The open and vulnerable nature of wireless channels exacerbates security risks in integrated sensing and communication (ISAC) systems, especially when the sensing targets act as potential eavesdroppers (Eves), and these risks intensify with collusion among Eves. To address this challenge, this paper investigates a novel strategy for a robust reconfigurable intelligent surfaces (RIS)-assisted secure ISAC system, where an ISAC base station facilitates simultaneous secure communication with legitimate users and sensing of multiple targets that may serve as Eves. We examine two different interaction mechanisms among Eves, namely, non-colluding Eves (NCE) and colluding Eves (CE), under both perfect and imperfect channel state information (CSI) assumptions. For both mechanisms, we formulate the optimization problem of maximizing users’ sum secrecy rate by jointly designing the transmit beamforming and RIS phase-shifts. This optimization is subject to constraints on transmit power, sensing requirements, and unit-modulus RIS phase shifts. The resulting non-convex problems are solved via alternating optimization (AO) algorithms. Specifically, in order to handle the severely non-convex and coupled objective function and multi-link accumulated channel error constraints caused by CE as well as imperfect CSI, we employ the majorizationminimization algorithm and the S-procedure to convert these problems into tractable forms. Simulation results validate the effectiveness of our proposed algorithms. We highlight that, at the expense of a 15% reduction in the users’ sum rate, our proposed algorithm achieves up to a 185% increase in the sum secrecy rate. Furthermore, we quantify the sensing-security trade-off by analyzing the reduction of the sum secrecy rate induced by sensing requirements, and we reveal the impacts of various factors on the sum secrecy rate, such as RIS element number, channel estimation errors, and sensing thresholds.

Movable antenna (MA) is an emerging technology which can reconfigure wireless channels via adaptive antenna position adjustments at transceivers, thereby bringing additional spatial degrees of freedom for improving system performance. In this paper, from a security perspective, we exploit the MA-enabled legitimate jammer (MAJ) to subvert suspicious multiuser downlink communications consisting of one suspicious transmitter (ST) and multiple suspicious receivers (SRs). Specifically, our objective is to minimize the benefit (the sum rate of all SRs or the minimum rate among all SRs) of such suspicious communications, by jointly optimizing antenna positions and the jamming beamforming at the MAJ. However, the key challenge lies in that given the MAJ’s actions, the ST can reactively adjust its power allocations to instead maximize its benefit for mitigating the unfavorable interference. Such flexible behavior of the ST confuses the optimization design of the MAJ to a certain extent. Facing this difficulty, corresponding to the above two different benefits: i) we respectively determine the optimal behavior of the ST given the MAJ’s actions; ii) armed with these, we arrive at two simplified problems and then develop effective alternating optimization based algorithms to iteratively solve them. In addition to these, we also focus on the special case of two SRs, and reveal insightful conclusions about the deployment rule of antenna positions at the MAJ. Furthermore, we analyze the ideal antenna deployment scheme at the MAJ for achieving the globally performance lower bound. Numerical results demonstrate the effectiveness of our proposed schemes compared to conventional fixed-position antenna (FPA) and other competitive benchmarks.

In this paper, we analyze the performance of covert communication using a half-colluding detection strategy for a pair of communicating users who face multiple Wardens aided by artificial noise. Introducing multiple Wardens increases the diversity of detecting strategy and makes the detection process more flexible. In our scenario, Wardens employ a half-colluding strategy where each detector performs the detection independently and uploads its respective detection results to the fusion center (FC) in the form of 1-bit messages. The FC combines these results and makes decisions based on the K-out-of-N rule. First, we analyze the detection performance of Wardens, solve the optimal threshold of the fusion rule, and derive an expression for the covertness constraint under the half-colluding strategy. Next, we analyze the covert rate under the covertness constraint and construct an optimization problem based on this analysis to maximize the covert rate of the communicating users by optimizing the transmit power. Finally, we validate our theoretical findings through numerical results.

This paper investigates the optimal channel exploration in multiple spectrum band covert communication. Different from most existing covert communication works that only consider the static wardens, we also take the reactive wardens that release real-time suppression tracking jamming based on their receiving power into consideration. In channel exploration period, the covert transmitter Alice aims to find a channel with high channel gain while guaranteeing its transmission covertness. However, obtaining channel gains on different bands before transmission takes Alice time and Alice has to choose the right time to stop exploring for throughput maximization. Firstly, to address the threats of the inactive and reactive wardens, the strategies of channel inversion power control and feedback based anti-jamming are respectively adopted. Then, we formulate the channel exploration problem with optimal stopping theory after analyzing Alice’s transmission covertness performance. Furthermore, since the conventional approach to this problem requires intensively computation, the one stage look ahead (1-SLA) rule is adopted to reduce the computation complexity. In particular, we mathematically prove that this rule is optimal in maximizing Alice’s expected throughput. At last, the simulation results are provided to validate the analytical results and the superiority of the proposed scheme compared with the benchmarks.

This paper addresses the challenge of anti-jamming in moving reactive jamming scenarios. The moving reactive jammer initiates high-power tracking jamming upon detecting any transmission activity, and when unable to detect a signal, resorts to indiscriminate jamming. This presents dual imperatives: maintaining hiding to avoid the jammer’s detection and simultaneously evading indiscriminate jamming. Spread spectrum techniques effectively reduce transmitting power to elude detection but fall short in countering indiscriminate jamming. Conversely, changing communication frequencies can help evade indiscriminate jamming but makes the transmission vulnerable to tracking jamming without spread spectrum techniques to remain hidden. Current methodologies struggle with the complexity of simultaneously optimizing these two requirements due to the expansive joint action spaces and the dynamics of moving reactive jammers. To address these challenges, we propose a parallelized deep reinforcement learning (DRL) strategy. The approach includes a parallelized network architecture designed to decompose the action space. A parallel exploration-exploitation selection mechanism replaces the$\varepsilon $-greedy mechanism, accelerating convergence. Simulations demonstrate a nearly 90% increase in normalized throughput.

This paper focuses on distributed resource management and task scheduling for multi-access MEC networks against the intelligent eavesdropping jammer (IEJ). Due to the lack of a central controller, the problem of joint task scheduling and network resource allocation is formulated as a distributed multi-user hybrid-integer non-convex model.The optimization objective is to maximize users’ satisfaction while meeting the Quality of Service (QoS) requirements of tasks and ensuring the high-reliable demands of data offloading. To overcome the challenge of partial observability for users, the channel observation matrix and Gramian Angular Field (GAF) are utilized to preprocess the limited channel state information and to mine the potential time-frequency characteristics of the external environment. Moreover, the hierarchical architecture and parallel networks are introduced for a parameterized redesign of the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) method to improve the decision accuracy. Finally, simulation results demonstrate the superiority of the proposed algorithm over existing methods in terms of delay, energy consumption, and security.

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.

Neighbor discovery is a prerequisite to establish self-organizing IoT networks. IoT devices are mostly energy-limited, so neighbor discovery is usually implemented in a duty-cycled manner. A fundamental problem of optimizing energy efficiency of duty-cycled neighbor discovery is to minimize neighbor discovery latency for given duty cycle. We investigate purely interval (PI)-based neighbor discovery that adopts pseudo-random delay to avoid blocking problem, which is also adopted by pseudo-random delay advertising mode of Bluetooth low energy (BLE). In this article, we proposed an additional scan windows-based assisted protocol, which achieves early discovery with the help of assistance mechanism. Based on the assistance mechanism, we model and optimize the neighbor discovery latency of the PI-based protocol. Moreover, to analyze the impact of assistance failure caused by contention problem in a clique scenario, we proposed a mutual exclusive drawer model. Simulation results show that the proposed protocol using the derived near-optimal parameters achieves 26% lower average latency in sparse networks.

This paper investigates the problem of multidimensional resource management in multi-access mobile edge computing (MEC) networks against external dynamic jamming. The objective is to minimize the long-term computational cost of the MEC network while satisfying the task computation delay requirements of user equipment (UE) by jointly optimizing computing and communication resource allocation. To overcome challenges such as frequency conflict and dynamic jamming attacks, a distributed multi-agent hierarchical deep reinforcement learning (MAHDRL) MEC framework based on hybrid heterogeneous decision-making is proposed. Specifically, a hierarchical MEC anti-jamming data offloading optimization model is constructed, and the MEC resource management problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). Based on this, a distributed MAHDRL algorithm based on the actor-critic (AC) model is designed to solve the multi-agent high-dimensional nonlinear hybrid integer programming NP-hard problem: the high-level network in the base station (BS) optimizes discrete channel access strategies, while the low-level network in UEs learns data offloading strategies. Additionally, the computational complexity is discussed and a theoretical proof of the algorithm convergence is presented. Simulation results demonstrate the superiority of the proposed algorithm, which reduces energy consumption and data processing delay across the network.

As a fundamental requirement for IoT communication systems the importance of highly reliable anti-jamming communication methods in safety use cases is growing. In this article, we propose a novel anti-intelligent jamming scheme called opponent awareness-based anti-jamming algorithm (OA3). The user-jammer-environment interaction is formulated as a two-player simultaneous action stochastic game where participators have the ability to update their strategies. The decision-making process of each agent is modeled as a Markov decision process (MDP). Begin with the intuition “learn how the jammer learns,” the opponent awareness-based iterative learning objective (OAL) of the user is presented by considering the learning awareness of the jammer to defeat the intelligent jamming. Finally, we introduce the framework, including offline policy learning and online policy exploiting to implement OAL and accelerate the learning. Simulations show that the OA3 outperforms the benchmark anti-jamming strategy in terms of packet success rate.

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.

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.

Intelligent reflective surfaces (IRSs) as low energy consumption and easy to attach devices have been widely applied in the field of anti-jamming recently. In particular, the combination of IRS and unmanned aerial vehicle (UAV), as IRS-UAV, further expands the scope of IRS services. In this article, the joint IRS selection and beamforming optimization problem has been investigated in multiple IRS-UAV-assisted anti-jamming D2D networks. To solve the above optimization problem, a distributed matching-based selection and$Q$-learning-based beamforming optimization algorithm (DMQ) was proposed. In detail, the optimization problem is decomposed into two subproblems, namely, the IRS selection subproblem is formulated as a noncommutative many-to-many matching game model to describe peer effects and uncertainty selection quotas, and the passive beamforming optimization subproblem is solved by a reinforcement algorithm to satisfy the complex environment. Numerical simulations confirm the convergence and near-optimal performance of the proposed scheme with lower latency and greater robustness.

The primary objective of anti-jamming techniques is to ensure that the transmitted data arrives at the intended receiver without being disturbed or jammed with by any jamming signal or other hostile activities to ensuring the security of the communication system. Deep reinforcement learning (DRL) has been extensively utilized in solving the dynamic spectrum anti-jamming problem. However, most of existing DRL-based algorithms require lots of training time, which fails to adapt the fast-channging jamming environment. Our objective is to find a practical and fast-convergence anti-jamming learning solution. To achieve this, we redesign the DRL algorithm in the following two ways. First, we split the cycle of reinforcement learning into two parts: applying process and training process. Second, we use soft labels instead of rewards which bring more information. We further theoretically show that the information gain can help our proposed algorithm converge faster. Moreover, we also show that our labeled DRL algorithm is better than the idealized DRL-based scheme which can obtain the same information as the soft labels. Simulation results demonstrate that compared with existing DRL-based algorithms, our proposed algorithm reduces the number of iterations by up to 90%.

We study a wireless powered network, including a power beacon (PB), an energy-harvesting (EH)-based source, and multiple users. To improve the spectrum efficiency of the network as well as for practical implementation consideration, two users are paired to perform nonorthogonal multiple access (NOMA) transmission. Specifically, the NOMA-based transmission protocol consists of two phases, where the source harvests energy from the PB in the first phase, and then sends a superimposed signal to the paired users in the second phase. We derive exact and asymptotic closed-form expressions of the average throughput for each paired user. Then, the joint optimization problem for the time and power allocation is investigated to achieve the optimal fair performance of the paired users. To provide a benchmark, optimal resource allocation strategy for an orthogonal multiple access (OMA)-based transmission protocol is also studied. Simulation results confirm the validity of our analytical derivations and show that the considered network with NOMA transmissions is superior to that with OMA transmissions, especially when the transmit power of the PB is low and the paired users have significant differences in channel gain.

This paper studies a two-user cooperative non-orthogonal multiple access network with a novel cooperation scheme, where the source communicates with the near user directly or through the help of K relays, while the communication to the far user relies on the help of the near user or the K relays. We propose a new amplify-and-forward (AF)-based transmission protocol in which the near user sends a 1-bit feedback to inform relays of its detection results at the end of the first phase. Based on the 1-bit feedback and the available channel state information at each relay, optimal relay selection strategy encompassing two different selection criteria is designed to minimize the system outage probability (SOP). Tight-approximated as well as asymptotic closed-form expressions of the SOP are derived. Asymptotic results illustrate that the proposed relay selection strategy can achieve the full diversity order of K + 1. Simulation results are finally provided to validate our analytical results and the superiority of the new cooperation scheme.

We consider a relay-assisted non-orthogonal multiple access (NOMA) network consisting of a source (S), an energy-constrained relay (R), and two users, where a hybrid power-splitting (PS) and time-splitting (TS) scheme is applied at R for energy harvesting. We provide optimal transmission designs for such a network by jointly optimizing the transmit power of S, the TS and PS ratios, the power allocation ratios at S and R, and the user ordering (indicating which user should apply the successive interference cancellation). In particular, when full channel state information at the transmitters (CSIT) is available, our design can minimize the energy consumption while ensuring that two users correctly receive the desired information. When only partial CSIT is available, our design can minimize the system outage probability. We analytically show that the joint optimal solution for a given TS ratio can be derived in a closed form for both CSIT cases. The optimal TS ratio can be found either in a closed form or using a bisection method for the full CSIT case, while it can be found using a one-dimension search for the partial CSIT case. Finally, numerical results are provided to validate the analytical results and to evaluate the performance advantage of the hybrid PS/TS scheme with the proposed optimal designs.

In this study, a dual‐hop multiple‐input–multiple‐output relay network with the N th‐best relay selection scheme in the presence of co‐channel interference is studied. Specifically, the N th‐best relay is selected based on the channel state information (CSI) of the first hop. The authors first consider the CSI is perfect feedback and derive exact as well as asymptotic closed‐form expressions for the outage probability. Results reveal that the diversity order of N R × min{ N S ( K − N + 1), N D } is achieved when there is no feedback delay, where N S , N R and N D represent the number of antennas at the source, the relays and the destination, respectively, K is the number of the relays and N (1 ≤ N ≤ K ) represents the rank of relay chosen. Then, they investigate the outage performance of the system with feedback delay, and exact and asymptotic outage probability expressions are also obtained. Results illustrate that outdated CSI degrades the diversity order of the system to min{ N R , N D }, which is independent of the number of antennas at the source, the number of relays and the rank of the relay chosen. The findings of the study provide valuable insights into the practical system design.