Unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) systems are envisioned to simultaneously provide secure data transmission and nighttime illumination. However, when buildings obstruct the optical links, both connectivity and lighting are disrupted, which may severely compromise system reliability and safety. This paper investigates an artificial noise-based physical layer security (PLS) scheme for a VLC-enabled UAV communication system in a multiuser environment with potential eavesdroppers, while explicitly incorporating awareness of shadowed area caused by blockage and enabling the UAV to autonomously adjust its trajectory to proactively avoid such shadow coverage to the ground users. We formulate a joint optimization problem of user association, power allocation, and UAV trajectory design to maximize the average secrecy rate of the system, while taking into account illumination requirements, shadowing effects, and UAV mobility. To tackle this mixed-integer and non-convex optimization problem, we decompose it into three subproblems and transform them into tractable convex forms. Furthermore, we also develop an iterative algorithm by leveraging successive convex approximation techniques under a block coordinate descent framework to efficiently obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can achieve fast convergence and improve the average secrecy rate at least by 51.1% compared with conventional schemes. Moreover, the algorithm still exhibits robustness and efficacy in exploiting the spatial-temporal trade-offs under severe eavesdropping threats and shadowing with diverse user geometries, highlighting its practicality for secure nighttime urban VLC-UAV communication.

This paper proposes a novel covert transmission framework for an unmanned aerial vehicle (UAV)-reconfigurable holographic surface (RHS)-aided full-duplex (FD) integrated sensing and communication (ISAC) system, where the aerial access point (AP) simultaneously performs target sensing and downlink covert communication. We jointly design the AP’s downlink transmit signal and uplink receive beamformers, the RHS weights, the users’ uplink transmit powers, and the UAV’s trajectory, considering imperfect knowledge of the warden’s channel state information (CSI). An optimization problem is formulated to maximize the minimum covert transmission rate (CTR) among all downlink covert users (DCUs), subject to constraints on required sensing and uplink transmission capabilities, covertness, and total power budget. To tackle the intractable non-convex problem, we leverage the Bernstein-type inequality, majorization-minimization (MM), and successive convex approximation (SCA), and propose a secure optimization framework that efficiently updates all variables using convex optimization techniques. To further understand the proposed algorithm, its convergence behavior and computational complexity are discussed. Simulation results demonstrate that integrating RHS and UAV techniques into the optimization design enhances the covert transmission performance of FD-ISAC systems while ensuring a certain level of sensing capability.

An optical reconfigurable intelligent surface (ORIS) was expected to offer extra secrecy performance gain in a visible light communication (VLC) system. However, nonlinear impairments involved degrade the confidential signal reception and have not been fully considered in designing physical layer security (PLS). In this paper, a novel PLS approach is proposed for an ORIS-aided VLC system with consideration of practical nonlinear impairments. It is mathematically formulated to be an optimization problem that maximizes the signal-to-interference-plus-distortion-and-noise ratio of the legitimate link, while entirely suppressing that of multiple eavesdroppers by jointly optimizing the beamforming, jamming and clipping at the transmitters, and also the surface configuration in terms of mirror assignments and rotation angles at the ORIS. We decompose this mixed combinatorial and non-convex optimization problem into three sub-problems and elaborately transform them to be conventional convex programming, quadratic programming and nonlinear programming problems, respectively. Moreover, we also develop a time-efficient iterative approach to achieve the suboptimal solution with low-computational complexity. Simulation results demonstrate the improvement of secrecy performance as compared with conventional schemes, and also the robustness to severe nonlinear impairments and spatial correlation, thereby confirming the beneficial insights of this methodology for secure VLC with nonlinear devices.

This paper investigates an energy-efficient beamforming design for a hardware-impaired simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided integrated sensing and communication (ISAC) system, where the base station (BS) concurrently performs target sensing and multi-user communication. Accounting for hardware impairments (HWIs) at the BS, user equipments (UEs) and STAR-RIS, a joint optimization problem is posed to maximize the system energy efficiency, subject to constraints on required sensing and transmission capabilities, and the total power budget. To tackle the intractable conflicts among sensing and transmission metrics introduced by HWIs, we propose a novel learning-based method that integrates a denoising diffusion probabilistic model (DDPM) into a twin-delayed deep deterministic policy gradient (TD3) algorithm enhanced with prioritized experience replay (PER). By leveraging the DDPM and PER for beamforming policy determination, our approach accurately models the complex dynamics, achieving a better balance between sensing and communication performance. Simulation results demonstrate that the proposed PER-DDPM-TD3-based beamforming strategy achieves a 69.3% higher energy efficiency performance than the existing deep reinforcement learning (DRL)-based method.

Hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks (SUTNs) can provide maritime users with ubiquitous communication services. However, eavesdropping poses a significant challenge to the secure communications of SUTNs due to their wide-area coverage. In this paper, we propose a novel secure scheme for maritime communications, where a terrestrial-UAV integrated network coexists with marine satellite (MS) systems in the presence of an eavesdropper (Eve). Considering imperfect channel state information (CSI) for both the MS and Eve, we focus on the collaborative design of beamforming for the terrestrial base station (TBS), UAV, and MS, as well as the UAV’s trajectory. A robust optimization problem is formulated to maximize the worst-case secrecy rate, subject to constraints on worst-case communication quality for each user, UAV locations, and TBS backhaul throughput. To tackle this intractable non-convex problem, we leverage the S-procedure, general sign-definiteness, and successive convex approximation (SCA) to propose a security solution that efficiently optimizes all variables using convex optimization techniques. Numerical results validate the effectiveness of the proposed solution, illustrating the impact of CSI errors and the secure performance enhancements achieved through joint trajectory and beamforming optimization.

This paper investigates joint transmit beamforming and trajectory optimization techniques for an unmanned aerial vehicle (UAV)-enabled covert full-duplex (FD) integrated sensing and communication (ISAC) systems with hardware impairments (HWIs), where the aerial access point (AP) transmits and receives sensing signals while the integrated communication operates in either downlink or uplink. We jointly optimize the downlink transmit signal and the uplink receive beamformers at the AP, the transmit power at the uplink users and the trajectory of the UAV. An optimization problem is formulated for maximizing the minimum covert transmission rate (CTR) among all covert users (CUs) subject to the constraints of the required sensing and uplink transmission capabilities, system covertness, total power budget. To tackle the intractable non-convex problem, we leverage majorization-minimization (MM) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Numerical results demonstrate the effectiveness of the proposed method in balancing the trade-off between covert communication and sensing performance, highlighting the UAV’s potential in adaptive ISAC deployment.

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known channel state information (CSI) for the eavesdropping target, we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the discrete reflection coefficients (DRC) of the multiple hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

User-centric (UC) philosophy is a promising network formation method in light emitting diode enabled visible light communication (VLC) systems. Nevertheless, the nonlinear channel impairments restrict the overall system performance and have not been fully considered in the association structure designing. In this paper, an adaptive user association approach within the UC-cells formation of dense VLC networks is investigated under the consideration of practical nonlinear impairments and adjacent interference. It is mathematically formulated to be an achievable data rate maximization problem by coordinately determining the optimal candidates of access point, clipping ratio and information-carrying power. We divide this mixed combinatorial and non-convex optimization problem into two subproblems and delicately transform them to be binary nonlinear programming and constrained linear programming problems, respectively. In addition, we develop an efficient approach to obtain the local optimal solution with low-computational complexity in an alternating iterative way. Simulation results demonstrate that the proposed scheme has relatively fast convergence and shows robustness to the variation of complex interference patterns and nonlinear impairments. Moreover, it can achieve significant throughput gain as compared with the conventional schemes, demonstrating the prospect and validity of this methodology for dense VLC networks with actual nonlinear devices.

Several prior studies have suggested that word frequency biases can cause the Bert model to learn indistinguishable sentence embeddings. Contrastive learning schemes such as SimCSE and ConSERT have already been adopted successfully in unsupervised sentence embedding to improve the quality of embeddings by reducing this bias. However, these methods still introduce new biases such as sentence length bias and false negative sample bias, that hinders model's ability to learn more fine-grained semantics. In this paper, we reexamine the challenges of contrastive sentence embedding learning from a debiasing perspective and argue that effectively eliminating the influence of various biases is crucial for learning high-quality sentence embeddings. We think all those biases are introduced by simple rules for constructing training data in contrastive learning and the key for contrastive learning sentence embedding is to "mimic" the distribution of training data in supervised machine learning in unsupervised way. We propose a novel contrastive framework for sentence embedding, termed DebCSE, which can eliminate the impact of these biases by an inverse propensity weighted sampling method to select high-quality positive and negative pairs according to both the surface and semantic similarity between sentences. Extensive experiments on semantic textual similarity (STS) benchmarks reveal that DebCSE significantly outperforms the latest state-of-the-art models with an average Spearman's correlation coefficient of 80.33% on BERTbase.

In MIMO multi-user networks, inter-user interference (IUI) significantly affects the system performance. To handle this problem, this paper proposes the reconfigurable intelligent surface assisted cooperative interference alignment scheme (RIS-CIA). The core idea of this work is that the base station and full-duplex users jointly design space-time precoding matrices, which can reduce the dimension of the interference space on the user side. Besides, the additional interference caused by the information exchange process is split into sub-blocks by space-time precoding, then eliminated by interference nulling assisting by the passive RIS. The simulation results show that the RIS-CIA scheme with few numbers of elements obtains higher DoF than that of benchmark schemes with a huge number of elements.

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.

Multiple-input multiple-output (MIMO) radar design for extended targets in a spectrally crowded environment is a challenge owing to the high sensitivity of the target impulse response (TIR) and the increasing requests for spectrum. Assuming unknown TIR, this paper proposes a joint design method to optimize the transmit waveforms and receive filter bank in MIMO structure ensuring spectral compatibility with the overlayed radiators. A priori information is used to impose a spectral constraint on the waveforms, which is the result of a non-convex optimization problem aimed at enhancing the average signal-to-interference-plus-noise-ratio (SINR) over a finite uncertainty set for the TIR. The new method realizes an improved spectral cohabitation with the surrounding radiators through an appropriate modulation of the transmitted energy. In addition, we develop an iterative optimization algorithm which successively enhances the average SINR. Each iteration of the algorithm involves a hidden convex problem, which can be solved resorting to the rank-one decomposition procedure. Finally, the performance is assessed by studying the trade-off among the achieved SINR and spectral shape. The reported results are presented to analyze the performance of the devised method against several counterparts in terms of the SINR value and robustness.

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies.

Deep clipping is beneficial for the optical orthogonal frequency division multiplexing (O‐OFDM) system, since it can lower the peak‐to‐average power ratio, reduce the direct current requirement in light emitting diodes (LEDs), and relax the bit‐resolution requirement in digital‐to‐analogue converters (DACs). However, it is accompanied by more signal distortions. In this study, a deep clipping noise mitigation scheme using iterative shrinkage/thresholding algorithm (ISTA) with three steps is proposed to improve bit error rate (BER) performance of the LED‐based DCO‐OFDM systems. In the first step, the estimated observation interference is eliminated from the received symbols to minimise the negative effect of channel noise. In the second step, two strategies are presented to generate the specified observations thus reduce the component of measurement noise in the whole observation vector. In the last step, combining the generalised cross validation and the estimation of observation interference, the appropriate regularisation parameter are calculated for ISTA to improve the robustness of the sparse recovery performance. They use simulations to show that the proposed scheme can correct the deep clipping noise with favourable reconstruction quality, which significantly improves the BER performance and therefore assist the LED non‐linearity mitigation.