Decentralized machine learning (DML) supports collaborative training in large-scale networks with no central server. It is sensitive to the quality and reliability of inter-device communications that result in time-varying and stochastic topologies. This paper studies the impact of unreliable communication on the convergence of DML and establishes a direct connection between the spectral properties of the mixing process and the global performance. We provide rigorous convergence guarantees under random topologies and derive bounds that characterize the impact of the expected mixing matrix's spectral properties on learning. We formulate a spectral optimization problem that minimizes the nontrivial spectral radius of the expected second-order mixing matrix to enhance the convergence rate under probabilistic link failures. To solve this non-smooth spectral problem in a fully decentralized manner, we design an efficient subgradient-based algorithm that integrates Chebyshev-accelerated eigenvector estimation with local update and aggregation weight adjustment, while ensuring symmetry and stochasticity constraints without central coordination. Experiments on a realistic low Earth orbit satellite constellation with time-varying inter-satellite link models and real-world remote sensing data demonstrate the feasibility and effectiveness of our method. The method significantly improves classification accuracy and convergence efficiency compared to existing baselines, validating its applicability in satellite and other decentralized systems.

Integrated sensing, communication, and computation (ISC2) has been increasingly studied to support high-precision sensing and low-latency computing. Fairness in ISC2remains unaddressed due to the non-convexity introduced by local computing, task offloading, result delivery, and sensing. This paper develops a new approach to max-min fair task computing while ensuring target sensing demands in ISC2systems, by holistically optimizing the beamformers of the base station (BS), the transmit powers and CPU frequencies of users (UEs), and the uplink and downlink durations. To solve this non-convex problem, we develop a new algorithm that judiciously decouples the problem into manageable steps, leveraging Rayleigh quotient maximization, semidefinite relaxation (SDR), and successive convex approximation. We rigorously prove the (local) optimality of the algorithm by proving the tightness of the SDR, i.e., a rank-one solution for the dual-purpose beamforming of the BS for both communication and sensing, and the corresponding sensing-only beamforming, can always be constructed from a solution without rank constraint. Moreover, we reveal the optimal structure of the transmit powers of the BS and UEs, the CPU frequencies of the UEs, and the attainability of absolute fairness. As corroborated numerically, our algorithm substantially outperforms its alternatives in task completion and fairness.

EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending source data to a server is bandwidth-intensive and may compromise privacy. Instead, federated learning allows each device to upload a shared machine-learning model update with locally processed data. However, this technique, which depends on aggregating model updates from various IoT devices, is vulnerable to attacks from malicious entities that may inject harmful data into the learning process. This paper introduces a new attack method targeting federated learning in EdgeIoT, known as data-independent model manipulation attack. This attack does not rely on training data from the IoT devices but instead uses an adversarial variational graph auto-encoder (AV-GAE) to create malicious model updates by analyzing benign model updates intercepted during communication. AV-GAE identifies and exploits structural relationships between benign models and their training data features. By manipulating these structural correlations, the attack maximizes the training loss of the federated learning system, compromising its overall effectiveness.

Mounted on an uncrewed aerial vehicle (UAV), UAV-borne reconfigurable intelligent surfaces (RISs) can enjoy flexibility with a considerable probability of line-of-sight (LoS) channels, and assist communications between terrestrial nodes. Challenges arise in designing UAV-borne RIS (U-RIS) assisted communications, such as the influence of non-LoS, dynamic RIS configuration, and energy consumption of the UAV. To tackle these challenges, we put forth a new U-RIS assisted uplink transmission framework, in which the direct links from the ground users to the corresponding ground base station are blocked. We optimize the energy efficiency of the network by collectively devising the RIS configuration, user scheduling, power allocation, and UAV trajectory, under a probabilistic LoS channel model. Through alternating optimization and successive convex approximation, an efficient approach is established. Simulations validate that our approach is able to significantly enhance energy efficiency by 78% in comparison with its benchmarks.

This paper develops a novel framework for differentially private (DP) wireless federated learning (FL) with integrated sensing and communication (ISAC). In this framework, which is referred to as DP-ISAC-FL, wireless devices sense data and upload the trained local models using ISAC technique. The local training can take place concurrently with sensing at each device. We analyze the convergence upper bound of DP-ISAC-FL and rigorously capture the impact of device selection (for model training), time allocation between sensing/training and model uploading for the selected devices, and the allocations of channels, modulations, and transmit powers. We also develop an algorithm that enforces the convergence of DP-ISAC-FL by minimizing the convergence upper bound in an OFDMA system with discrete modulations. The beamforming for sensing, device selection, and the allocations of time, subchannels, modulations, and transmit powers are jointly optimized using successive convex approximation (SCA), adapting to the channels and computing capabilities of the devices. Experiments on multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) show that DP-ISAC-FL with optimal allocations can significantly improve the learning convergence and accuracy under different privacy levels, e.g., by 7% and 18%, compared with its benchmarks. This is attributed to 68% more sensing data that DP-ISAC-FL can admit for model training.

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. Specifically, the detection success rate is improved by up to 65%, 55%, and 51% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.

The use of legitimate unmanned aerial vehicles (UAVs) to surveil and track misbehaved UAVs can serve a crucial role in public safety and security. This paper proposes a new deep reinforcement learning (DRL)-based online control scheme for visual-based UAV-on-UAV tracking and monitoring, where a solar-powered, fixed-wing UAV tracks a suspicious UAV target by having the target inside its effective visual range. The key idea is a new deep deterministic policy gradient (DDPG)-based model, which can cope with the continuous state and action spaces of the monitor and learn the optimal acceleration control policy adapting to the solar power availability and the target’s movement. The state space is designed to be the relative position of the monitor to the target, thereby preventing model infeasibility. Experiments show that the new algorithm can maintain a desired distance from the target, and outperform control-and optimization-based alternatives in terms of energy efficiency and tracking accuracy. An interesting finding is that our algorithm learns faster and better with a constraint of a minimum allowed battery energy reserve. The reason is that, without the constraint, the monitor is more likely to deplete its battery before the end of a surveillance mission.

Federated learning (FL) can suffer from communication bottlenecks when deployed in mobile networks, limiting participating clients and deterring FL convergence. In this context, the impact of practical air interfaces with discrete modulation schemes on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F2L) over an orthogonal frequency division multiple-access (OFDMA) air interface, termed as “OFDMA-F2L”, allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulation scheme, adapting to channel conditions and computing power. Specifically, we derive an upper bound on the optimality gap of OFDMA-F2L capturing the impact of these selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a “winner-takes-all” policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F2L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18% and 5%, compared to potential alternatives.

This article presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet of Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance of a reconfigurable intelligent surface (RIS), we propose to augment the radio environment, suppress jamming signals, and enhance the desired signals. The UAV is designed to learn its trajectory and the RIS configuration based solely on changes in its received data rate, using the latest deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) models. Simulations show that the proposed DRL algorithms give the UAV with strong resistance against jamming and that the TD3 algorithm exhibits faster and smoother convergence than the DDPG algorithm, and suits better for larger RISs. This DRL-based approach eliminates the need for knowledge of the channels involving the RIS and jammer, thereby offering significant practical value.

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming. It is non-trivial to perform holistic selections of users, data streams, and modulation-coding modes for all subchannels, and RIS configuration in a downlink multiuser OFDMA system under jamming attacks, because of a mixed-integer program nature and difficulties in acquiring the channel state information (CSI) of the channels to and from the RIS and from an uncooperative jammer. We propose a new deep reinforcement learning (DRL)-based approach that learns through changes in the data rates of the users to reject jamming and maximize the sum rate. The key idea is to decouple the continuous RIS configuration from the discrete selections of users, data streams, subchannels, and modulation-coding modes. Another critical aspect is that we show the optimal selections almost surely follow a winner-takes-all strategy. Accordingly, the new DRL framework learns the RIS configuration with a twin-delayed deep deterministic policy gradient and takes the winner-takes-all strategy to evaluate the reward, thereby reducing the action space and accelerating learning. Simulations show the framework converges fast and fulfills the benefit of the RIS. With no need for the CSI of the channels to and from the RIS and from the jammer, the framework offers practical value.

Unmanned aerial vehicles (UAVs) play a critical role in radio surveillance to decipher malicious messages, thanks to their flexibility, mobility, and likely line-of-sight (LoS) to ground targets. Reconfigurable intelligent surfaces (RISs) can potentially create radio surveillance channels towards the UAVs by passively configuring the radio environments without raising suspicion. This paper presents a new deep reinforcement learning (DRL)-driven framework for radio surveillance, where a fixed-wing UAV is employed to acquire the radio fingerprint of a suspicious transmitter (Tx) with the aid of a benign RIS. A new Twin Delayed Deep Deterministic policy gradient (TD3) model is designed to allow the UAV to learn its trajectory and the RIS configuration based on its observed transmit rate of the suspicious Tx, eliminating the need for channel state information to and from the RIS. The novel contributions include the consideration of the fixed-wing UAV, and the action and reward designed to capture the mobility constraint of the UAV. Simulations demonstrate that the new approach offers the UAV monitor an exceptional and reliable radio surveillance capability, while keeping a desired distance from the UAV to the Tx. The use of the RIS allows for significant improvements of over 37% and 59% in the eavesdropping success probability and average eavesdropping rate, respectively.

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%.

Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by legitimate monitoring UAVs (or monitors) can be crucial to public safety and security. It is non-trivial to optimize the trajectory of a monitor while conceiving its monitoring intention, due to typically non-convex propulsion and thrust power functions. This article presents a novel framework to jointly optimize the propulsion and thrust powers, as well as the 3D trajectory of a solar-powered monitor which conducts covert, video-based, UAV-on-UAV tracking and surveillance. A multi-objective problem is formulated to minimize the energy consumption of the monitor and maximize a weighted sum of distance keeping and altitude changing, which measures the disguising of the monitor. Based on the practical power models of the UAV propulsion, thrust and hovering, and the model of the harvested solar power, the problem is non-convex and intangible for existing solvers. We convexify the propulsion power by variable substitution, and linearize the solar power. With successive convex approximation, the resultant problem is then transformed with tightened constraints and efficiently solved by the proximal difference-of-convex algorithm with extrapolation in polynomial time. The proposed scheme can be also applied online. Extensive simulations corroborate the merits of the scheme, as compared to baseline schemes with partial or no disguising.

Thanks to their quick placement and high flexibility, unmanned aerial vehicles (UAVs) can be very useful in the current and future wireless communication systems. With a growing number of smart devices and infrastructure-free communication networks, it is necessary to legitimately monitor these networks to prevent crimes. In this paper, a novel framework is proposed to exploit the flexibility of the UAV for legitimate monitoring via joint trajectory design and energy management. The system includes a suspicious transmission link with a terrestrial transmitter and a terrestrial receiver, and a UAV to monitor the suspicious link. The UAV can adjust its positions and send jamming signal to the suspicious receiver to ensure successful eavesdropping. Based on this model, we first develop an approach to minimize the overall jamming energy consumption of the UAV. Building on a judicious (re-)formulation, an alternating optimization approach is developed to compute a locally optimal solution in polynomial time. Furthermore, we model and include the propulsion power to minimize the overall energy consumption of the UAV. Leveraging the successive convex approximation method, an effective iterative approach is developed to find a feasible solution fulfilling the Karush-Kuhn-Tucker (KKT) conditions. Extensive numerical results are provided to verify the merits of the proposed schemes.

Optimal resource management for smart grid powered multi-input multi-output (MIMO) systems is of great importance for future green wireless communications. A novel framework is put forth to account for the stochastic renewable energy sources (RES), dynamic energy prices, as well as random wireless channels. Based on practical models, the resource allocation task is formulated as an optimization problem that aims at maximizing the weighted sum-rate of the MIMO broadcast channels. A two-way transaction mechanism and storage units are introduced to accommodate the RES variability. In addition to system operating constraints, a budget threshold is imposed on the worst-case energy transaction cost due to the possibly adversarial nature. Capitalizing on the uplink-downlink duality and the Lagrangian relaxation-based subgradient method, an efficient algorithm is developed to obtain the optimal strategy. Generalizations to the setups of time-varying channels and ON-OFF transmissions are also discussed. Numerical results are provided to corroborate the merits of the novel approaches.

A framework is introduced to integrate renewable energy sources (RES) and dynamic pricing capabilities of the smart grid into beamforming designs for coordinated multipoint (CoMP) downlink communication systems. To this end, novel models are put forth to account for harvesting, storage of nondispatchable RES, time-varying energy pricing, and stochastic wireless channels. Building on these models, robust energy management and transmit-beamforming designs are developed to minimize the worst-case energy cost subject to the worst-case user QoS guarantees for the CoMP downlink. Leveraging pertinent tools, this task is formulated as a convex problem. A Lagrange dual-based subgradient iteration is then employed to find the desired optimal energy-management strategy and transmit-beamforming vectors. Numerical results are provided to demonstrate the merits of the proposed robust designs.