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.

Public communication links can serve as shelters to facilitate covert transmissions. To improve the covertness performance, we regard multiple multi-antenna public users as friends and leverage their abundant spatial degrees of freedom (SDoF) to resist illegal surveillance. However, the resulting complex directional interference introduces challenges in measuring the covertness and optimizing the beamformers. Accordingly, this study investigates a covert communication system where multiple public user pairs coexist with one covert user pair in multiple-input multiple-output interference channels. Specifically, a covert rate maximization problem through joint transceiver beamforming is formulated, which is difficult to solve as it is a non-convex problem. To address this, a centralized algorithm is first developed based on the successive convex approximation method that transforms the problem into a convex optimization framework. Additionally, to alleviate the backhaul overhead, we develop a decentralized solution utilizing primal decomposition to decouple the interference management and the covertness metrics at each transmitter, relying only on local channel state information. Simulations demonstrate that our proposed algorithms fully exploit the SDoF jointly provided by the spatial distribution of public users and the employment of multi-antenna technology. Therefore, our algorithms achieve superior performance over baselines that do not effectively leverage these spatial resources.

This paper investigates an integrated sensing and communication (ISAC) system with finite constellations. Both the problems of performance loss caused by discrete inputs and the conflicting requirements on symbol distribution of sensing and communication (S&C) functionalities are considered. Towards this, a waveform design based on the probabilistic shaping (PS) scheme is proposed, where a base station sends unified signals to estimate a sensing target and communicate with a user simultaneously. The analytical closed-form expressions of the sensing estimation rate (SER) and communication ergodic rate for PS-based ISAC are derived to evaluate the performance of the S&C functionalities, which are found to be mutually influenced by both the probability mass function (PMF) and power scaling factor. Furthermore, the optimal PMF and scaling factor are obtained by the alternative optimization algorithm to maximize SER while preserving communication capability. Simulation results validate the efficiency of our proposed PSbased waveform compared with those of schemes based on other input distributions. Additionally, the performance limit of ISAC systems with finite constellations is numerically obtained, which characterizes the tradeoffs between S&C functionalities.

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.

As a vital resource for the Earth’s ecological environment, effective extraction of surface water has a profound impact on human livelihoods and socio-economic development. Currently, water indices based on satellite imagery are widely used for surface water extraction. However, their accuracy remains significantly limited in complex scenarios—such as urban areas with high- and low-reflectivity buildings and shadow interference, sediment-laden or eutrophic water bodies, and intricate water–land transition zones. These limitations typically manifest in the failure to detect smaller water bodies, unclear water edges, and the presence of surrounding noise. To address these challenges, this study introduces a novel and practical water-body index—the Harmonized Landsat-Sentinel Water Index (HLSWI)—based on the Harmonized Landsat and Sentinel-2 dataset. HLSWI integrates the complementary spectral characteristics of the Landsat OLI and Sentinel-2 MSI sensors and optimizes classification thresholds via the ISO-Data clustering algorithm. HLSWI’s effectiveness is assessed through comparisons with seven commonly adopted water indices across 14 study areas worldwide and three representative complex scenarios, covering various surface types, including wetlands, arid zones, and urban aquatic environments. Experimental findings indicate that HLSWI outperforms the other seven water indices, reducing total water extraction errors by an average of 1.18% to 5.55% and improving overall accuracy and the Kappa coefficient by 1.42%–3.63% and 0.02–0.05, respectively. Therefore, HLSWI serves as a simple yet effective tool for surface water detection using optical remote sensing imagery and provides essential technical support for water extraction in complex environments.

Covert communication is an important approach to ensure information security by hiding the transmission behavior. Space-domain-coding intelligent reflecting surface (SDC-IRS) can adjust the phase of the reflection signal for passive beamforming in angle domains, which is widely employed in covert communications. However, the gains by SDC-IRS vanish when the warder is proximal to the receiver in angle domains. To overcome this limitation, in this paper, the space-time-coding IRS (STC-IRS) is considered, which can adjust both the phase and the frequency of the reflection signal for passive beamforming in angle-distance domains. Specifically, system performance under STC-IRS and SDC-IRS is compared, revealing the essence that angle and distance discrimination for the receiver is achieved with STC-IRS. Further, to fully exploit STC-IRS, optimization problems are formulated to maximize the covert rate in both line-of-sight scenarios and Rician fading scenarios. To solve the above problems, penalty-based algorithms are proposed where the transmit power, the phase shift and the frequency shift at STC-IRS are optimized jointly with majorization-minimization and block successive upper bound minimization techniques. Considering more general and adverse cases, the proposed algorithms are also extended to the scenario with multiple warders. Simulation results demonstrate the superiority of the proposed scheme compared with other benchmarks. Especially, when the warder and the receiver overlap in angle domains, covert rates with STC-IRS exceed 3 bps by distance domain discrimination, whereas covert rates with SDC-IRS are less than 0.01 bps.

Integrated sensing and communication (ISAC) has been recognized as a key technology of the next-generation wireless networks. This paper focuses on the ISAC system with discrete signal inputs. Compared with the ISAC systems with continuous inputs in most prior works, it can provide useful insights for ISAC networks with digital modulation at the expense of compensating for the performance gap caused by the finite constellations. To tackle the above problem, a unified analytical framework is proposed to analyze and optimize the ISAC systems with finite constellations in this paper. Based on the analytical framework, both the communication ergodic rate and sensing estimation rate considering non-uniform constellations are derived to characterize the sensing and communication (S&C) performance and quantify the performance gap caused by discrete signals in ISAC networks. To compensate for the performance gap, the probabilistic shaping (PS)-based waveform design is formulated as a multi-objective optimization problem by jointly optimizing the discrete symbol distribution and the power scaling factor for achieving the Pareto-optimal S&C performance. Given the non-convexity of the above problem, an alternating algorithm is proposed to obtain the PS-based waveform. Moreover, the properties of the sensing-optimal and communication-optimal PS-based waveform are analyzed. It is proved that the sensing function prefers amplitude-deterministic signals, and the probabilities of outermost constellations are equal if they are distributed in a circularly symmetric manner. It is indicated that communication functionality requires quasi-Gaussian distribution, which is consistent with the results of our proposed algorithm. Numerical results characterize the tradeoff between S&C and validate the superiority of the proposed PS scheme in comparison with other input distributions.

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 the mobile Internet era, users can achieve social interaction and spontaneously form music communities through the music content, digital services and interactive functions provided on digital music platforms. Previous research on music communities has mainly focused on economic, cultural and social fields. However, music communities are complex and multidimensional, and qualitative analyses make it difficult to demonstrate the network structure and attributes of the communities comprehensively. Therefore, this paper uses social network analysis to study the “family” in the Chinese online music platform WeSing as the research object. The social network constructed by users sending flowers, gifts, and comments to each other within the community is studied through data statistics and analysis. The study found that the music communities organised and participated by users on digital music platforms are relatively loose social networks. However, within the community, driven by common music interests and recognition of the value of the community, users continue to gather and deepen their participation in the community around music events. Several different sub-groups have gradually formed within the community. This study also shows that digital music platforms are important in generating music communities and disseminating music culture.

Short-packet communications are applied to various scenarios where transmission covertness and reliability are crucial due to the open wireless medium and finite blocklength. Nonetheless, transmission covertness and reliability present a dilemma and significantly limit the throughput for short-packet communications. Intelligent reflection surface (IRS) is utilized in this paper to address this issue by enabling channel reconstruction, thereby releasing the fundamental limit between transmission covertness, reliability, latency (blocklength), and throughput. Specifically, to reveal the benefits brought by IRS, the achievable throughput without IRS is derived first in terms of the transmission covertness requirement, reliability requirement, and blocklength. Subsequently, the achievable throughput with IRS is derived, which releases the fundamental limit among the above-mentioned performance metrics. Besides, considering the phase uncertainty at IRS and limited channel estimation overhead, the cases where the warder/IRS knows instantaneous/statistical channel state information (CSI) of warder-related/legitimate links are all discussed, elucidating the impacts of CSI availability on the throughput. Furthermore, numerical results verify the accuracy of analytical results and demonstrate the benefits provided by IRS. Especially, the achievable covertness and reliability performance can be enhanced while the minimum required blocklength can be reduced by introducing IRS, which is crucial for short-packet communications.

Wireless short-packet communications pose challenges to the reliability and security of the transmission. Besides, the proactive warder compounds these challenges, who detects and interferes with the potential transmission. Thus, the tradeoff among reliability, covertness, latency (blocklength) and transmission rate is crucial, and efficient system design schemes are required for short-packet communications against the proactive warder. To address these issues, we investigate the reliable and covert performance of above systems. Specifically, detection error probabilities and their approximations are derived for cases where the warder knows the instantaneous/statistical channel state information. Besides, the decoding error probability is derived. The asymptotic relationship among the detection/decoding error probability, blocklength and transmission rate is established, revealing the non-trivial tradeoff between reliability and covertness performance. Furthermore, to maximize the effective throughput, an optimization framework is proposed under reliability and covertness constraints. Numerical results verify the tightness of the approximations and the feasibility of the optimization framework. Furthermore, it is shown that the proactive warder severely degrades the throughput compared to the passive one. And longer blocklength is always beneficial to improve the throughput for systems with optimized transmission rates, whereas the optimal blocklength is not necessarily the maximum one when transmission rates are fixed.

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.

Intelligent reflecting surface (IRS) is widely utilized in covert communications to enhance the system performance by passive beamforming. However, transmission covertness is seriously threatened when the warder is located on the line between IRS and the legitimate receiver. To overcome this limitation, the space-time-modulation IRS (STM-IRS) is considered in this paper, which can adjust both the phase and the frequency of the reflection signal to realize three-dimensional (3D) beam focusing. To maximize the signal-to-noise ratio at the receiver, an optimization problem is formulated by jointly adjusting the transmit power, the phase and the frequency shift at each reflection element. To solve above non-convex problem with highly coupled optimization variables, a penalty-based algorithm is proposed wherein the majorization-minimization technique and block successive upper bound minimization technique are adopted. Simulation results demonstrate that 3D beam focusing can be realized by STM-IRS to distinguish the warder and the legitimate receiver in both angle and range domains. Besides, the proposed scheme outperforms the scheme without frequency shift and other existing frequency shift schemes.

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.

Integrated sensing and communication (ISAC) technique has recently gained significant research interest by supporting dual functions by a unified hardware platform. Traditionally, the ISAC system deemed the sensing and communication functions restricted by each other. In this paper, we demonstrate that a mutual benefit between sensing and communication functions can be achieved for the millimeter-wave ISAC system by efficiently exploiting the common information shared by both functions. Specifically, the sensing estimation can potentially be utilized to improve beamforming accuracy and the communication echo signal can enhance the sensing estimation as well. To demonstrate the mutual performance gain, we evaluate the joint outer bounds of the communication and sensing estimation rates. First, we derive the ergodic communication rate when the communication beamforming is assisted by sensing estimation. The asymptotic characteristic is further explored when the number of antennas approaches to infinity. Then, the closed-form estimation rate is obtained in terms of the Cramér-Rao Bound when the sensing estimation is assisted by communication signals. Numerical results verify the superiority of the mutual benefit when the common information was efficiently exploited by the ISAC BS transceiver. And when the transmission power is equally allocated, there are 10% and 2% gains on the communication rate and the sensing estimation rate simultaneously.

Probabilistic shaping (PS) has been demonstrated as an efficient method to achieve the shaping gain and to approach the Shannon limit in additive white Gaussian noise channels. To narrow the shaping gap caused by the channel fluctuation and the finite blocklength, a novel PS strategy is designed for polar coded communication systems with finite blocklength in Rayleigh flat fading channels. The proposed system transmits the quadrature amplitude modulation symbols and demodulates the received signal through log-maximum-a-posteriori (Log-MAP) detection. The channel capacity is theoretically derived in terms of the mutual information considering the influence of PS and the finite blocklength. Moreover, to solve the non-convex problem that maximizes the channel capacity on the basis of mutual information, an alternating optimization algorithm is proposed. Finally, numerical results indicate that with the optimal probabilistic shaping scheme, the system has 1.7 dB and 0.45 dB gain on the block error rate and channel capacity of the uniform distribution, respectively.