Xiaohui Lin

Secure communication is essential in UAV-assisted wireless networks, where the broadcast nature of air-to-ground channels makes legitimate transmissions vulnerable to eavesdropping. Physical layer security (PLS) provides an effective means to mitigate this threat, with secrecy rate commonly adopted as the performance metric. However, practical engineering requirements may further require the eavesdropper’s signal-to-interference-plus-noise ratio (SINR) to remain below a prescribed threshold to prevent reliable decoding. The resulting optimization problem is challenging because both the secrecy-rate objective and the eavesdropper-SINR constraint depend on the unknown locations of passive eavesdroppers, while conventional reinforcement learning (RL) cannot explicitly enforce security-related constraints. To address these challenges, we formulate the problem as a constrained Markov decision process and develop a robust soft-fusion SAC-Lagrangian algorithm. The constrained RL component incorporates a long-term discounted eavesdropper-SINR cost into the learning process through a Lagrangian mechanism, enabling secrecy-rate optimization under explicit security requirements. It should be noted that the original per-slot hard SINR constraint is relaxed into a discounted cumulative-cost constraint when the problem is formulated as a CMDP, which is the standard formulation for long-term policy learning. To handle eavesdropper-location uncertainty, the potential eavesdropping region is partitioned into multiple subregions, and parallel reward-critic pairs are used to evaluate the worst-case secrecy performance in each subregion, while a single global cost critic evaluates the softmax-fused constraint cost. Their outputs are then fused to learn a single robust policy that balances secrecy-rate maximization and constraint satisfaction across uncertain eavesdropper locations. Simulation results show that the proposed method achieves faster stabilization of the training curve, a higher average secrecy rate, a higher worst-user secrecy rate, and a significantly lower constraint-violation probability than benchmark schemes.

Using solar power to drive ground sensors in an uncrewed aerial vehicle-Internet of Things (UAV-IoT) mobile edge computing (MEC) system deployed in inaccessible or hazardous areas provides a sustainable solution to battery replacement for IoT sensors. Nevertheless, this approach faces two critical challenges. First, terrain variations and landscape shadowing cause uneven light distribution, leading to significant disparities in solar energy harvesting among nodes, which subsequently affects system throughput fairness due to unequal energy availability for data computation and task offloading. Second, atmospheric attenuation dynamics introduce stochastic variations in solar panel output, resulting in energy conversion instability and potential temporal battery outages. These challenges are further aggravated by the randomness of data arrival, which can destabilize the data queue. To address these difficulties, in this article, we first design an $\alpha $ -fairness utility function to tackle the throughput fairness issue. After that, to handle the randomness of energy and data arrivals, we employ a Lyapunov-based optimization approach to maximize the long-term system utility function, formulating the problem as a multistage online stochastic optimization, with time-average constraints on solar energy supply, data queue stability, and energy consumption of the sensor. We then decompose the original problem into a series of deterministic per-slot optimization problems to decouple control solutions across slots. Afterward, we iteratively optimize the data admission control, communication and computation resource allocations, and the UAV’s trajectory in each slot. The proposed scheme has low computation complexity for online execution. Extensive simulations demonstrate its effectiveness in achieving application-specific throughput fairness while maintaining energy and data queue stability under fluctuating working conditions. In addition, compared with benchmark algorithms, our scheme achieves higher system throughput through more judicious resource management and trajectory control strategies.

Federated learning (FL) reaps high accuracy by protecting data ownership, governance or privacy. Compared to conventional Federated Averaging (FedAvg), Federated Learning with Adaptive Optimizer (FedAdam) achieves better convergence performance by dynamically adjusting both optimization direction and learning rate. However, FedAdam suffers from impulse overshooting which induces poor convergence performance in terms of accuracy and/or convergence speed. To improve the convergence performance of FedAdam, we first borrow the idea of feedback control to re-interpret FedAdam as an optimizer consisting only of integral term (I). Next, we propose an adaptive federated optimization algorithm by incorporating proportional term P and differential term D to form a centralized PID controller at the parameter server. The proposed FedAdamPID generalizes FedAdam while the paper consider the problem of hyperparameters P, I and D optimization. In particular, we propose two methods to find the values of P, I, and D terms, including grid searching over three dimensions (Grid), and emloying reinforcement learning, namely deep deterministic policy gradient (DDPG) The two schemes are called Grid-FedAdam-PID and DDPG-FedAdam-PID, respectively. A series of experimental tests on typical models and datasets for Internet of Things (IoT) settings verified that FedAdam-PID achieves significantly better convergence performance when compared with FedAdam. DDPG-FedAdam-PID provides the best performance, compared with grid search, while also being flexible enough for different environments.

In future B5G/6G broadband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance, especially when uplink users share the wireless medium using non-orthogonal multiple access (NOMA) schemes. This is because the successive interference cancellation (SIC) decoding technique, used in NOMA, is incapable of eliminating the interference caused by PA distortion. Consequently, each user’s decoding process suffers from the cumulative distortion noise of all uplink users. In this paper, we establish a new and tractable DPD-PA distortion signal model based on real-world measurements, where the distortion noise power is a polynomial function of PA transmit power diverging from the oversimplified linear function commonly employed in existing studies. Applying the proposed signal model, we characterize the capacity rate region of multi-user uplink NOMA by optimizing the user transmit power. Our findings reveal a significant contraction in the capacity region of NOMA, attributable to polynomial distortion noise power. For practical engineering applications, we formulate a general weighted sum rate maximization (WSRMax) problem under individual user rate constraints. We further propose an efficient power control algorithm to attain the optimal performance. Numerical results show that the optimal power control policy under the proposed non-linear PA model achieves on average 13% higher throughput compared to the policies assuming an ideal linear PA model. Overall, our findings demonstrate the importance of accurate PA distortion modeling to the performance of NOMA and provide efficient optimal power control method accordingly.

As a non-verbal form of communication, gestures convey information through bodily movements and postures. Gesture recognition provides a more intuitive and natural human-computer interaction (HCI) experience, making it an integral component of the field of HCI. Recently, Wi-Fi-based gesture recognition has become a popular direction in research and applications due to its low cost, privacy-friendly nature, and convenience. However, due to the differences in the distribution of gesture data between the known and target environments, deploying the gesture recognition model in a new environment may induce high costs in annotating data labels and retraining the model. To achieve a cost-effective transferable gesture recognition model, we propose an efficient Wi-Fi-based gesture recognition domain-adaptive method (LAGER) that can maintain high recognition accuracy in a new environment without the need for labeled samples. Specifically, LAGER divides the cross-domain Wi-Fi gesture recognition problem into two inter-related sub-problems, where we iteratively apply a pseudo-label guided feature alignment and feature augmentation method in a latent space by leveraging the wisdom of unsupervised domain adaptation. To minimize the negative impact of erroneous pseudo-labels in the early training stage, we introduce a pre-heated training technique that separates the training process into two parts associated with different training strategies. We evaluate our method on the Widar3.0 dataset and compare the performance under various cross-domain settings with several representative benchmark methods. The proposed LAGER evidently outperforms all the benchmark methods. In particular, compared to the cross-domain recognition method used in Widar3.0, the proposed LAGER achieves 6.89% to 7.83% higher average accuracy in different cross-domain experiments considered.

By separating huge dimensional matrix-matrix multiplication at a single computing node into parallel small matrix multiplications (with appropriate encoding) at parallel worker nodes, coded distributed computing (CDC) tackles the straggler problem and hence speeds up the computation significantly. Existing CDC encoding schemes are based on linear combination (LC), which have two drawbacks: First, heavy computational burden is introduced to both encoding and decoding phases. Second, large numerical error occurs in the decoding phase. To relieve these two effects, a fresh new 2D-SAZD-CDC framework that non-trivially generalizes 1D-SAZD-CDC is proposed, where D is short for dimension, the operation for encoding and decoding is implemented by shift-and-add (SA) and zigzag decoding (ZD) that replaces LC and matrix inversion, respectively. The nontrivial generalization lies in joint design of the operations in 2D are needed in both the encoding and the decoding phases, so as to ensure possesion of combination property (CP) and ZD from 2D viewpoint. More specifically, 2D-SA encoding is designed, 2D-ZD decoding (alternates intermittently between 2D) is proposed, and a proof for satisfying CP and ZD from 2D viewpoint is also given. Numerical studies show that 2DSAZD-CDC significantly improves the numerical stability and computational load performance over existing LC based schemes.

Due to its agility, reusability, and programmability, the unmanned aerial vehicle (UAV) can be utilized as a flying base station in mobile-edge computing (MEC) systems, providing cost-effective computation services to distributed ground devices in the absence of terrestrial infrastructure. A defect of traditional UAVs is that they rely heavily on the onboard limited battery for the power supply, severely restricting UAVs’ operating endurance and flying range. To tackle this problem, we consider using a solar-powered UAV as the edge server for sensing data collection and processing. However, owing to the atmospheric absorption, the amount of harvested solar energy increases with the flying altitude, resulting in a nontrivial tradeoff between energy harvesting and communication performance. In addition, the dynamics of the moving clouds also make energy harvesting exhibit stochastic variations in the solar panel’s output, rendering the instability of the energy conversion. In this article, given the randomness of energy and data arrivals, we propose a Lyapunov-based method to maximize the long-term system throughput, subject to the time average constraints on the solar power supply, the data queue stability, and the energy consumption of the devices. Specifically, without knowing the future system knowledge, we formulate the problem as a multistage online stochastic optimization and decompose the original problem into per-slot deterministic optimization problems. In each slot, we iteratively optimize the data sensing rate, the computation offloading, the communication resource allocation, and the 3-D trajectory of the UAV. The proposed algorithm can adaptively adjust the UAV’s altitude according to its residual energy, thus striking a balance between energy harvesting and system throughput. Furthermore, it has low complexity which makes it suitable for online implementation. Extensive simulations have demonstrated the effectiveness of the algorithm, in that, it significantly outperforms the benchmark schemes in the system throughput, while satisfying the prescribed time average constraints at the same time.

This article considers a cognitive wireless-powered communication network (WPCN) (C-WPCN), which overlays to a primary peer-to-peer communication link and constitutes a hybrid access point (HAP) with multiple antennas. The HAP first transmits wireless power to an array of wireless devices (WDs) with low-power consumption and then receives sensing data from these WDs. In particular, user cooperation is proposed among the cluster of WDs to enhance the throughput fairness. We consider that the HAP assists primary transmission by cooperatively transmitting the primary message, and at the same time cancels the interference from the primary transmission. However, the mutual interference between the WDs and the primary link cannot be fully mitigated. To guarantee throughput fairness and protect the primary communication link, the minimum achievable data rate among the WDs (max–min throughput) is maximized under a primary rate constraint by optimizing the C-WPCN's transmission time allocation. To settle this nonconvex issue, a high-efficiency algorithm is put forward on the ground of the technique of sequential convex approximation. Furthermore, we run simulations in real network settings and the simulation results prove that our proposed approach can validly raise the secondary WPCN's throughput fairness under a constraint of primary rate.