designed to learn the relationship between beam training measurements and the user position, enabling not only the establishment of communication links but also the additional estimation of the user's position. Throughout simulations, we compare the proposed algorithm with the traditional beam training algorithms. The simulation results confirm that the proposed algorithm achieves higher user positioning accuracy compared to beam training.

Due to the multi-dimensional search in the near-field (NF), the excessive computational burden has become one of the major problems. To address this issue, this paper proposes a computationally efficient angle and distance estimation algorithm for extremely large uniform planar array (UPA) systems. To reduce computation, the proposed algorithm decouples 3D search into a series of 2D search and 1D search. The 2D search estimates the azimuth and elevation, followed by the 1D search that estimates the distance. While the proposed algorithm brings significant improvement in computational complexity, the estimation of the proposed algorithm is guaranteed to be accurate as long as the distance between the receiver and transmitter (or scatterer) exceeds a specific threshold. For UPAs, we establish that this threshold is around a quarter of the Rayleigh distance. The simulation results demonstrate that the proposed algorithm has a superior accuracy-complexity trade-off compared to existing works.

The massive amount of data related to spatiotemporal mobility offers new opportunities to understand human mobility with applications in various sectors, including transportation, logistics, and safety. However, the increase in the volume and in the dimension of mobility data makes it challenging to retrieve important information and critical features of spatiotemporal mobility. This paper develops a method to estimate probabilistic occurrences of travel demands considering interactions between origin, destination, and departure time. First, we reveal the important features in the complex structure of mobility data and identify mobility patterns. Then, we derive a data-driven model, accounting for mobility patterns, to estimate and predict travel demands. We quantify the accuracy of the proposed method for a case study using both New York city yellow taxi trip data and for-hire vehicles trip data over the entire city. Results show the accuracy of the proposed method compared to existing approaches.

Quantum ranging is crucial in several applications such as radar and localization. This paper determines the quantum advantage of ranging with single-mode displaced squeezed states. Exact analytical expressions for the quantum Fisher information (QFI) about the range parameter are derived in the presence of thermal loss channels characterized by arbitrary loss and noise parameters. The quantum advantage, termed as gain, is determined as a ratio of QFI with and without employing squeezing. It is shown that the gain can be unboundedly large in the optical regime whereas it is upper bounded in the microwave regime.