Yan Huo

Low-altitude wireless networks (LAWNs) require reliable multi-user communication together with accurate range and velocity sensing. Communication and sensing share the same transmit power and spatial degrees of freedom (DoF), and therefore joint beamforming is required to coordinate multi-user spectral efficiency with delay-Doppler estimation accuracy. An adaptive multi-objective beamforming and power allocation framework is developed for a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) base station. Communication performance is measured by the achievable multi-user sum spectral efficiency. Sensing performance is characterized by the Cramér–Rao lower bounds (CRLBs) for delay and Doppler frequency. A dimensionless system effectiveness integrated metric (SEIM) combines the three normalized performance components. The beamforming problem is lifted to transmit covariance matrices and treated via semidefinite relaxation (SDR) and alternating successive convex approximation (SCA) under power and per-user signal-to-interference-plus-noise ratio (SINR) constraints. An entropy-regularized weight subproblem provides a closed-form softmax update, and a damping step couples the weight update with the covariance iterations. Numerical results characterize the communication–sensing tradeoff with respect to the transmit power, array size, user loading, SINR requirements, and objective weights.

In smart grids, real-time electricity data uploaded by smart meters may be analyzed by an attacker with other data analytics methods, which may expose users’ privacy. To ensure user privacy, differential privacy methods are often used to process data. However, these methods reduce the accuracy of the data results obtained by the center and lead to unavailability of the data. In this paper, we address this problem and propose a distributed differential privacy protection scheme. Two methods of data noise addition and data perturbation are fused and used in the protection scheme. Data accuracy is improved by optimizing the noise generation method. To address the problem of quantitatively balancing the users’ privacy needs with the central analytics needs, this paper describes the needs of both through mathematical definitions, i.e., data accuracy and data privacy, and proposes a privacy budget that balances data accuracy and privacy. The performance of the proposed scheme is evaluated using the typical power data, which proves the excellent performance.

In the realm of edge computing, the analog aggregation based Federated Learning Over the Air (FLOA) emerges as a promising technology, offering heightened communication efficiency and privacy provisions. This approach involves concurrent transmission and aggregation, where edge devices (workers) collectively upload their local updates to a Parameter Server (PS) through shared time-frequency resources. The PS then obtains averaged updates only but not the individual local ones, reducing latency and communication costs. However, this simultaneous process exposes FLOA to vulnerabilities, particularly Byzantine attacks. Addressing this concern, we introduce an innovative framework utilizing Unmanned Aerial Vehicles (UAVs) to assist in a heterogeneous FLOA. This framework mitigates the impact of Byzantine attacks while preserving the advantages of over-the-air computation for efficient federated learning. The UAVs engage in over-the-air computation, collecting gradients from local workers and aggregating them. Subsequently, the UAVs transmit these aggregated gradients to the PS in different time slots. Robust aggregation techniques are applied at the PS to combine updates from UAVs. To enhance the robustness of over-the-air transmissions from workers to UAVs, we propose a power control policy. The resilience of the proposed framework to attacks is demonstrated through the derived expected convergence rate, validated by experiments on real datasets.

Federated Learning (FL) in the Internet of Vehicles (IoVs) facilitates vehicle collaboration while ensuring user privacy. However, FL’s reliance on communication resources presents significant challenges in IoV environments, where limited resources and rapid vehicle mobility hinder deployment. In addition, the presence of malicious nodes further complicates these issues. To address these challenges, we introduce a client selection algorithm based on Reservoir Computing (RC) to improve both client selection reliability and communication efficiency. In particular, we first develop a trust value calculation method to address unreliable communication behaviors and model transmissions from malicious vehicles. We define an optimization criterion, “Federated Model Satisfaction,” which comprehensively evaluates client drift and trustworthiness in IoV-based FL. Next, the problem of maximizing federated model satisfaction is formulated as a Partially Observable Markov Decision Process (POMDP) that incorporates IoV-specific characteristics. We also propose a novel algorithm, MD-DESQN, which integrates the Echo State Network (ESN) and the Double Deep Q-Network (DDQN) for client selection and bandwidth allocation. This algorithm considers vehicle mobility, trust values, and communication conditions, enhancing both communication robustness and FL security. Simulation results demonstrate that the proposed trust value calculation method effectively integrates historical information and current behaviors, providing reliable guidance for client selection. The algorithm demonstrates superior efficiency and effectiveness compared to traditional methods across various scenarios with varying ratios of malicious vehicles and system sizes.

In 6G networks, Integrated Sensing and Communication (ISAC) is a key technology owing to its capability in enhancing spectral efficiency. Yet, in urban wireless environments, characterized by persistent random interference, maintaining stringent environmental prerequisites is infeasible. This bottleneck renders robust sensing under nonideal conditions difficultly. In this article, we propose a paradigm for multi-Base stations (BSs) cooperative sensing. In this paradigm, we simultaneously consider both reflected and scattered signals during the target uncrewed aerial vehicle (UAV) movement to improve sensing accuracy. As for the multipath signals arising from such paradigm, we have developed a mathematical model for the echo signals received at the ISAC BSs and illustrated how channel time-variability affects the receiving signals. Based on analysis for the time-varying nature of the real channel environment, we introduce a range power gain algorithm based on fuzzy positioning and name it Fuzzy Range Enhancement-Transmission Spatial Gain (FRE-TSG) inspired by Linearly Constrained Minimum Variance (LCMV). Furthermore, to robustly use multiple sensing results, we design a result-level fusion method Data-Driven-Maximum Consensus ISAC (2-D-MaCeS) matching algorithm. This algorithm fully exploits the spatiotemporal diversity of multiple sensing results and reduces dependence on prior information. Finally, we validate the efficacy and robustness of the proposed algorithm through extensive simulation experiments.

While physical layer authentication can mitigate wireless channel vulnerabilities, its reliability is often compromised by inherent noise and variability of observed physical layer attributes. As a solution, collaborative physical layer authentication (CPLA) introduces multiple nodes to enhance performance, but incurs additional computational and communication costs for collaborators. Without incentive, desired collaborators may act selfishly and withdraw, and involving unreliable collaborators could degrade performance. Therefore, this paper proposes an incentive mechanism with a new centralized governance approach to coordinate CPLA, engaging reliable collaborators to optimize authentication accuracy. Specifically, we model the interaction between the center and collaborators as a Stackelberg game to establish. To reduce redundant computations in equilibrium solving, we first construct a candidate pool containing potential trainable combinations. Subsequently, we design incentive and training schemes for each candidate combination. Moreover, a quality-driven combination selection scheme is proposed to maximize incentive effectiveness. Based on the candidate pool and strategies, it integrates a deep Q-network as collaborator quality manager and a combination-level evaluation module, and via “filter-then-verify” identifies optimal incentive targets with low complexity while improving authentication accuracy. Simulations demonstrate that the proposed scheme successfully incentivizes selfish collaborators and achieves 99% authentication accuracy in unreliable collaborative environments.