Due to the triple mobility of mobile users (MUs), unmanned aerial vehicle (UAV) relays, and low Earth orbit (LEO) satellites, handover becomes a critical and challenging issue for maintaining the continuity and quality of communication services in UAV-assisted LEO satellite networks. This paper proposes a distributed handover decision-making process aimed at improving scalability and reducing communication overhead. The handover problem is modeled as a decentralized Markov decision process (DEC-MDP) with the objective of maximizing the total end-to-end (E2E) throughput. We design an independent proximal policy optimization-based distributed intelligent handover (IPPO-DIH) algorithm within a centralized training with decentralized execution framework to solve the DEC-MDP. To analyze the theoretical optimal E2E throughput, we eliminate the correlation between handover decisions at different time steps. A three-sided matching algorithm with theoretical convergence guarantees is designed to obtain a stable matching among MUs, UAV relays, and LEO satellites at each time step. These stable matchings are combined to provide a theoretical performance benchmark for the handover algorithms. Simulation results validate the convergence of the proposed IPPO-DIH and three-sided matching algorithms. Additionally, the total E2E throughput achieved by the IPPO-DIH algorithm approaches the theoretical performance benchmark and outperforms typical handover algorithms.

This paper systematically analyzes and optimizes the outage performance for a multi-cell vehicle-to-infrastructure (V2I) network, wherein each cell is assisted by a roadside-mounted reconfigurable intelligent surface (RIS) for signal enhancement. We first derive the closed-form outage probability (OP) expression for Nakagami-m-fading V2I links by modeling signal and interference distributions via the moment-matching technique. The OP expression reveals that enlarging the element quantity of RISs can significantly reduce OP, as the inter-cell interference grows much slower than the signal power due to incoherent combination of interference paths. To further optimize the network-wide outage performance, we propose two power control mechanisms: a distributed game-based approach achieving Nash equilibrium through properly-designed utility functions, and a centralized deep reinforcement learning (DRL)-based approach that minimizes the maximum OP of all the V2I links by leveraging the advanced soft actor-critic (SAC) algorithm. Finally, simulations verify our theoretical derivations, and demonstrate that RIS deployment combined with optimized power control substantially improves the reliability of V2I networks.

We examine a secure wireless communication system that is enabled by unmanned aerial vehicles (UAVs) in this research. In the wireless communication system with an eavesdropping UAV, we deploy a relay UAV to facilitate the transmission of confidential signals from the source station (denoted asS) to ground users. Additionally, we select an idle relay UAV to act as a cooperative jamming UAV, sending interference signals to the eavesdropping UAV. It is quite feasible that the eavesdropping UAV will leverage its mobility to improve the quality of its eavesdropping, making its trajectory unpredictable. First, to address the worst-case scenario for the ground user’s security performance, we assume the eavesdropping UAV approaches at the closest distance.We aim to maximize the worst secrecy rate under perfect CSI via designing the flight trajectory and transmission power of both the relay UAV and the jamming UAV. Second, we investigated the performance of the system’s secrecy outage probability under imperfect CSI. The presence of eavesdropping UAVs and the unpredictable nature of their environment makes traditional convex optimization methods mathematically complex for solving the trajectory optimization problem of the relay and jamming UAVs. To address this, we propose the Multi-Agent joint design trajectory and power (MAJDTP) algorithm based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to optimize the flight trajectory and transmission power of both UAVs. During the design and training process, the relay and jamming UAVs are treated as agents to derive their optimal flight paths and transmission energy. Finally, our approach surpasses the benchmark algorithm, as demonstrated by the simulation results.

The integration of network function virtualization (NFV) with mobile edge computing (MEC) fosters a more agile service provisioning in a network operational cost-efficient manner. However, some challenges exist in adapting to the unpredictable network stochastics and resource restrictiveness, when placing virtualized network functions (VNFs) or service function chains (SFSs) appropriately onto MEC networks. In this work, we study the cost-efficient online SFC deployment in MEC networks, where each service is translated as an SFC flow and traverses through networks to meet service demands. First, we formulate a long-term time-averaged network operational cost minimization problem, by optimizing both SFC mapping and flow routing, to keep the system stability. Then, to deal with the non-trivial mixed-integer programming (MIP) and stochasticity properties in the SFC deployment, we use both Lp(0 <p< 1) norm-based relaxation and penalization, and learn-and-adapt techniques, to obtain an improved performance-stability tradeoff. Finally, both theoretical analyses and numerical simulations are conducted to demonstrate the proposed method’s superiority, in terms of its asymptotic optimality and reduced queue backlog.

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies, such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT; 2) the complex communication environments; and 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated CPN (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a markov decision process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly.

Ensuring signal confidentiality against eavesdroppers is particularly challenging, especially with imperfect channel state information (CSI). To address this, we propose a novel approach leveraging reconfigurable intelligent surfaces (RISs) to enhance security and optimize transmission performance. This paper focuses on secure communication in symbiotic radio (SR) systems by investigating cooperative jamming-assisted transmission with RISs, providing a robust solution to these challenges. RIS-I, acting as a secondary transmitter (STx), multicasts confidential signals from the primary transmitter (Alice) to a primary user (Bob), protecting against eavesdropping by Eve. Additionally, RIS-I transmits its own signals to a secondary user (SU) using backscattering radio technology. Meanwhile, RIS-II serves as a cooperative jammer, converting received confidential signals from Alice into jamming signals by strategically adjusting its reflection coefficients to disrupt Eve’s reception. These RISs can operate cooperatively; when RIS-II transmits as an STx, RIS-I functions as a cooperative jammer. We explore two scenarios: 1. With perfect CSI for the wiretap channel, we propose a joint SDR(Semi-definite relaxation)+MM(Minorization-maximization) optimization algorithm to simultaneously optimize Alice’s beamforming vector and the RISs’ reflection coefficients. 2. With imperfect CSI, we derive the secrecy outage probability formula and evaluate the scheme’s performance across different scenarios. Numerical results demonstrate that RIS-assisted cooperative jamming significantly enhances the secrecy rate and reduces the secrecy outage probability for Bob, outperforming traditional RIS-assisted SR systems.

In advanced vehicular networks, Roadside Unit (RSU)-based amplify-and-forward (AF) relay and passive Reconfigurable Intelligent Surface (RIS) are two potential helpers to enhance the vehicle-to-vehicle (V2V) communications when the direct link experiences poor quality. This paper presents a comprehensive comparison of the two enhancement modes from the outage performance perspective. In the presence of both direct link and enhanced link, the analytical expressions of the outage probability (OP) for the V2V communication under the two enhancement modes are derived respectively. Moreover, considering the co-channel interference caused by relay/RIS, the OP of the neighbouring vehicle-to-infrastructure (V2I) communication is also derived. Additional analysis compares the diversity order and the strength of interference created by the V2V communication under the two enhancement modes. Further discussions are presented on the effect of the channel estimation error and phase quantization error under the RIS mode. Finally, the pros and cons of the two enhancement modes are demonstrated by both the analytical and numerical results.

This paper investigates a centralized cooperative cognitive radio network (CCRN) where a primary base station (PBS) transmits a message to a primary user while a secondary user transmitter (SU-Tx) function as a friendly jammer. The jammer sends jamming signals to protect the PBS’s messages from a potential eavesdropper (Eve). However, the SU-Tx also attempts to covertly transmit its own messages to a secondary user receiver using the allocated spectrum resource, contravening the PBS regulations. To address this issue, the PBS requests its partner CBS to help detect jammer’s behavior. Specifically, we propose a generative adversarial network (GAN) optimization framework that models the strategic game between the CBS monitoring and the covert transmission of cooperative jammers. We introduce a novel GAN-based beamforming design algorithm, termed GAN-BD, to determine the power allocation at the jammer for covert communication. Additionally, we develop the detection error probability (DEP) at the CBS and derive its expression using a hypothesis testing problem. Through extensive simulation results, we demonstrate that the proposed GAN-BD algorithm can achieve near-optimal solutions for conducting covert communication, leveraging knowledge of the current network environment and exhibiting rapid convergence capabilities. The simulation results highlight the effectiveness of our GAN-BD algorithm.

Accurate cellular network traffic prediction is crucial for intelligent network planning and management in 6G. However, the non-stationary characteristics of cellular network traffic present significant challenges when training deep neural networks for traffic forecasting. To address this issue, we propose a two-stage deep learning framework, JO-DPNet, based on Bayesian joint parameter optimization, which integrates data decomposition techniques with Bayesian joint optimization to effectively mitigate the adverse impacts of non-stationarity and error accumulation on prediction accuracy. In the first stage, a data decomposition module uses Variational Mode Decomposition (VMD) to decompose the original data into network traffic subset series(TSS), thereby alleviating the negative effects of non-stationarity. In the second stage, a prediction and construction module leverages a bi-directional LSTM (Bi-LSTM) network to extract deep spatial-temporal features from the TSS in a bidirectional manner. A fully connected layer then captures the relationships between the TSS and reconstructs the predicted results into the final output. The JO module employs the Tree-structured Parzen Estimator based Bayesian optimization algorithm(TP-BO) simultaneously determines the optimal VMD mode number k and the hyperparameters of the Bi-LSTM network through probabilistic surrogate model. Extensive experiments on three real-world cellular traffic datasets demonstrate that the proposed method significantly mitigates the non-stationary characteristics of the traffic data. Compared to state-of-the-art methods, JO-DPNet achieves reductions in MAE by 29%, 3%, and 19% for three type prediction tasks on the Telecom Italia dataset. The source code is available to the public at: https://github.com/VicentZhang259/JO-DPNet.

This work studies the joint transmit power control and receive beamforming in uplink rate splitting multiple access (RSMA)-based low earth orbit (LEO) satellite networks, using both generative diffusion model and proximal policy optimization (PPO) learning framework. In particular, using RSMA, interference is partially decoded and partially treated as noise, thereby improving the spectral efficiency, while the dynamics and uncertainty in LEO satellite networks would pose challenges to the real-time power control and receive beamforming optimization. First, a long-run sum data rate maximization problem is formulated, subject to the individual data rate requirement, and then the Markov decision process (MDP) is used to model it. Second, on the basis of MDP, a generative diffusion model-based proximal policy optimization (PPO) framework is proposed, where a denoising network is taken as the actor network in PPO to output the optimal continuous policy, thereby facilitating the hyperparameter tuning and improve the sample efficiency. Finally, experiments are conducted to show advantages of merging diffusion model into PPO, in terms of larger spectral efficiency, by comparing proposed framework with benchmarks.

Vehicle-to-infrastructure (V2I) network is a new paradigm of wireless system with special topology where roadside units (RSUs) are linearly deployed along the roadside and vehicles linearly move on the road. For such system, some classical problems would have new formulations and solutions. We in this paper investigate the joint power control and user scheduling problem for a multi-cell downlink V2I network, the objective of which is to maximize the sum-rate of the network under the signal-to-interference-plus-noise ratio (SINR) constraint of each V2I link. Considering the high mobility of vehicles, the objective function is set as the mean of the sum-rate over a sequence of time slots. For ease of handling, we first decouple the problem into multiple separate subproblems based on the linear distribution of RSUs. Then we employ the quadratic transform technique for fractional programming (FP) to transform the mixed integer nonlinear programming (MINLP) subproblems into convex problems, and obtain the solutions with Branch and Bound method. Finally the validity of our proposed algorithm is verified by numerical simulations.

With recent advents of industrial Internet of Things (IIoT), the connectivity and data collection capabilities of industrial equipment have be significantly enhanced, yet bringing new challenges for the remaining useful life (RUL) prediction. To fulfill the RUL predicting demand in multivariate time series, this work proposes an encoder-decoder model termed as dual-scale transformer model (DSFormer), built upon the Transformer architecture. First, in the encoder part, a dual-attention module is designed for the weight feature extraction from both dimensions of the sensor and time series, aiming to compensate for the diverse impacts of different sensors on the prediction. Next, a temporal convolutional network (TCN) module is introduced to capture sequence features and alleviate the loss of positional information incurred by stacking blocks. Then, the feature decomposition module is integrated into the decoder for trend feature extraction from sequences, providing the model with additional sequence information. Finally, compared to existing models, the proposed method can obtain the superior performance in terms of the root mean square error (RMSE) and Score metrics on the FD001, FD002 and FD003 subsets of the C-MAPSS dataset, with an average improvement of 3.2% and 2.5% respectively. In particular, the ablation experiment further validates the effectiveness of proposed modules in handling multivariate time series and extracting features.

This paper examines integrating jamming and secondary signals for covert communications in cognitive radio networks (CRNs), aiming to enhance covertness by using jamming and secondary signals in an overlay cooperative CRN. The scenario involves a primary base station (PBS) transmitting to a primary user (PU), with a secondary user transmitter (SU-Tx) acting as a cooperative jammer to obscure the message from a malevolent secondary user named “Willie.” During idle intervals on the primary channel, the SU-Tx opportunistically accesses it to transmit secondary signals, reinforcing the covert communication of primary signals. The study quantifies the detection error probability (DEP) experienced by Willie, considering perfect and statistical channel state information (CSI) scenarios. In the perfect CSI scenario, optimization has two phases. Phase I aims to maximize the signals-to-interference-plus-noise ratio (SINR) of the PU, subject to the warden DEP exceeding a specified threshold. Phase II uses an iterative search algorithm to optimize beamforming vectors, enhancing SINR. In the statistical CSI scenario, the goal is to maximize effective transmission throughput (ETT), measuring the information transmitted from PBS to PU under covert constraints. Numerical results validate the theoretical analysis.

Owing to network function virtualization (NFV), each industrial application is constructed as a service function chain (SFC), concatenating the ordered service functions, to offer applications more flexibly in industrial Internet of Things (IIoT). When it comes to the emerging edge intelligence, the integration of NFV with edge in IIoT would enable more close-proximity services, yet also posing new challenges owing to more complicated environment. Although some efforts have been made to service function chaining in IIoT, the radio resource dynamics are not fully perceived. In this article, we investigate the radio-aware SFC deployment in the edge-enabled IIoT. First, a radio-aware deployment formulation is exhibited, steering the flow traversing both wireless and wired links. Next, Markov decision process is exhibited to track dynamics in both IIoT and radio resources. Afterwards, the natural gradient-based actor-critic SFC paradigm is introduced to adapt to network variation, by incorporating the curvature of parameter space into gradient information. To resolve the high-dimensionality in action space, we then recur to the norm penalty approach, reducing the space size by two orders of magnitude. Finally, numerical experiments are executed to uncover superiority of presented method, disclosing that the latency performance benefits from both the SFC routing between IIoT servers and elaborated wireless resource orchestration.

The aim of this paper is to present a groundwork on the delay-minimized routing problem in a vehicular ad-hoc network (VANET) where some of the vehicles are equipped with full-duplex (FD) radios. We first give the generalized delay calculation model for a multi-hop path, and prove that the Dijkstra algorithm is unable to get the delay-minimized routing path from source to destination. Then we propose two routing methods: graph-based method and deep reinforcement learning (DRL)-based method. In the graph-based method, the network topology is reformulated as an equivalent graph and then an evolved-Dijkstra algorithm is proposed. In the DRL-based method, the deep Q network (DQN) is employed to learn the shortest end-to-end path, wherein the delay is modeled as the rewards for routing actions. The graph-based method can achieve the exact minimum end-to-end delay, while the DRL-based method is more feasible due to its acceptable complexity. Finally, extensive simulations demonstrate that the DRL-based approach with proper hyper-parameters can achieve near minimum end-to-end delay, and the achieved delay has a notably decline as the number of FD nodes increases.

With artificial intelligence prevailing in intelligent transportation system, pavement crack detection with deep learning has aroused wide attentions in both academia and transportation sector. Nevertheless, it still remains a challenge to accomplish crack detection due to the complexity in pavement background. Motivated by latest advents in computer vision research, a fractional integral-based filtering method is advocated to remove pavement noise, and a fractal dimension estimation method has also emerged to present shape feature at pixel level, with the multi-scale feature architecture. Therefore, we try to propose a deep learning method, integrating fractional integral with fractal dimension, for crack detection in transportation service management. Firstly, the crack image is taken as input in the bottom-up architecture to extract fractal dimension on multi-scale levels, and a per-level feature unit is built to incorporate maps to make context information flow. Secondly, after fed into a convolutional filter for dimension resizing, all the resized feature maps are next fused at each level to comprise a group network. Finally, extensive experiments are executed on different crack datasets, exhibiting that the proposed method surpasses existing cutting-edge ones in terms of both generalizability and accuracy, with the benefits from not only fractional integral filtering, but also multi-scale fractal dimension features.

Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.

As an emerging type of Internet of Things (IoT), Internet of Vehicles (IoV) denotes the vehicle network capable of supporting diverse types of intelligent services and has attracted great attention in the 5G era. In this study, we consider the multimedia content caching with multicast beamforming in IoV-based vehicular edge networks. First, we formulate a joint vehicle-to-vehicle- (V2V-) assisted clustering, caching, and multicasting optimization problem, to minimize the weighted sum of flow cost and power cost, subject to the quality-of-service (QoS) constraints for each multicast group. Then, with the two-timescale setup, the intractable and stochastic original problem is decoupled at separate timescales. More precisely, at the large timescale, we leverage the sample average approximation (SAA) technique to solve the joint V2V-assisted clustering and caching problem and then demonstrate the equivalence of optimal solutions between the original problem and its relaxed linear programming (LP) counterpart; and at the small timescale, we leverage the successive convex approximation (SCA) method to solve the nonconvex multicast beamforming problem, whereby a series of convex subproblems can be acquired, with the convergence also assured. Finally, simulations are conducted with different system parameters to show the effectiveness of the proposed algorithm, revealing that the network performance can benefit from not only the power saving from wireless multicast beamforming in vehicular networks but also the content caching among vehicles.

Capable of significantly reducing cell size and enhancing spatial reuse, network densification is shown to be one of the most dominant approaches to expand network capacity. Due to the scarcity of available spectrum resources, nevertheless, the over-deployment of network infrastructures, e.g., cellular base stations (BSs), would strengthen the inter-cell interference as well, thus in turn deteriorating the system performance. On this account, we investigate the performance of downlink cellular networks in terms of user coverage probability (CP) and network spatial throughput (ST), aiming to shed light on the limitation of network densification. Notably, it is shown that both CP and ST would be degraded and even diminish to be zero when BS density is sufficiently large, provided that practical antenna height difference (AHD) between BSs and users is involved to characterize pathloss. Moreover, the results also reveal that the increase of network ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and network performance, we further study the critical density, under which ST could be maximized under the CP constraint. Through a special case study, it follows that the critical density is inversely proportional to the square of AHD. The results in this work could provide helpful guideline towards the application of network densification in the next-generation wireless networks.

In this study, the authors study the downlink resource allocation optimisation in femtocell networks, to maximise the number of guaranteed users whose data rate requirements are fully met. The spectral access of femtocell networks is based on orthogonal frequency division multiple access. In their work, two challenges are solved. The first is the intractable inter‐cell interference coordination brought about by the transmission delay in backhaul connections, and the second is the incorporation of physical interference model into problem formulations. To solve these problems, the authors propose a novel two‐level resource allocation scheme, implemented in both radio resource management controller and femtocell base stations, based on the maximisation of the number of guaranteed users. Notably, their proposed scheme is efficient and requires low overhead. Simulation results show that the proposed scheme offers significant performance improvement in both the percentage of guaranteed users and spectrum spatial reuse over existing methods proposed in the literature.

Femtocells have emerged as an effective solution to enhance indoor coverage and improve system performance in cellular networks. However, the inter-cell interference (ICI) caused by the unplanned nature of femtocells considerably leads to the degradation in throughput of users with guaranteed performance (GP). Meanwhile, the existing resource allocation algorithms bring about high complexity and large overhead. By tracking channel variation as well as arrival and departure of users, we propose a hybrid centralized/distributed resource allocation algorithm to maximize the number of GP users, with lower complexity and smaller overhead. Simulation results show that the proposed algorithm can significantly improve the system performance compared to the existing algorithms such as Q-FCRA.