Covert communications have recently emerged as an innovative technology to enhance communication security and can utilize multiple antennas to improve system performance. Existing transmit precoding algorithms rely on the Gaussian inputs assumption, which is impractical in reality. Notably, conventional transmit precoding strategies for finite-alphabet inputs lack covertness considerations, limiting their effectiveness in ensuring covert communications. Therefore, based on finite-alphabet inputs, the practical transmit precoding for multiple-input multiple-output covert communication systems is investigated in this paper, where a warder equipped with multiple antennas aims to detect whether the transmission occurs. Specifically, an explicit expression for the outage probability of the legitimate communication is derived under imperfect channel state information. Then the covert throughput is formulated to measure transmission performance, while the Kullback-Leibler (KL) divergence is employed to evaluate covertness performance. Since both covert throughput and KL divergence are highly non-convex with respect to the transmit precoding matrix, maximizing covert throughput while satisfying the covertness constraint presents a significant challenge. To address the intractable optimization problem, an effective robust transmit precoding optimization algorithm based on deep reinforcement learning (DRL) is proposed to find the solution. Numerical results highlight the key differences between transmit precoding for Gaussian inputs and finite-alphabet inputs, while also demonstrating that the DRL algorithm outperforms other benchmark methods.

The existing works on intelligent reflecting surface (IRS) aided covert communications consider the Gaussian input, which is however infeasible in practical systems. Two core issues remain to be answered: 1) How much performance gain can be obtained by applying the IRS for covert communications with finite-alphabet inputs? 2) How to jointly design the highly coupled parameters (constellation distribution and reflection coefficients) to obtain the optimal performance? To address these issues, in this work, the performance of the IRS aided covert communications with finite-alphabet inputs is analyzed, and a joint optimization scheme is proposed. In particular, the channel cutoff rate (CR) and the lower bound of the average detection error probability at the warder are derived under fading channels. Further, the impact of the reflection coefficients on the performance is discussed to reveal the benefits brought by the IRS. In addition, a two-layer algorithm is proposed to maximize the channel CR, where a channel variance tuple is introduced to decouple the optimization variables (constellation distribution and reflection coefficients). Numerical simulation demonstrates the superiority of the proposed scheme over various benchmarks. Moreover, the stricter the covertness constraint, the more concentrated the optimal probability distribution is at central constellation points.

Intelligent reflecting surface (IRS) based symbiotic radio (SR) technology offers a promising approach to enhance Internet of Things (IoT) connectivity. Despite its potential, current simple implementations face significant security challenges that could undermine the reliability of IoT connectivity. To tackle this security issue, this paper introduces a novel model for covert SR transmission that employs joint passive and active beamforming strategies to counteract risks from multiple warders. In this model, the IRS acts as a secondary transmitter, backscattering binary phase shift keying signals. The system is categorized into two operational modes: parasitic SR (PSR) and commensal SR (CSR), based on the synchronization requirements between primary and secondary signals. A comprehensive analysis of the bit error rate for each mode is conducted, and the Kullback-Leibler divergence is leveraged to measure system covertness, establishing a tractable covertness constraint for subsequent optimization. To address the effects of imperfect channel state information on beamforming performance, a robust joint beamforming algorithm is proposed. Our results show that this algorithm outperforms existing baselines. Particularly in CSR mode, high reliability and strong covertness can be achieved with a minimal number of IRS reflecting elements, providing a cost-effective solution for communication security in IoT applications.

The intelligent reflecting surface (IRS) has been widely applied in covert communications to hide the transmission behavior. However, the existing IRS relies on grid/battery power for its operation, which is unprocurable for practical covert communication applications. To address this issue, a novel multi-functional IRS (MF-IRS) is proposed for harvesting energy, signal reflecting, and amplifying simultaneously, where each element can flexibly switch between energy harvesting mode and passive/active reflection modes. To reveal the benefit of the MF-IRS for covert communications against multiple warders, the critical performance is analyzed, and effective design schemes are also provided. In particular, the detection error probabilities at warders are derived when the warders are non-collusive/collusive. In addition, the covert rate maximization problem is formulated by jointly optimizing the beamforming vector, element allocation matrices, and the reflection coefficient matrix. To solve this non-convex problem with highly coupled variables, an efficient successive convex approximation-based algorithm is proposed for the non-collusive scenario first and then extended to the collusive scenario. Simulation results demonstrate that the proposed MF-IRS always outperforms the self-sustainable passive/active IRSs, and it even outperforms the full-passive/active IRSs powered by grid/battery when the IRS is located near the signal source.

In this paper, the covert communication is considered with a full-duplex (FD) receiver under covertness and reliability constraints in the finite blocklength regime. Conventional covert communication systems are restricted by the square root law. To break through this limitation, an FD receiver is introduced, generating artificial noise (AN) to confuse the warder’s detection and achieving positive covert rates. To investigate the tradeoff among covertness, reliability, blocklength and throughput, the expressions of the warder’s detection error probability and the receiver’s decoding error probability in the finite blocklength regime are derived. Moreover, the asymptotic throughput is analyzed when the maximum transmit power of the FD receiver approaches infinity. Through the asymptotic analysis, we find that the blocklength’s impact on the covertness performance vanishes, which is a reason for achieving positive covert rates. However, the AN will also cause harmful residual self-interference to the receiver for which the FD receiver should be carefully designed by making a tradeoff between the covertness and the effective throughput. Hence, an optimization problem is formulated and solved to maximize the covert throughput. Numerical results show that the proposed scheme is effective in the finite blocklength regime where positive covert rates can be obtained.

Covert communication is crucial to ensure the safety of wireless communications in Internet of Things (IoT) systems. In this article, a multiantenna relay is employed to enhance the communication link and avoid the transmission being detected by the multiantenna warder simultaneously. Considering the dynamic fluctuating fading channels of IoT systems, a novel adaptive coordinated direct and relay transmission (ACDRT) scheme is proposed where the relay switches on/off adaptively to maximize the achievable covert rate. The covertness constraint requirements are derived with instantaneous and statistical warder-related channel state information based on the availability of the channel information in practical systems. Since both the direct and the relay links impact the system performance, the optimization problem is formulated, where the beamforming vectors are coupled. A semidefinite relaxation-based line search method is proposed to address this problem. Besides, the globally optimal solutions can be obtained by the proposed method, which is rigorously proved mathematically. In addition, the conditions for achieving a positive covert rate are analyzed with the multiantenna warder. Simulations demonstrate that the performance of the ACDRT is robust to covertness requirements when the positive rate condition holds, and significant covert rate gains can be obtained by the ACDRT with the multiantenna relay compared with the conventional systems.