In low-altitude Internet of Things (IoT) systems, uncrewed aerial vehicles (UAVs) are increasingly deployed as agile and distributed platforms. In scenarios where infrastructure support is limited or unavailable, UAVs can form ad hoc networks that enable flexible and self-organizing communication, making them well-suited for delivering real-time edge intelligence. A key challenge in such UAV ad hoc networks lies in edge server placement to ensure low-latency data transmission while balancing the computation load among servers. In this paper, we provide insights into the design of UAV ad hoc network-assisted IoT systems by characterizing the trade-off between data transmission latency and computational load. To this end, we formulate a bi-objective optimization problem that jointly minimizes the worst-case transmission latency and the load imbalance, subject to constraints on edge UAV server selection and data assignment. To solve this problem, we propose a directed-evolution non-dominated sorting genetic algorithm (DNSGA) that removes conventional crossover operations and incorporates two problem-specific heuristics: (i) a K-means-based task assignment module to reduce latency, and (ii) an efficiency-driven load migration strategy to balance server-side workloads. Simulation results verify that the proposed DNSGA converges significantly faster than non-dominated sorting genetic algorithm II (NSGA-II), while providing a more diverse set of non-dominated solutions with up to 13.3% broader result range. Compared to multi-objective particle swarm optimization (MOPSO), DNSGA achieves up to 18.8% reduction in worst-case latency, and compared to NSGA-II, it reduces the load imbalance by up to 41.2%. These advantages highlight its effectiveness for high-efficiency UAV ad hoc network-assisted IoT systems.

We study the two-user broadcast channel with degraded message sets and derive second-order achievability rate regions. Specifically, the channel noises are not necessarily Gaussian and we use spherical codebooks for both users. The weak user with worse channel quality applies nearest neighbor decoding by treating the signal of the other user as interference. For the strong user with better channel quality, we consider two decoding schemes: successive interference cancellation (SIC) decoding and joint nearest neighbor (JNN) decoding. We adopt two performance criteria: separate error probabilities (SEP) and joint error probability (JEP). Under our analysis, SIC and JNN decoding share the same second-order achievable rate region despite the fact that JNN decoding often yields better performance in other multiterminal problems. Furthermore, we generalize our results to the case with quasi-static fading and show that the asymptotic notion of outage capacity region is an accurate performance measure even at finite blocklengths.

To satisfy diverse security requirements of ground users in unmanned aerial vehicle (UAV) networks, we propose a channel inversion power control (CIPC) aided multi-user collaborative secret and covert uplink transmission strategy for UAV secure communication. Specifically, using the non-orthogonal multiple access (NOMA) technology, multiple ground covert users named Carlo, hide their weak covert signals in the strong secret signal from a secret user named Bob, and transmit to the UAV named Alice. An adversary Willie attempts to eavesdrop Bob’s confidential message and detect whether Carlo is transmitting or not. To evaluate the link reliability and security of secret and covert transmissions, we first derive closed-form expressions of the secret connection probability (SCP), secrecy outage probability (SOP), covert connection probability (CCP), and detection error probability (DEP) under perfect channel state information while accounting for the uncertainty of the adversary’s noise power. We then further incorporate the legitimate-link channel uncertainty into the analysis and characterize its impact on the key performance metrics, particularly the average values of SCP, SOP, and CCP. To characterize the theoretical benchmark of the proposed transmission strategy, we investigate the performance in both rotary-wing and fixed-wing UAV scenarios. Particularly, in the rotary-wing UAV scenario, we formulate an optimization problem to maximize the average effective sum covert rate subject to constraints of SCP, SOP, DEP, CIPC parameter, ground user’s transmission power, and the UAV’s altitude. Subsequently, we provide an optimal and a sub-optimal solution to the optimization problem. In the fixed-wing UAV scenario, we formulate an optimization problem to maximize the average covert rate subject to the constraints of SCP, SOP, DEP, CIPC parameter, user scheduling, and the UAV’s flight parameters. Furthermore, using the successive convex approximation (SCA) method, we propose an alternating optimization (AO) algorithm to obtain a high-quality feasible solution. Finally, our results reveal the influence of key parameters on the system performance, analytically and numerically.

In recent years, multivariate time series forecasting (MTSF) has gained significant attention. And transformer-based models have showed strong performance. However, the quadratic complexity of attention mechanisms leads to inefficiency and high overhead. The Mamba state-space model offers a more efficient alternative with lower complexity and fewer parameters. However, its unilateral nature limits the effective capture of time and feature dimensions. To address this, we propose A Dual-stream Cross-time and Cross-feature with Mamba (DCCMamba) for MTSF. Specifically, we employ a dual-stream structure to extract different information from the time and feature dimensions. One stream encodes data from a feature perspective and passes it through a Mamba layer to capture cross-time information. The other stream encodes the data from the time perspective, decomposes it into trend and seasonal components, and captures cross-feature information using a Mamba layer. Finally, we fuse the cross-feature information and cross-time information to get the final result. Experiments on four public datasets demonstrate that DCCMamba achieves state-of-the-art performance.

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data; 2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model.

Model inversion and adversarial attacks in semantic communication pose risks, such as content leaks, alterations, and prediction inaccuracies, which threaten security and reliability. This paper introduces, from an attacker's viewpoint, a novel framework called RepObE (Representation Learning-Enhanced Obfuscation Encryption Modular Semantic Task Framework) to secure semantic communication. This framework employs dynamic encryption during semantic extraction and feature transmission to hinder attackers from reconstructing data through eavesdropping, thus strengthening system privacy. To combat image communication task challenges, we propose a prototype adversarial collaborative alignment training approach enhanced by representation learning. This method extracts and encodes semantic features while using dynamic perturbation and robust optimization to improve system resilience against adversarial threats. The approach ensures reliable semantic communication in complex environments, maintaining performance while countering attacks using feature obfuscation, adversarial training, and representation learning. Experimental results demonstrate that our method surpasses existing techniques by more than 2% in resisting model inversion attacks on classification tasks. Visually, our method excels with minimal decipherable images for attackers. It also shows a 3% to 5% improvement in countering adversarial attacks on classification tasks.

Cell-free technology is considered a pivotal advancement for next-generation mobile communications, which can effectively enhance the quality of service for user equipments (UEs) located at the cell edge. For cell-free systems, in this paper, we propose a hybrid downlink transmission method that combines non-orthogonal multiple access (NOMA) and multiuser diversity (MUD). To evaluate the communication performance of the system, we derive closed-form expressions for both instantaneous and average sum rates of UEs using the NOMA and MUD transmission methods. Furthermore, we comprehensively investigate the spectrum efficiency of the NOMA and MUD transmission methods to provide a basis for selecting the hybrid transmission strategy. On the basis of the proposed hybrid transmission strategy, we can derive an optimal hybrid transmission strategy for the scenarios with two access points (APs) and two UEs. Particularly, we extend the aforementioned strategy to the scenarios with multiple UEs, and formulate an optimization problem to maximize the system spectrum efficiency subject to the transmission strategy and power allocation. Furthermore, we propose a low-complexity user selection strategy and power allocation algorithm to solve the problem. Numerical results demonstrate that the hybrid transmission method and power allocation strategy can achieve higher system spectrum efficiency. Our results reveal the influence of key parameters on the downlink spectrum efficiency, analytically and numerically.

Motivated by diverse secure requirements of multi-user in uncrewed aerial vehicle (UAV) systems, we propose a collaborative secret and covert transmission method for multi-antenna ground users to UAV communications. Specifically, based on the power domain non-orthogonal multiple access (NOMA), two ground users with distinct security requirements, named Bob and Carlo, superimpose their signals and transmit the combined signal to the UAV named Alice. An adversary Willie attempts to simultaneously eavesdrop Bob’s confidential message and detect whether Carlo is transmitting or not. We derive close-form expressions of the secrecy connection probability (SCP) and the covert connection probability (CCP) to evaluate the link reliability for wiretap and covert transmissions, respectively. Furthermore, we bound the secrecy outage probability (SOP) from Bob to Alice and the detection error probability (DEP) of Willie to evaluate the link security for wiretap and covert transmissions, respectively. To characterize the theoretical benchmark of the above model, we formulate a weighted multi-objective optimization problem to maximize the average of secret and covert transmission rates subject to constraints SOP, DEP, the beamformers of Bob and Carlo, and UAV trajectory parameters. To solve the optimization problem, we propose an iterative optimization algorithm using successive convex approximation and block coordinate descent (SCA-BCD) methods. Our results reveal the influence of design parameters of the system on the wiretap and covert rates, analytically and numerically. In summary, our study fills the gaps in collaborative secret and covert transmission for multi-user multi-antenna uplink UAV communications and provides insights to construct such systems.

The detection of malicious macros has been a prominent focus of research. Previous approaches exhibit two notable shortcomings. Firstly, methods centered on document and macro code features often fall short in effectively countering targeted adversarial strategies. Secondly, detection techniques relying on deceptive information, such as visual and textual cues, although alleviating certain challenges, introduce a new vulnerability to adversarial machine learning techniques. In this paper, we present Collaborative Adaptive Feature Extraction method (CAFE), designed for robust detection based on deceptive information. The core of CAFE is a feature fusion network architecture, where modality-shared associations and modalityprivate information are modeled from feature of different modalities, resulting in independently valid and comprehensive feature representations. An adaptive feature sampling module is introduced to address partial feature absence, enhancing detection robustness. Experimental results, conducted on two datasets, demonstrate that CAFE adeptly captures shared and complementary information from two modalities, showcasing its capability for robust malicious macro detection in the presence of input noise and adversarial samples. Index Terms—Malicious Macro Detection, Multi-modal Features, Model Robustness, Security Wen Wang is corresponding author.

Covert communication, also known as communication with low probability of detection, aims to provide reliable communication for legal users and prevent any other user from detecting the occurrence of legal communication. Motivated by the strong need of security links of the next generation communication systems, we study covert communication with millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) hybrid beamforming. Consistent with existing studies on covert communication, we use the Kullback-Leibler (KL) divergence and the total variation (TV) distance as the covertness measure. Under both covertness measures, for block fading channels, we derive the covert transmission rate with and without artificial noise. These results are obtained by optimizing the transmit power and the jamming power to satisfy the covertness constraints and to maximize the transmission rate. Specifically, when artificial noise is allowed, we show that there exists an optimal jamming power to achieve the covert transmission rate given the transmit signal power. Furthermore, we propose a metric to measure the inherent sparsity of the mmWave massive MIMO channel in the spatial domain, and study its effect on the covertness measures and the corresponding covert transmission rates. Our results provide insights and benchmarks for the design of practical covert communication systems with mmWave massive MIMO.