The ability to ensure covert unmanned aerial vehicle (UAV) communications is imperative in critical missions such as military surveillance and emergency response. In this paper, a multi-UAV covert communication system with informed jammers is investigated. To increase ground wardens’ detection uncertainty, we propose a joint dynamic scheduling and sensing jamming (DSSJ) scheme. Unlike existing approaches with fixed UAV roles and non-informed jamming, DSSJ dynamically schedules UAVs across adjacent time slots (TSs), while the jamming UAV performs sensing-based informed jamming per TS. Closed-form expressions are derived for the covert rate and minimum detection error probability (MDEP) under the worst-case scenario with optimal warden detection. An optimization problem is formulated to maximize the normalized weighted sum of covert rate and MDEP, subject to multiple constraints, including scheduling, sensing ratio, and other key factors. To solve this mixed-integer non-convex problem, we design a double deep Q-network (DDQN)-DSSJ algorithm, integrating DSSJ within a deep reinforcement learning framework, accelerated by experience replay and dynamic exploration, achieving real-time covert decision-making with polynomial complexity. Simulations demonstrate that DDQN-DSSJ achieves 25% faster convergence, enhanced stability, and superior covertness compared to proximal policy optimization and deep Q-network. Additionally, DDQN-DSSJ improves the covert rate by over 4× and MDEP by up to 28.3%, outperforming state-of-the-art schemes.

Real-time speech communication over wireless networks remains challenging, as conventional channel protection mechanisms cannot effectively counter packet loss under stringent bandwidth and latency constraints. Semantic communication has emerged as a promising paradigm for enhancing the robustness of speech transmission by means of joint source channel coding (JSCC). However, its cross-layer design hinders practical deployment due to the incompatibility with existing digital communication systems. To address this, we perform JSCC over the network layer to combat packet loss and support real deployment. Inspired by the generative latent modeling, we propose Glaris, a generative latent-prior-based resilient speech semantic communication framework that performs resilient transform coding in the generative latent space. Generative latent priors enable high-quality packet loss concealment (PLC) at the receiver side, well-balancing semantic consistency and reconstruction fidelity. Additionally, an integrated error resilience mechanism is designed to mitigate the error propagation and improve the effectiveness of PLC. Compared with traditional packet-level forward error correction (FEC) strategies, our new method achieves enhanced robustness over dynamic wireless networks while reducing redundancy overhead significantly. Experimental results on the LibriSpeech dataset demonstrate that Glaris consistently outperforms existing error-resilient codecs, achieving JSCC-level robustness while maintaining seamless compatibility with existing systems, and it also strikes a favorable balance between transmission efficiency and error resilience.

In this paper, we study the coded caching problem for the (L,K,M,N) multi-user information retrieval (MIR) system, which consists of a content library of N files, an L-antenna base station (BS) without direct library access, and K single-antenna users, each equipped with a cache of M files. The users communicate with each other assisted by the BS to decode their required files. M. Abolpour et al. proposed an MIR coded caching scheme (referred to as the ASMST scheme), where the uplink/downlink normalized delivery time (NDT) achieves the information-theoretic lower bound for the multiple-input multiple-output (MISO) coded caching system under uncoded cache placement and one-shot linear delivery. However, its subpacketization and computational complexity are extremely high. In order to reduce the computational complexity, we derive that the condition for the uplink/downlink strategy is exactly that for a multi-antenna placement delivery array (MAPDA) with the optimal sum Degree-Of-Freedom (sum-DoF). Based on existing MAPDAs, we proposed three new MIR coded caching schemes, which significantly reduce both the subpacketization and computational complexity while maintaining the same uplink/downlink NDT as the ASMST scheme.

This paper proposes a digital twin network (DTN)-driven framework for multi-unmanned aerial vehicle (UAV) covert communication with informed jammers. To maximize warden detection uncertainty, we design a dynamic scheduling and sensing jamming (DSSJ) scheme that dynamically schedules UAV roles (communication/jamming/backup) across adjacent time slots (TSs), while the jamming UAV employs sensing-based informed jamming per TS. Leveraging DTN’s real-time digital-physical synchronization, closed-form expressions for the user’s covert rate and warden’s minimum detection error probability (MDEP) are derived under worst-case detection. A DTN-driven double deep Q-network (DDQN)-DSSJ algorithm solves the normalized weighted-sum maximization problem under covertness, sensing ratio, power, and speed constraints. Simulations demonstrate that DTN-driven DDQN-DSSJ achieves 25% faster convergence, enhanced stability, and superior covertness compared to proximal policy optimization (PPO) and deep Q-network (DQN).

Data compression technology is able to reduce data size, which can be applied to lower the cost of task offloading in mobile edge computing (MEC). This article addresses the practical challenges for robust trajectory and scheduling optimization based on data compression in the uncrewed aerial vehicle (UAV)-assisted MEC, aiming to minimize the sum energy cost of terminal users while maintaining robust performance during UAV flight. Considering the nonconvexity of the problem and the dynamic nature of the scenario, the optimization problem is reformulated as a Markov decision process (MDP). Then, a randomized ensembled double Q-learning (REDQ) algorithm is adopted to solve the issue. The algorithm allows for higher feasible update-to-data ratio, enabling more effective learning from observed data. The simulation results show that the proposed scheme effectively reduces the energy consumption while ensuring flight robustness. Compared to the PPO and A2C algorithms, energy consumption is reduced by approximately 21.9% and 35.4%, respectively. This method demonstrates significant advantages in complex environments and holds great potential for practical applications.

By fully exploring the edge computing “supply-demand” relationship between the mobile edge computing (MEC) servers and the differentiated application requests, the computing pricing (i.e.“, supply”) and allocating (i.e.“, demand”) can be coordinated well for the practical network consisting of heterogeneous users and MEC operator. In this paper, the fair-aware computing pricing, beneficial offloading (i.e., obtaining positive utility) and local computing adjustment are jointly discussed under a pricing-enabled MEC. By considering heterogeneous application requests, fair service demand and limited computing provisioning, a multi-objective composite utility optimization is developed to maximize the user utility and the MEC operator profit simultaneously. Therein, the fair service condition is proposed, under which each user can experience a similar chance to obtain beneficial offloading. In order to solve the goal problem with undetermined objective function and conditions, a fair service enabled pricing and allocating algorithm (FS_PAA) with extremely low complexity is proposed by exploiting classification discussion method and convex optimization. Our FS_PAA reveals the explicit relationship between the optimal offloading decision and computing pricing, and the explicit relationship between the optimal computing pricing and the maximum computing provisioning, which helps to provide an effective reference for practical edge computing deployment. Simulation results show that our FS_PAA can 1) ensure fair offloading services for practical differentiated requests; 2) provide green offloading service for more users; 3) greatly improve the utilization of edge computing resource.

The noncoherent single-input-multiple-output (SIMO) system experiences severe cross-product mutual interference with a limited number of receive antennas, severely limiting the number of simultaneous users that can be served in the uplink. In Rician channel, a multiuser energy difference detection (EDD) scheme is introduced for dual-frequency complementary amplitude signals. It eliminates cross-product interference, significantly reducing the number of antennas required by the base station (BS), while extending the constellation space for energy detection (ED). Based on this extended constellation space and channel gain information, two optimized multiuser constellations are obtained by maximizing the minimum joint constellation distance and directly equalizing the receive constellation space. Additionally, the impacts of user mobility are analyzed, and a simple strategy is designed to maintain performance by updating the moving user’s path gain. Simulation results show that the proposed scheme can handle more simultaneous users with the same scale antennas, and it also exhibits strong robustness to user mobility at the same time.

For multiple unmanned-aerial-vehicles (UAVs)-assisted mobile-edge computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multitype tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multiagent Markov decision process and propose a multiagent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic algorithms.

3D object classification is an important task in computer vision. In order to explore the high-order and multi-modal correlations among 3D data, we propose an adaptive multi-hypergraph convolutional networks (AMHCN) framework to enhance 3D object classification performance. The proposed network improves the current hypergraph neural networks in two aspects. Firstly, existing networks rely on hyperedge constrained neighborhoods for feature aggregation, which may introduce noise or ignore positive information outside the hyperedges. To this end, we develop the partially absorbing random walks (PARW) to hypergraph for capturing optimal vertex neighborhoods from hypergraph globally. Then, based on the PARW on hypergraph, we design a new hypergraph convolution operator to learn deep embeddings from the optimized high-order correlation, which enables effective information propagation among the most relevant vertices. Secondly, concerning the multi-modal representations in practice, the current multi-modal hypergraph learning models either treat all modalities equally or introduce abundant parameters to learn weights of different modalities. To overcome these shortcomings, we propose a simple but effective dynamic weighting strategy for combining multi-modal representations, in which the importance of each modality can be adjusted adaptively by the loss function. We apply the proposed model to 3D object classification, and the experimental results on two 3D benchmark datasets demonstrate that our method outperforms the state-of-the-art methods, testifying to the effectiveness of both our convolution method and multi-modality fusion strategy.

Under the condition of limited number of antennas in current noncoherent single-input-multiple-output (SIMO), the key problems of serious mutual interference and small number of supported users are still difficult to overcome. In this paper, we proposed a multi-user massive SIMO based on energy difference detection (EDD) in Rician channels. The proposed method only needs to simply estimate the line-of-sight (LOS) amplitude gain of users. The cross-product interference caused by energy detection is eliminated by designing complementary amplitude constellations and performing energy difference detection at the base station (BS). It reduces the number of antennas required by the BS to distinguish data streams from different users. In addition, we also analyze the impact on the system performance when users move, and adopt the low-complexity approach of re-estimating the moving user’s coefficient to maintain a good performance. Simulation results show that the proposed method can use the LOS component of the Rician channels to support more concurrent users, and obtain a better performance than existing methods.

Recently, hypergraph learning has shown great potential in a variety of classification tasks. However, existing hypergraph neural networks lack flexibility in modeling and extracting high-order relationships among data. To solve this problem, we propose a novel framework called hypergraph wavelet neural networks (HGWNN) to explore the high-order correlation in 3D data. Firstly, considering the non-uniformity of most data sets in the real world, we propose a “data-driven” hypergraph construction scheme, which is more efficient than some commonly used hypergraph construction methods. Secondly, in order to efficiently learn deep embeddings from the constructed hypergraph, we propose a hypergraph wavelet convolution operator. It enables efficient information aggregation by fully exploiting the localization property of wavelets. This convolution operator is suitable for both non-uniform and uniform hypergraphs. Finally, we design a new hypergraph regularizer based on the sparse prior of wavelet coefficients to promote local smoothness and avoid network overfitting. We have conducted experiments on object classification tasks on two 3D benchmark datasets: the National Taiwan University (NTU) 3D model dataset and the ModelNet40 dataset. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states.

Offloading application to cloud can augment mobile devices' computation capabilities for emerging resource-hungry mobile applications, however it can consume both much time and energy for mobile devices offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve the above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. Finally, the simulation results show that, compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.

Recent advances in network coding have shown great potential for efficient information transfer. In this paper, exploiting inter-layer and inter-session network coding, we address the distributed control problem in heterogeneous-service networks (HSNs) with booming multi-rate multicast (MRM) and unicast (UC) services. Different from the literatures on inter-layer/inter-session schemes, heterogeneity and fairness among MRM users and between different services are jointly considered. With the Lagrangian and subgradient method, a decentralized rate control algorithm is developed with little coordination among intermediate nodes, in which only local information is needed to achieve rate, congestion, and fairness balance control. Numerical examples are provided to verify the effectiveness and convergence of the proposed algorithm. Furthermore, we demonstrate the performance improvement and implementation advantages of the proposed algorithm compared with the previous solutions considering layered coding for an MRM service or inter-session coding limited for UC services.