This work employs intelligent reflecting surface (IRS) to enhance secure and covert communication performance. We formulate an optimization problem to jointly design both the reflection beamformer at IRS and transmit power at transmitter Alice in order to optimize the achievable secrecy rate at Bob subject to a covertness constraint. We first develop a Dinkelbach-based algorithm to achieve an upper bound performance and a high-quality solution. For reducing the overhead and computational complexity of the Dinkelbach-based scheme, we further conceive a low-complexity algorithm in which analytical expression for the IRS reflection beamforming is derived at each iteration. Examination result shows that the devised low-complexity algorithm is able to achieve similar secrecy rate performance as the Dinkelbach-based algorithm. Our examination also shows that introducing an IRS into the considered system can significantly improve the secure and covert communication performance relative to the scheme without IRS.
Intelligent Reflecting Surface (IRS), with the potential capability to reconstruct the electromagnetic propagation environment, evolves a new IRS-assisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage. However, when multiple IRSs are involved, accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead. Besides the cross-interference caused by massive reflecting paths, it is hard to obtain the close-formed solution for the optimization of covert communications. On this basis, the paper improves a heterogeneous multi-agent deep deterministic policy gradient (MADDPG) approach for the joint active and passive beamforming (Joint A&P BF) optimization without the channel estimation, where the base station (BS) and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency (CSE) cooperatively. Thanks to the 'centralized training and distributed execution' feature of MADDPG, each agent can execute the active or passive beamforming independently based on its partial observation without referring to others. Numeral results demonstrate that the proposed deep reinforcement learning (DRL) approach could not only obtain a preferable CSE of legitimate users and a low detection of probability (LPD) of warden, but also alleviate the communication overhead and simplify the IRSs deployment.
In this paper, we study the covert performance of the downlink low earth orbit (LEO) satellite communication, where the unmanned aerial vehicle (UAV) is employed as a cooperative jammer. To maximize the covert rate of the LEO satellite transmission, a multi-objective problem is formulated to jointly optimize the UAV's jamming power and trajectory. For practical consideration, we assume that the UAV can only have partial environmental information, and can't know the detection threshold and exact location of the eavesdropper on the ground. To solve the multi-objective problem, we propose the data-driven generative adversarial network (DD-GAN) based method to optimize the power and trajectory of the UAV, in which the sample data is collected by using genetic algorithm (GA). Simulation results show that the jamming solution of UAV generated by DD-GAN can achieve an effective trade-off between covert rate and probability of detection errors when only limited prior information is obtained.
In recent years, deep learning has been gradually used in communication physical layer receivers and has achieved excellent performance. In this paper, we employ deep learning to establish covert communication systems, enabling the transmission of signals through high-power signals present in the prevailing environment while maintaining covertness, and propose a convolutional neural network (CNN) based model for covert communication receivers, namely DeepCCR. This model leverages CNN to execute the signal separation and recovery tasks commonly performed by traditional receivers. It enables the direct recovery of covert information from the received signal. The simulation results show that the proposed DeepCCR exhibits significant advantages in bit error rate (BER) compared to traditional receivers in the face of noise and multipath fading. We verify the covert performance of the covert method proposed in this paper using the maximum-minimum eigenvalue ratio-based method and the frequency domain entropy-based method. The results indicate that this method has excellent covert performance. We also evaluate the mutual influence between covert signals and opportunity signals, indicating that using opportunity signals as cover can cause certain performance losses to covert signals. When the interference-to-signal power ratio (ISR) is large, the impact of covert signals on opportunity signals is minimal.
Covert communication can conceal the existence of wireless transmission and thus has the ability to address information security transfer issue in many applications of the booming Internet of Things (IoT). However, the proliferation of sensing devices has generated massive amounts of data, which has increased the burden of covert communication. Considering the spatiotemporal correlation of data collection causing redundancy between data, eliminating duplicate data before transmission is beneficial for shortening transmission time, reducing the average received signal power of warden, and ultimately realizing covert communication. In this paper, we propose to apply delta compression technology in the gateway to reduce the amount of data generated by IoT devices, and then sent it to the cloud server. To this end, a cost model and evaluation method that is closer to the actual storage mode of computer systems is been constructed. Based on which, the delta version sequence obtained by existing delta compression algorithms is no longer compact, manifested by the still high cost. In this situation, we designed the correction scheme based on instructions merging (CSIM) correction to save costs by merging instructions. Firstly, the delta version sequence is divided into five categories and corresponding merge rules were derived. Then, for any COPY/ADD class delta compression algorithm, merge according to strict to relaxed to selection rules while generating instructions. Finally, a more cost-effective delta version sequence can be gained. The experimental results on random data show that the delta version sequences output by the CSIM corrected 1.5-pass and greedy algorithms have better performance in cost reducing.
In this work, we investigate the covert communication in cognitive radio (CR) networks with the existence of multiple cognitive jammers (CJs). Specifically, the secondary transmitter (ST) helps the primary transmitter (PT) to relay information to primary receiver (PR), as a reward, the ST can use PT's spectrum to transmit private information against the eavesdropper (Eve) under the help of one selected cognitive jammer (CJ). Meanwhile, we propose three jammer-selection schemes, namely, link-oriented jammer selection (LJS), min-max jammer selection (MMJS) and random jammer selection (RJS). For each scheme, we analyze the average covert throughput (ACT) and covert outage probability (COP). Our simulation results show that CJ is helpful to ST's covert communication, the expected minimum detection error probability and ACT can be significantly improved with the increase of false alarm of CJ. Moreover, the LJS scheme achieves best performance in ACT and COP, followed by RJS scheme, and MMJS scheme shows the worst performance.
Covert communication technology makes wireless communication more secure, but it also provides more opportunities for illegal users to transmit harmful information. In order to detect the illegal covert communication of the lawbreakers in real time for subsequent processing, this paper proposes a Gamma approximation-based detection method for multi-antenna covert communication systems. Specifically, the Gamma approximation property is used to calculate the miss detection rate and false alarm rate of the monitor firstly. Then the optimization problem to minimize the sum of the missed detection rate and the false alarm rate is proposed. The optimal detection threshold and the minimum error detection probability are solved according to the properties of the Lambert W function. Finally, simulation results are given to demonstrate the effectiveness of the proposed method.
With the increasing number of communication devices and the complexity of communication environments, unmanned aerial vehicles (UAV), due to their flexible deployment and convenient networking capabilities, have shown significant advantages in tasks such as high-density communication areas and emergency rescue within special communication scenarios. Considering the openness of air-to-ground wireless communication, it is more susceptible to eavesdropping attacks. As a result, the introduction of physical layer security (PLS) in UAV communication systems is crucial to safeguard the security of transmitted data. In this paper, we investigate the PLS issues in a UAV cooperative communication system operating in Nakagami-$m$ fading channels with the presence of friendly interference. It considers the effects of imperfect successive interference cancellation (iSIC) and power allocation coefficients on system performance based on non-orthogonal multiple access (NOMA) techniques. By deriving closed-form expressions for the outage probabilities at the receiving users and the intercept probability of UAV eavesdropper (U-EAV), the performance of the considered cooperative UAV-assisted NOMA relay system with the presence of friendly interference is evaluated.
With the rapid development of cloud computing, edge computing, and smart devices, computing power resources indicate a trend of ubiquitous deployment. The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect. To overcome these problems and improve network efficiency, a new network computing paradigm is proposed, i.e., Computing Power Network (CPN). Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly. In this survey, we make an exhaustive review on the state-of-the-art research efforts on computing power network. We first give an overview of computing power network, including definition, architecture, and advantages. Next, a comprehensive elaboration of issues on computing power modeling, information awareness and announcement, resource allocation, network forwarding, computing power transaction platform and resource orchestration platform is presented. The computing power network testbed is built and evaluated. The applications and use cases in computing power network are discussed. Then, the key enabling technologies for computing power network are introduced. Finally, open challenges and future research directions are presented as well.
