Reinforcement learning (RL) has emerged as a promising anti-jamming solution in frequency-hopping (FH) communication systems, owing to its dynamic anti-jamming capability independent of FH patterns. Nevertheless, conventional RL-based methods may require extensive retraining when previously encountered jamming signals reappear after a long period because of the obliviousness of RL. To address this issue, this paper proposes a memory-driven anti-jamming approach for FH communications. Inspired by reinforced continual learning (RCL), the proposed approach employs an adaptive expansion mechanism of sub-deep neural networks to efficiently capture and retain jamming patterns. Simulation results demonstrate that the proposed approach can make more rapid anti-jamming decisions compared to conventional methods when encountering previously observed jamming patterns.

Ambient backscatter communication (AmBC) is a crucial technology for addressing the energy consumption challenge in green Internet of Things through the reflection or absorption of surrounding radio frequency (RF) signals. Nevertheless, it grapples with the intricacies of ambient RF signal and the round-trip path loss. For the traditional detectors, the incorporation of pilot sequences results in the reduction in spectral efficiency. Furthermore, traditional energy-based detectors are inherently susceptible to a notable error floor issue, attributed to the co-channel direct link interference (DLI). Consequently, this paper proposes a blind symbol detector without the prior knowledge concerning the channel state information, signal variance, and noise variance. Leveraging the intra-symbol differential amplitude shift keying (IDASK) scheme, this detector effectively redirects the majority of DLI energy towards the largest eigenvalue of the received sample covariance matrix, thereby utilizing the second largest eigenvalue for efficient symbol detection. Simulation results demonstrate that the proposed blind detector exhibits a significant enhancement in symbol detection performance compared to traditional counterparts.

Users’ connection is pivotal for advancing meta-computing in Industrial Internet of Things (IIoT), where efficient data transmission can help overcome the barriers of computing resources distributed among billions of devices from diverse IIoT networks. Cooperative device-to-device (D2D) transmission, which is underlaid with cellular networks, plays an important role in enhancing the users’ connections for IIoT. However, since serving as a content provider in cooperative transmission needs consuming resources, IoT devices are typically willing to participate in cooperative transmission only when the content requesters are within the same IIoT networks. To encourage the cooperation across different IIoT networks, we in this article introduce social credit as a universal virtual concurrency to stimulate users’ willingness to assist content requesters via D2D transmission. Specifically, we first establish the relationship between social credit and data transmission, and model the process of credit acquisition and expenditure using a queue, which is initially unstable. Then, the inversely queuing technique is adopted to construct an equivalent stable queue, and a statistical credit guarantee mechanism is proposed to maintain cooperative transmission. Based on this mechanism, we explore the throughput maximization problem for cooperative D2D transmission in IIoT underlaying cellular networks, considering constraints on average and peak transmit power as well as interference to cellular communications, for obtaining the optimal credit-aware power control scheme. When multiple content providers are available, we prioritize the provider located at the shortest distance and examine the corresponding credit-aware power minimization problem, where the optimal power control scheme is obtained. Simulation results demonstrate that the proposal offers more stable credit guarantees than baseline methods, encouraging greater participation in cooperative transmission while achieving higher throughput and reducing transmit power consumption.

We propose a robust transceiver design for a covert integrated sensing and communications (ISAC) system with imperfect channel state information (CSI). Considering both bounded and probabilistic CSI error models, we formulate worst-case and outage-constrained robust optimization problems of joint transceiver beamforming and radar waveform design to balance the radar performance of multiple targets while ensuring the communications performance and covertness of the system. The optimization problems are challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and the coupled transceiver variables. In an effort to tackle the former difficulty, S-procedure and Bernstein-type inequality are introduced for converting the SICs into finite convex linear matrix inequalities (LMIs) and second-order cone constraints. A robust alternating optimization framework referred to alternating double-checking is developed for decoupling the transceiver design problem into feasibility-checking transmitter- and receiver-side subproblems, transforming the rank-one constraints into a set of LMIs, and verifying the feasibility of beamforming by invoking the matrix-lifting scheme. Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm in improving the performance of covert ISAC systems.

In this paper, we study the beamforming schemes via the novel frequency diverse array (FDA) on enhancing covert communications performance. We first consider the ideal scenario where the channel state information (CSI) is perfectly known at the transmitter. Then we characterize the key role of minimizing the correlation of the communication and detection channels in boosting the covertness of the system, which also provides a theoretical design principle for FDA-specific carrier frequency scheduling. By exploiting the optimization framework block successive upper bound minimization (BSUM), we propose a frequency scheduling method which leads to a two-phase beamforming scheme to facilitate the covert transmission. Subsequently, we extend the scenario to the more practical one with partial CSI. By leveraging the convex hull, we manage to transform the formulated semi-infinite programming problem to an equivalent semi-definite one which can be solved optimally. In addition, we present a process to construct the optimal beamforming vector. To mitigate the channel correlations in this scenario, we generalize the steps of BSUM and propose an algorithm to schedule the frequencies efficiently. Afterwards, a three-phase robust beamforming scheme is summarized, which boosts the covert rate significantly. Numerical results are provided to demonstrate the superiority of the proposed schemes.

In this paper, we investigate multi-antenna covert communications, where the transmitter Alice equips multiple antennas, the legitimate receiver Bob and the eavesdropper Willie equips single antenna. Through analyzing the detection performance and the covert rate, we prove that a positive covert rate is achievable with a certain number of antennas in Alice. Numerical results are shown to illustrate the correctness of our results and the performance of MISO covert communication.

This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the UAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. In the considered system, the UAV’s trajectory is critical to the covert communication performance, since the AN transmitted by the UAV also generates interference to Bob. To maximize the system performance, we formulate an optimization problem to jointly design the UAV’s trajectory and Alice’s transmit power. The formulated optimization problem is non-convex and is normally solved by a conventional iterative (CI) method, which requires multiple approximations based on Taylor expansions and an initialization on the UAV’s trajectory. In order to eliminate these requirements, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. By analyzing the covertness constraint, the GM method decouples the joint optimization into optimizing the UAV’s trajectory and Alice’s transmit power separately. Our examination shows that the GM method can significantly outperform the CI method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the CI method.

In this paper, to obtain a higher information rate in the block fading channel, we propose an LDL-decomposition-based linear precoded faster-than-Nyquist (FTN) signaling with power allocation. We further propose an LDL-decomposition-based linear precoded FTN signaling with truncated power allocation (LDL-TPA-FTN) to extend the application range of the acceleration factor. Moreover, we derive the corresponding maximum average information rate, outage probability, and outage capacity of the proposed schemes. Also, the complexity of the proposed schemes is analyzed theoretically and compared with classic ones, which shows the proposed schemes have lower complexity. Numerical results show that the proposed schemes have higher outage capacity and achievable rate than Nyquist signaling in the block fading channel. Also, the proposed schemes can reduce the incurred inter-symbol interference (ISI) and have the same application range of the acceleration factors as the singular value decomposition-based FTN. Moreover, a trade-off should be made between the side-lobe suppression of the power spectral density of the transmit signal and threshold selection in LDL-TPA-FTN to obtain higher performance gain for small acceleration factors.

This paper considers the problem of covert communication over a multiple-input single-output channel (MISO) in the case of the imperfect channel state information. The transmitter wants to communication with the receiver while the eavesdropper detects the existence of the transmission. We first study the scenario that the accurate channel state information (CSI) is known and show that the condition achieving the positive rate by the beamforming vector. However, because of the non-cooperation between the transmitter and the eavesdropper, we consider the CSI is imperfect. There are estimation errors for the transmitter. When the positive rate can not be achieved, the objective is to maximize the communication rate. We discuss the deterministic bounded CSI errors for the covert communication. Robust beamforming and the transmitter power is investigated, which can be transformed as a second order cone programming (SCOP) problem. We consider the line-of-sight (LoS) channel and the linear array antenna model. Comparing with the perfect CSI, the results show that the strategy of the transmitter is more conservative under the imperfect CSI. The capacity declines by the estimation errors.