This paper investigates the issue of wireless network topology inference via passive sensing of radio frequency (RF) signals without accessing their content. In non-cooperative scenarios, topology inference faces numerous challenges including the indirect, limited, and unreliable nature of available information, with no practical application instances demonstrated to date. To address these issues, we design a blind wireless network topology inference framework consisting of three key functional modules: signal detection, specific emitter identification (SEI), and topology inference. Guided by this framework, we present a systematic approach. Specifically, to confirm the identity of the detected signals, we propose a cross-domain robust SEI method based on transfer learning. These capabilities enable the mapping of raw RF signals to network interaction behaviors. Furthermore, to adapt to network dynamics and interaction behaviors across different time scales, we propose an adaptive topology inference method based on multivariate Hawkes processes (MHP) with dual timestamps. Finally, we develop an experimental validation system to evaluate the proposed framework. Experimental results demonstrate that our framework can accurately reconstruct the network topology from over-the-air RF signals, representing a significant step from theory to practice. Additional performance analysis confirms the superiority of the proposed topology inference method in both accuracy and adaptability.

The integrated communication and jamming system can integrate communication and jamming functionalities to reinforce each other in one system, arising from the increasing demand for equipment miniaturization and resource multiplexing. This paper proposes the integrated communication and jamming network (ICAJN) where multi-functional nodes serve asmobileaccess points, providing flexible communication and jamming coverage in the temporary, infrastructure-less scenarios. In addition to coverage, connectivity is also essential to ensure distributed coordination and signalling interaction among nodes. However, achieving simultaneous coverage and connectivity is challenging due to two main factors: on one hand, there exists an inherent tradeoff between coverage and connectivity in terms of node deployment; on the other hand, the dynamic electromagnetic environment complicates the real-time link gain estimation. The goal of this paper is to incorporate environment-awareness capabilities into the ICAJN, dynamically adjusting the working state to achieve simultaneous coverage and connectivity. To this end, the workflow of the ICAJN is structured into an environment-awareness phase and an execution phase, and the event-triggered sensing protocol (ETSP) is introduced to activate sensing function on demand. In the environment-awareness phase, the spatial interpolation is developed to estimate the gains of wireless links in the yet-to-reach locations, and further infer communication/jamming coverage boundaries. In the execution phase, the working state optimization algorithm is proposed to determine optimal node deployment locations, transmit power, and grouping policies in the current environment. Extended simulation results demonstrate that incorporating environment-awareness capabilities into ICAJN can significantly improve the network performance. And the designed protocol shows adaptability to the dynamic electromagnetic environments.

In this paper, we aim at reconfigurable intelligent surfaces (RIS) aided multi-user communication systems including multiple user pairs, where an important target user pair needs to guarantee covertness in the presence of an adversarial warden with imperfect channel state information (CSI). By adopting the grouping technology, the user pairs in the same group share the same channel while different groups use orthogonal channels. Then, the mutual interference from the perspective of cooperation is considered to provide a covert cover for the target user pair. As a result, a grouping enhanced cooperative covert communications scheme with the aid of RIS is proposed. First, the analytical expressions of detection error probability, equivalent covert constraint based on Kullback-Leibler divergence and general user covert rate are derived. Then, to simultaneously guarantee the covertness and the robustness, the maximization of the worst-case sum rate with covert constraint is modeled as a non-convex optimization problem by jointly designing user group selection, transmission power allocation and RIS phase shift. To deal with the intractable problem consisting of mixed-integer programming, an alternative optimization algorithm that includes three subproblems is proposed. In each subproblem, fractional programming, relaxation variables and successive convex approximation methods are applied. Besides, S-procedure and general sign definition lemmas are applied to transform the CSI uncertainty into linear matrix inequalities. Finally, the simulation results demonstrate that the proposed cooperative covert communications scheme always outperforms the benchmark schemes with a better robustness.

Specific emitter identification (SEI) is a physical-layer authentication technique that identifies devices by extracting radio frequency fingerprints (RFFs) from received signals. Open-set SEI (OS-SEI) refers to classifying known classes while rejecting unknown classes, which typically requires a sufficient amount of labeled training samples. However, in open-world scenarios, labeled samples are often limited, and unlabeled samples may contain unknown classes. Moreover, open-world recognition not only requires detecting unknown class samples but also identifying specific novel classes within these unknown samples and integrating them into the recognition model. Current OS-SEI methods can only categorize all unknown samples as a single class, lacking the ability to further differentiate these unknown classes. To address these challenges, we formulate a novel semi-supervised open-world SEI (SSOW-SEI) problem, which aims to overcome the shortcomings of OS-SEI in utilizing unlabeled data, distinguishing unknown classes, and addressing class distribution mismatches between labeled and unlabeled data. Furthermore, we develop an end-to-end similarity-adaptive (SAA) framework for SSOW-SEI. Specifically, after automatically extracting sample features, SAA first identifies novel classes by measuring pairwise similarities between the features, and then recognizes known classes using adaptive cross-entropy, which balances the learning rate between known and novel classes to prevent model bias toward known classes. Additionally, entropy regularization is applied to mitigate model overfitting. Extensive experimental results demonstrate that the proposed SAA framework effectively leverages limited labeled data, handles large volumes of unlabeled data, and accurately identifies both known and novel classes. The results also highlight its strong generalization, stability, and enhanced adaptability to novel classes.

Specific emitter identification (SEI) is a device identification technology that extracts radio frequency (RF) fingerprint from received signals. However, channel effects on RF fingerprint can vary between the training and testing stage, and SEI based on deep learning (DL) will be unable to withstand channel changes. To address this problem, we propose a channel-robust SEI scheme driven by causal learning. We analyze received signals from the causal perspective and construct a structural causal model (SCM) of SEI. In the SCM, received signals are considered as mixtures of the causal element and interference element, and only the former affects identification. Additionally, we design a new RF fingerprint feature representation called the centralized logarithmic power spectrum (CLPS) to reduce the impact of channel effects. Furthermore, we propose a causal purification network (CPNet) driven by causality to further alleviate channel effects. CPNet weakens the spurious associations between the channel and emitter labels through feature decorrelation and feature purification, strengthens the correlation between RF fingerprint and labels, and improves the generalization of SEI. Finally, our approach is evaluated extensively using 20 ZigBee devices under different channel environments. Experimental results demonstrate that our scheme can effectively alleviate channel effects, improve SEI performance under various channel environments, and exhibit good generalization and stability.

In this paper, unmanned aerial vehicle (UAV) and reconfigurable intelligent surfaces (RIS) are combined together to further enhance the covert communication system. In particular, a multi-antenna transmitter Alice transmits information to Bob, through UAV-RIS in the presence of an adversarial warden, whose channel state information (CSI) is not perfectly known at Alice. To simultaneously guarantee the covertness and the robustness, the minimum worst-case average covert transmission rate is maximized by jointly optimizing the beamforming vector, the phase shift vector and UAV trajectory. Moreover, the robust design of beamforming, phase shift and three-dimensional (3D) trajectory is formulated as a non-convex problem. To deal with the intractable optimization problem, an alternative optimization algorithm consisting of three subproblems is designed to deal with the joint optimization problem. The general sign-definiteness is utilized to transform the CSI uncertainty and successive convex approximation (SCA) methods are proposed to transform non-convex constraints. Besides, considering the high complexity of the proposed SCA based algorithm, we further develop a low-complexity algorithm for a special case of perfect CSI scenario, where analytical expressions of the beamforming and phase shift are derived. Finally, simulation results demonstrate that the noise uncertainty for hiding legitimate communication is not necessarily as high as possible for a large equivalent receiving power threshold β and the proposed scheme outperforms the state-of-the-art schemes.

In this paper, we study the problem of network reconfiguration when unmanned aerial vehicle (UAV) swarms suffer damage. Multiple UAVs are divided into several groups to perform various tasks. Each master UAV is connected to the ground control station and provides network services for small UAVs that perform various tasks, ensuring that the information of small UAVs can be transmitted back in a timely manner. When master UAVs are destroyed due to factors such as jamming or attacks, the associated small UAVs must select new master UAVs for network service and cooperate with other small UAVs to execute tasks. Based on the heterogeneity and relevance of tasks, we model and analyze the task relationship among different UAVs. Since both master UAVs and small UAVs have respective optimization objectives in the network reconfiguration process, we construct a many-to-one bilateral matching market to model the interaction between master UAVs and small UAVs. To realize an efficient solution for UAV network reconfiguration in complex environments, we propose a distributed matching algorithm and prove that the algorithm can converge to two-sided stable matching. Simulation results indicate that the proposed algorithm can significantly improve the task completion degree of the network compared with three other algorithms.

This article investigates the problem of synchronous randomized neighbor discovery with directional antennas. Due to the long tail effect, it will take long time to discover the last few neighbors, which increases overall neighbor discovery time. This effect is due to small proportion of remaining undiscovered neighbors. Moreover, improper choices of reception probabilities make the discovery even worse. In this article, a cognitive framework is proposed to minimize the expectation of neighbor discovery time. We present a scheme in which reception probabilities are dynamically adjusted. We consider an ideal scenario and a practical scenario. In an ideal scenario where perfect information about the number of neighbors is available, reception probabilities are adjusted according to the number of neighbors. A method of dynamic programming is used to recursively calculate the optimal reception probabilities. In an actual scenario where perfect information about number of neighbors is unavailable, a neighbor estimation method based on maximum-likelihood estimation is executed before probability adjustment. Simulation results show that when perfect information about neighbor is available and total transmission probability is within a proper range (between 0.1 and 0.2), the average neighbor discovery time can be significantly reduced (by 38% to 43%, respectively) compared with an existing probability-fixed scheme. With imperfect information, the scheme also works well and realizes appreciable reduction in average neighbor discovery time compared with existing self-adaptive schemes.

Unmanned-aerial-vehicles (UAVs)-enabled wireless communication for Internet-of-Things (IoT) applications has attracted increasing attention. This article studies a UAV-assisted data dissemination system, where a rotary-wing UAV is dispatched to disseminate data to terrestrial IoT devices. We target to minimize the completion time via a joint optimization of the UAV trajectory and transmit power, while considering the indispensable constraints which cover the maximum energy budget, speed, transmit power of the UAV, and data requirement for each IoT device. First, we formulate the UAV data dissemination as a completion time minimization problem. To tackle the nonconvex optimization problem, the original problem is transformed into two subproblems: 1) the trajectory optimization and 2) the transmit power optimization, respectively, by introducing auxiliary variables and leveraging the concave-convex procedure. Then, we develop a joint trajectory and transmit power algorithm via tailoring the successive convex approximation and alternating descent method. We further improve the algorithm by maximizing the throughput instead of minimizing the completion time in the transmit power optimization process. The improved algorithm not only reduces the computational complexity but also enhances the achieved performance. In addition, simulation results demonstrate the superior performance of the proposed algorithms under various parameter configurations.

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

This article focuses on the slot access problem for neighboring cooperation in unmanned aerial vehicle (UAV) swarms. To avoid the slot access process being hindered by unavailable topology information or information exchanges, a self-organized collision discovery mechanism is proposed. Each broadcaster can know whether its transmission is successful through the mechanism which provides the basic knowledge for finding the slot access strategy. Considering the distributed feature, the slot access problem is formulated as two game models. Both games are proved to have at least one Nash Equilibrum (NE) and the best NE is the optimum of the problem. Two distributed and synchronous algorithms are proposed to reach the NE. The first algorithm converges fast which satisfies the dynamic feature of UAV swarms and the second one converges to the optimum asymptotically. Moreover, to enhance the time efficiency of UAV swarms, the total number of required slots is investigated in some typical topologies and then conjectured to general ones. Simulation results verify that the proposed method is effective and the conjecture is true in almost all topologies.

Message interaction among neighborhood is necessary for unmanned aerial vehicles (UAVs) and Time Division Multiple Access is a feasible implementation by which each UAV broadcasts in one dedicated slot. However, the dynamic character requires the access process fast and the constrained energy limits the feedbacks during the process uninformative. Therefore, this article focuses on the slot access problem in UAV swarms. To be energy saving, a collision discovery method which is independent of valid information is designed to play the role of feedback. Furthermore, considering the decentralized structure, the slot access problem is formulated as a game model. To reach the Nash Equilibrium of the game fast, a synchronous and uncoupled learning algorithm is proposed to, in which all UAVs update simultaneously based on the information they obtain. The algorithm releases the requirement for a scheduler or a common control channel and is therefore applicable in UAV swarms. Simulation results verify the effectiveness of the proposed method and some investigations about the requirement for the number of slots are made.

In this study, the authors study the self‐organising user assignment problem for the multi‐user cooperation network. In the cellular network with user cooperation, idle users near the base station are seen as potential relay nodes, which can assist cell edge users to transmit data. Practically, the selfish nature of users is considered in the authors’ model. They design a score‐based system, where the strategies of relay assignment are affected by the historical behaviours of users. That means not only the transmission performance, but also the accumulative contributions of users are considered. The proposed score‐based system sufficiently encourages idle users to assist active users. Furthermore, the multi‐user assignment problem is formulated as a one‐to‐one matching game, in which idle users and active users rank one another individually based on their own preference. To address the issue, they propose a self‐organising mutually beneficial matching algorithm, which is proven to converge to a stable matching. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results.