Introducing friendly jammer to unmanned aerial vehicle (UAV) covert communication could further enhance the covert performance. However, due to the additional communication overhead required for information synchronization (IS) among cooperating parties, the security risk is further elevated. It is noteworthy that cognitive jamming (CJ) could sense the cooperating node signals without IS links. Therefore, in this paper, we propose the cooperative UAVs covert communication scheme assisted by the CJ that maximize the minimum average covert rate (MACR). Specifically, the UAV (Alice) tries to transmit its private message to legitimate users under the surveillance of the Wardens. Since the Warden is in passive surveillance, the practical location uncertainty of the Warden is considered, and we further analyze the worst-case situation that derives the minimal detection error probability of the Wardens. Moreover, we maximize the MACR by alternatively optimizing the sense time ratio and trajectory of CJ, the transmit power and trajectory of the Alice. To expedite algorithmic convergence, we further introduce a principled initialization scheme. The simulation results reveal that the covert performance of the proposed algorithm outperforms the other schemes, especially, in the multi-warden scenario. Furthermore, compared with the full-time jammer and probabilistic jammer, the CJ can not only achieve a higher covert rate with less average jamming power, but also require no information synchronization link with Alice based on spectrum sensing, which promises considerable application prospects in real-world systems.

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.

Autonomous aerial vehicle (AAV) covert communication could further enhance the quality and coverage of covert channels. However, in complex low-altitude environments, the air-to-ground (A2G) links are susceptible to the fading effects, which may degrade the performance of covert communication. In this paper, we investigate the fluid antenna (FA) enabled AAV covert communication, where a AAV equipped with FA serves multiple ground users in the presence of an active Warden. We aim to maximize the minimum average covert rate by jointly optimizing the beamforming vectors, AAV trajectory and fluid antenna positions. First, we derive closed-form expressions for the minimum detection error probability (MDEP) and the optimal detection threshold, accounting for uncertainties in both the noise variance and the self-interference channel coefficient. Secondly, we propose an alternating optimization algorithm subject to the covertness constraint, power constraint, and the Warden’s position uncertainty. Specifically, the original nonconvex problem is decomposed into tractable subproblems via the block coordinate descent, which could be solved successively by successive convex approximation, semidefinite relaxation, and Dinkelbach transformation. What’s more, a low-complexity algorithm is developed for the single-user scenario to improve the practical applicability of the proposed framework. Finally, simulation results validate the effectiveness of the proposed FA-AAV covert communication scheme. Moreover, compared to the fixed position antenna scheme, the FA-AAV could improve the covert performance, especially in strong channel fading environments, which is beneficial for practical application.

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.

The environmental shelters such as public links can enable covert communication by covering covert transmission. To further exploit shelters, this paper focuses on a two-hop system where multiple pairs of public users and one pair of covert users communicate through a multi-antenna relay. We aim to improve covertness performance while satisfying the covertness constraints of two hops and quality of service (QoS) requirements of public users. The covert throughput maximization problem is formulated via jointly optimizing transmit power and beamformer, which is challenging to solve. We introduce successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to convert the problem into convex, where the joint optimization algorithm is developed. Considering the computational complexity, we further design a block diagonalization (BD) beamformer at the relay, which translates the beamformer optimization into a power allocation problem and derives the optimal solution in a closed form. We analytically show that the covert throughput first increases and then decreases as the number of public pairs increases in BD-based design, which has been verified numerically and can be generalized in other designs. Numerical results also evaluate the superiority of the joint optimization algorithm and the effectiveness of the BD-based efficient design. In particular, the BD-based design is very close to the joint optimization under small maximum transmit power of users or large maximum transmit power of relay.

Data-driven cross-layer secure design is expected to provide effective support for high-reliability and high-speed 6G network services. In this paper, we study the joint anti-jamming decision problem for phy-layer scheduling and congestion control in the transport layer. To address the challenges of the extremely huge action space and the simultaneous existence of multi-dimensional and multi-scale action variables, we propose a hierarchical DRL anti-jamming algorithm. Firstly, we unify the time scales of the variables by defining the state space, action space, and reward function, and construct them as a Markov decision process. Second, we make decisions in different dimensions sequentially through a hierarchical learning algorithm, which compresses the size of the action space while maintaining the correlation between variables. Simulation results show that the proposed cross-layer design method can realize a good adaptation between the lower layer and the transport layer in the context of anti-jamming requirements, which significantly improves the user QoS and system throughput compared with the baseline algorithms.

In this paper, we consider a novel covert communication scenario where the warden does not have prior knowledge of the transmitter’s power. In this scenario, the current widely adopted likelihood ratio test (LRT)-based detector is not optimal anymore. To address this, we formulate a detection framework based on the generalized likelihood ratio test (GLRT), which replaces the unknown parameter with maximum likelihood estimation (MLE) and is proven optimal in the scenario. Based on the GLRT-based framework, two different detection models are proposed, where one only utilizes observations of the current time and the other can exploit observations of past time slots. We analyze the estimation and fisher information of the transmit power, and even the detection and covert performance under two different detection models, respectively. Based on the analytical results, we further derive the maximum number of tolerable slots under which the transmitter can remain the same transmit power while the detection error probability of the warden is still larger than the regulated threshold. Moreover, the transmit power and the number of tolerable slots are jointly optimized to maximize the transmission throughput subjecting to covertness constraint. The numerical results demonstrate the correctness of the theoretical analysis. We show how the covert rate is influenced by the number of transmitting slots and the transmit power, which can guide the design of the covert transmission strategy.

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.

Neighbor discovery is the procedure to establish a first contact between two wireless devices. For duty-cycled low-power devices, energy consumption is closely related to neighbor discovery latency. Actually, in recent protocols, such as Bluetooth low energy (BLE) or ANT+, neighbor discovery latency is determined by the parameters used by the devices, such as advertising interval, scan window, scan interval, and so on. A fundamental problem of the BLE-like protocol is that the exact relation between parameters and discovery latency has not been fully analyzed. In this article, we propose a Euclidean-division-based low-complexity precise analytical approach that can derive the mathematical expressions of both worst-case latency and average latency for any parameter groups. It is confirmed by simulation results that our solution can make highly accurate predictions about the value of latencies. Simulation results also show that the proposed solution has an extremely low complexity. Moreover, we derive the lower bound of latency for given duty cycles, which provides useful guidelines for the choice of energy-efficient parameter groups for BLE.

This article proposes a framework of cooperative multistation secure transmission in high-frequency skywave communications for wide-area Internet of Things applications by simultaneously exploiting the benefits of massive multiple-input multiple-output and coordinated multiple points communications. In this framework, the original message sent to the user is divided into several submessages by the core network, each of which will be transmitted to the user by the base stations (BSs). A joint optimization problem is established to maximize the user rate by optimally utilizing multiple BSs with multiple antennas for cooperative precoding. Semidefinite programming is introduced for the precoding vector design for the partial channel state information scenario. To solve the mixed-integer optimization problem of selecting BSs, a low complexity algorithm is proposed. Simulation results show that the performance of the proposed algorithm approaches that of exhaustive search, which demonstrates the effectiveness of the proposed algorithm.

Unmanned aerial vehicle (UAV)-enabled air-ground integrated communication systems are vulnerable to jamming attack, mainly due to the high probability with line-of-sight wireless channel conditions. Although a few UAV anti-jamming transmission schemes have been recently proposed, the fundamental performance limits by simultaneously considering the UAV and jammer’s mobility have not yet been reported. These observations motivate us to formulate an interactive reward region (IRR) characterization problem for the scenario that the UAV and the jammer compete with each other to maximize their respective rewards via power and mobility control. Then, we propose a transmit and jamming power optimization algorithm to characterize the IRR with power control, by leveraging successive convex approximation technique, where two transmit power design schemes are given in closed-form expressions from a game-theoretic perspective. Furthermore, the IRR with joint power and mobility control is characterized based on alternating optimization, in which both the UAV and jammer can simultaneously adjust their power and trajectories. Finally, extensive simulation results reveal that joint transmit power and trajectory optimization can enlarge the IRR, and increasing the UAV mobility and flight time length improves the UAV reward.

Abstract This paper studies unmanned aerial vehicles (UAV) enabled industrial Internet of Things while a UAV dispatched to collect data of low‐power ground sensor nodes (SNs) in multi‐obstacle environment. The authors aim to minimize the completion time while satisfying the communication link constraints of each SN and obstacle avoidance, data collection requirements etc. To this end, the authors first formulate the completion time minimization problem by jointly optimizing the UAV trajectory and collection sequence of SNs. The problem is difficult to be optimally solved, as it is non‐convex. To tackle this problem, the authors first transform the original problem to a Traveling Salesman Problem‐like (TSP‐like) problem based on a hover point that can naturally satisfy the communication link constraints of data collection. The dynamic programming (DP) algorithm to figure out the order in which the UAV collects each SN, which gives the initial path of the UAV traversing each SN from the beginning point to the end point. Next, the authors consider the general scenarios of data collection tasks where the UAV also communicates while flying. The authors construct an equivalent problem with integer variable constraints for the original problem with indicative function constraints. The authors rewrite the non‐convex constraints of the equal problem by introducing slack variables and leveraging the SCA, and add the discrete region threat constraints for the traditional path discretization method. Finally, the simulation results verify the effectiveness of the proposed algorithm under different parameter configurations.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

The anti-jamming routing for the Internet of Satellites (IoS) has drawn increasing attentions due to the unknown interrupts, unexpected congestion and smart jamming. This paper investigates anti-jamming routing scheme for heterogeneous IoS, with the aim of minimizing anti-jamming routing cost. Firstly, to tackle the smart jamming which can automatically change jamming strategies according to the jamming effect, we formulate the routing anti-jamming problem as a hierarchical anti-jamming Stackelberg game. Secondly, we propose a deep reinforcement learning based routing algorithm (DRLR) to obtain an available routing path subset. Furthermore, based on this set, a fast response anti-jamming algorithm (FRA) is proposed to achieve fast and reliable antijamming routing. Finally, the simulations have shown that the proposed algorithm have lower routing cost and better antijamming performance than existing approaches.

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.

Satellite-enabled army Internet of Things (SaIoT) has drawn increasing attention due to the wide-coverage and large-capacity transmission. However, the smart jamming based on artificial intelligence technologies has seriously degraded SaIoT performance. Thus, this article investigates the distributed dynamic anti-jamming scheme for SaIoT to decrease energy consumption in the jamming environment. First, a hierarchical anti-jamming Stackelberg game (HASG), which consists of the leader subgame for jammers and the follower subgame for SaIoT devices, is proposed to formulate the confrontation interaction between jammers and SaIoT devices. It has been proved that there exists a Stackelberg equilibrium in the proposed HASG. Then, an anti-jamming coalition formation game (CFG) is proposed for the follower subgame to decrease the energy consumption in the jamming environment, and the modified coalition preference order and coalition change principle are put forward to enhance the performance of the proposed anti-jamming CFG. Furthermore, with the help of the exact potential game, we have demonstrated that the proposed anti-jamming CFG could converge to the stable coalition formation and it is able to achieve similar performance to the centralized optimization via a distributed approach. Finally, reinforcement-learning-based algorithms are utilized to obtain the suboptimal anti-jamming policies according to the dynamic and unknown jamming environment, and simulation results validate that the proposed approach achieves better performance than the existing approaches.

Unmanned aerial vehicles (UAVs) have attracted increasing attention in wireless communications due to the high mobility. This paper investigates a fixed-wing UAV-to-UAV (U2U) communications system, with the aim of minimizing the information transmission time via proactively designing the UAV paths. First, we propose a general optimization framework for U2U communications, which covers the communication throughput requirement, interference from terrestrial transmitters, UAV maximum/minimum speeds and accelerations, and minimum U2U distance. To tackle the formulated optimization, the communication throughput constraint that contains uncertain locations of terrestrial transmitters is transformed into a deterministic expression with the aid of S-procedure, and the nonlinear equality constraints on the UAV paths are replaced by linear equality constraints with additional positive semidefinite matrix constraints. Then, we develop a path planning algorithm based on the exact penalty method and successive convex approximation. Furthermore, we design a heuristic path planning algorithm that solves the completion time minimization problem by iteratively addressing a series of throughput maximization problems. The proposed heuristic algorithm strikes a good tradeoff between the computational complexity and the achievable performance. Finally, the simulation results are presented to verify the proposed path planning algorithms under various parameter configurations.

In this paper, we consider the spectrum sharing planning problem for a full-duplex unmanned aerial vehicle (UAV) relaying systems with underlaid device-to-device (D2D) communications, where a mobile UAV employed as a full-duplex relay assists the communication link between separated nodes without direct link. Our design aims to maximize the sum throughput under the transmit power budget, while guaranteeing the coexistence with terrestrial D2D pairs, satisfying the information causality and UAV's trajectory constraints. First, the transmit power planning with a given trajectory is investigated, where a successive convex algorithm is developed by leveraging the D.C. (difference of two convex) programming. Then, we propose a two-step trajectory design method for the given transmit power since the constraints of D2D pairs result in a non-convex feasible set. Furthermore, an efficient spectrum sharing method for an aerial UAV and terrestrial D2D communications is designed by alternately optimizing the transmit power and UAV's trajectory. Finally, simulation results under various parameter configurations are provided to show the effectiveness of the proposed algorithms.

Device-to-device communication and energy harvesting are both key technologies to improve spectrum and energy efficiency. In this paper, we investigate the resource allocation problem for the energy harvesting-powered D2D communication underlaying cellular networks, where D2D pairs firstly harvest energy and then transmit information signals. The goal is to maximize the sum throughput via joint time scheduling and power control while satisfying the SINR requirement of cellular user and taking into account the energy constraint. The formulated non-convex problem is transformed into a nonlinear fractional programming problem with a tactful reformulation. Coupled with D.C. (difference of two convex functions) programming, a near optimal solution of the non-convex problem can be obtained by iteratively solving a sequence of convex problems. Then, a first-order algorithm is employed to solve these convex problems. Numerical simulations are conducted to validate the effectiveness of the proposed algorithm and evaluate the system throughput performance.

In this paper, we study robust spectrum sharing under channel uncertainty in a cognitive radio network (CRN), where a great number of secondary users (SUs) and a primary user (PU) coexist with each other sharing the same frequency spectrum. Considering the practical challenge that the channel gain between SU transmitters (SU-Txs) and PU receiver (PU-Rx) is typically uncertain, a probabilistic interference constraint is introduced in this paper. Although the interference constraint can be transformed into a linear outage probability formation, the optimization problem is a non-convex and non-linear programming (NCNLP). To circumvent the difficulty of directly solving the problem, we convert the original objective function and the outage probability constraint into suitable forms by the mathematical transformation and apply the convex optimization theory to solving this intractable problem. Moreover, we employ the interior point method to design an efficient algorithm and acquire a near-optimal solution. Finally, numerical simulations are carried out under various parameter configurations, which demonstrate that the proposed robust spectrum sharing algorithm can achieve higher performance than the state-of-the-art schemes.