To meet the computational requirements of computation-intensive and delay-sensitive applications, we construct an Unmanned Aerial Vehicle (UAV)-assisted three-layer collaborative computing framework that integrates local, edge, and cloud computing resources. However, in dynamic UAV-assisted environments, some existing approaches lack adaptability and struggle to effectively balance delay and energy consumption. To address these challenges, we formulate a joint optimization problem that minimizes the weighted sum of delay and energy consumption, where adaptive weight factors are dynamically adjusted according to system state variations. Due to the non-convex and high-dimensional nature of our proposed problem, traditional optimization methods are generally inadequate. Hence, the problem is modeled as a Markov Decision Process (MDP), and a normalization-based reward function is designed to eliminate the dimensional imbalance between delay and energy consumption. A Dynamic Normalization Twin Delayed Deep Deterministic Policy Gradient (DN-TD3) algorithm is then proposed, which incorporates mechanisms of adaptive exploration and criticdriven policy updates to enhance convergence stability and reduce sensitivity to hyperparameters. Simulation results demonstrate that the proposed DN-TD3 algorithm outperforms benchmark schemes in terms of system cost reduction, convergence speed, and overall stability.

To enable compute-intensive and delay-sensitive maritime services, unmanned surface vessels (USVs) can offload tasks to mobile edge computing (MEC) servers mounted on unmanned aerial vehicles (UAVs). However, jointly minimizing energy consumption and latency is challenging due to the strong coupling between communication, computation, and mobility under stringent quality-of-service (QoS) requirements. To capture this trade-off, we formulate a weighted energy–delay minimization problem that jointly optimizes one-to-one UAV–USV scheduling, task partitioning, and UAV trajectory. The resulting problem is particularly difficult due to a hybrid discrete–continuous decision space and strong temporal coupling under stringent feasibility constraints. To address this mixed-integer nonconvex optimization problem, we reformulate it as a Markov decision process (MDP) and develop a constraint-aware OU–TD3 algorithm that integrates differentiable scheduling relaxation, feasibility-aware action mapping, and adaptive OU–Gaussian mixed exploration for stable learning in high-dimensional continuous control. We further extend the formulation and solution to a cooperative multi-UAV MEC setting with signal-to-interference-plus-noise ratio (SINR)-coupled interference and coordination constraints. Extensive simulations with statistical evaluation demonstrate stable convergence and up to 54.2% cost reduction over baseline schemes, while maintaining robustness under realistic maritime disturbances.

The evolution of information and communication technology, besides the proliferation of mobile devices, pushes the horizon of the Internet of Things. The main challenges for mobile devices are limited battery power and insufficient computational resources. Multiaccess edge computing (MEC) is an emerging paradigm that can provide task offloading services in the proximity of mobile devices to alleviate their load. In addition, mobile devices cannot work in emergency scenarios (e.g., post-earthquake, post-flood, and post-hurricane) or areas without infrastructure. Tethered unmanned aerial vehicles (UAVs) have received widespread attention as an alternative base station because of their line-of-sight solid links status, flexible deployment, and sufficient energy supply. With this in mind, we propose a tethered-unmanned-aerial-vehicle-based aerial MEC network to provide communication and task offloading services in areas without infrastructure. In addition, an energy-efficient task scheduling framework is proposed to achieve more energy-efficient task scheduling. First, we propose a geometry-based placement algorithm to generate optimal placement positions for the UAV placement problem. Then, for the nonconvex task scheduling and the resource allocation problem, we propose a low complexity divide and conquer scheme, which decomposes the original problem into three subproblems and solves them separately. Extensive simulations demonstrate the better energy efficiency of the proposed framework.

In this paper, the secure information transmission of an energy harvesting (EH) cooperative network is considered, in which multiple eavesdroppers can overhear the forwarded relay signal. To prevent multiple eavesdroppers from decoding confidential signals, the destination transmits the jamming signal while the source transmits the confidential signal to the relay. Simultaneously, the relay can harvest more energy from source signals and destination jamming by the power splitting (PS) protocol, which is depicted as a non-linear EH process. Considering the imperfect self-interference cancellation (SIC) at the destination, secrecy rate maximization optimization is formulated to optimize the transmission power of source and destination. However, the formulated optimization is non-convex. To solve this problem, an iterative algorithm is proposed based on the difference of convex functions (DC) programming, which can transform non-convex optimization problems into successive approximate convex problems. Simulation results show that the proposed algorithm has a quick convergence rate, and the proposed scheme leads to a higher achievable secrecy rate.

Mobile data offloading enables the mobile network operator (MNO) to deal with the explosive growth of cellular data by leasing third‐party access points (APs) to partially deliver the mobile traffic. This study proposes a novel incentive framework for the mobile data offloading market under QoE‐aware users. Considering user satisfaction, the authors formulate the interaction among the MNO, APs, and offloaded users as a three‐stage Stackelberg game. Through the Stackelberg game, the APs determine their optimal contributions via the best response method and the offloaded users determine their optimal accepted prices via the proposed dynamic pricing mechanism. Then the MNO makes its decision for profit maximisation. Furthermore, based on contract theory, an optimal dynamic scheme between the MNO and the remaining users is established. Under the dynamic scheme, they prove the personal rationality and incentive compatibility properties. Moreover, the optimisation contract problem is transformed into a relaxed contract problem, and the proposed dynamic algorithm is subsequently used to handle non‐feasible solutions. Thus, the proposed framework can improve user satisfaction without affecting MNO profits. Simulation results show that the proposed framework can achieve better performances in terms of user satisfaction and MNO profits compared with traditional algorithms.

Orthogonal frequency division multiplexing (OFDM) introduces large peak power of transmitted signals in time, which can result in significant signal distortion in the presence of nonlinear amplifiers. Partial transmit sequence (PTS) are well-known techniques for peak-power reduction in OFDM. However, the exhaustive search of phase factors in conventional PTS causes high computational complexity. In this paper, we present a suboptimal strategy for combining partial transmitted sequences that achieve good balance between computational complexity and power control performance. The simulation results show that the proposed algorithm can not only reduces the PAPR significantly, but also decreases the computational complexity

Some load balancing algorithms in heterogeneous wireless networks can not consider the problems arising from the admission control of new service and service transfer of heavy load networks. To solve these problems, we propose a load balancing algorithm based on neural networks. This algorithm is used to conduct prediction through network load rate and achieve the network admission of new service by combining an admission control optimization algorithm. Moreover, by analyzing network performance, some services of heavy load network are transferred to overlay light load network. The simulation results indicate that our algorithm can well realize the load balancing of heterogeneous wireless network and provide high resource utilization.

When adaptive arrays are applied to practical problems, the performances of the existing adaptive algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of performance degradation can occur when the signal array response is known precisely but the training sample size is small. In this paper, we propose a novel neural network approach to robust adaptive beamforming. The proposed algorithm is based on explicit modeling of uncertainties in the desired signal array response and a three-layer radial basis function neural network (RBFNN). In the proposed algorithm, the computation of the optimum weight vector is viewed as a mapping problem, which can be modeled using a RBFNN trained with input/output pairs. Our proposed approach offers fast convergence rate, provides excellent robustness against some types of mismatches and makes the mean output array SINR consistently close to the optimal one. Computer simulation results are presented, which show that the proposed algorithm yields significantly better performance as compared with the existing adaptive beamforming algorithms.