Satellite-enabled Internet of Things (IoT) services such as maritime sensing, aviation tracking, emergency telemetry, and wide-area monitoring require timely network-state awareness to support access control, load balancing, and resource scheduling. In Low Earth orbit (LEO) constellations, conventional resource-state reporting via ground stations is limited by short and intermittent contact windows, while large-scale LEO-to-LEO relaying is constrained by inter-satellite capacity and multi-hop latency. We investigate a LEO-medium Earth orbit (MEO) architecture where MEO satellites act as persistent aggregation nodes that collect resource-state updates from many IoT-serving LEO satellites over cross-orbit links. Due to spatially non-uniform IoT traffic and heterogeneous service rhythms, LEO satellites exhibit different resource-evolution time scales, making fixed-period sensing either waste signaling for slowly varying satellites or produce stale information for rapidly varying satellites. This creates a coupled trade-off between reporting delay and Age of Information (AoI), under limited LEO–MEO sensing capacity. To address this issue, we propose a rhythmic resource-state sensing framework that adapts each LEO satellite’s reporting cadence using a dynamic frame structure and a deep reinforcement learning policy trained by proximal policy optimization to minimize average reporting delay subject to AoI and sensing constraints. Simulations across constellation scales and traffic patterns show that the proposed approach reduces the average reporting delay by 22.5% on average and up to 25.5% compared with a heuristic baseline, while reducing the composite delay–freshness cost by 16.7% on average and up to 25.7%.

Low earth orbit Internet of things (LEO-IoT) networks offer global coverage and low-latency communication, making them attractive for large-scale IoT deployments. However, rapidly varying satellite connectivity and uneven, burst ground traffic lead to unstable routing performance, resulting in fluctuating delays and increased packet loss. To address these challenges, we propose a delay-aware routing optimization (DARO) algorithm that integrates traffic prediction and distributed control. A CNN-BiLSTM-Attention model is developed to capture spatial-temporal traffic patterns, enhancing the accuracy of dynamic traffic prediction. A closed-form end-to-end delay model is derived to characterize the effect of routing decisions on network latency. The routing problem is then formulated as a delay and packet loss minimization task and solved using multi-agent proximal policy optimization (MAPPO), enabling each satellite to adapt its routing strategy based on local observations and a shared critic. Simulation results show that DARO improves prediction accuracy by 19.05% to 76.39%, reduces packet loss by 27.35% to 90.76%, and lowers end-to-end delay by 2.43% to 56.04%, demonstrating its effectiveness in dynamic LEO-IoT environments.

Precisely estimating the propagation time error induced by complex terrains and meteorological factors during the propagation of the signal along the ground, also termed the additional secondary phase factor (ASF), is crucial to improve positioning accuracy in the eLoran system. Among all of the ASF estimation algorithms, the well-known kriging-based algorithms, a kind of methods that estimate the ASF only with limited ASF data, often suffer performance reduction, as the fixed basis functions adopted lack adaptability. In this paper, we propose a trend kriging algorithm, which dynamically selects basis functions to construct the optimal trend function, for estimating the ASF under limited ASF data. Specifically, we design an optimization framework that integrates the selection task of basis functions and the search task of the correlation parameter into a single problem for jointly handling these nested tasks. We then develop an objective function based on the weighted covariance model to ensure that the detrended data follows the spatial correlation feature. To tackle this problem with multiple local optima, we employ the niching differential evolution algorithm, a recently developed metaheuristic algorithm. The performance verification results based on the simulated and practical ASF data cautiously exhibit the superiority of our proposed algorithm. For example, based on the practical data, compared with the representative IDW, the OK, the UK 1, and the UK 2, our proposed algorithm reduces the root mean square error by 21.68%, 29.26%, 10.53%, and 23.45%, respectively.

The integration of blockchain technology with the sixth generation (6G) networks offers a promising approach to enhance the reliability and trustworthiness of industrial Internet of Things (IIoT) systems. Since IIoT devices typically lack the capability to directly participate in blockchain consensus, drone networks offer a viable alternative by providing dynamic coverage and reducing dependence on fixed infrastructure such as centralized servers. Sharding is an effective method to improve the scalability of blockchain systems, yet existing sharding schemes overlook the complexity and dynamic nature of drone network topologies. These networks frequently experience changes due to drone mobility, task variations, and energy constraints, all of which can disrupt consensus communications. To address these challenges, we propose a time-varying graph-based blockchain sharding scheme (BSTVG) tailored for large-scale drone blockchain networks. The time-varying graph-based model captures the temporal dynamics of drone communications. We adopt an improved K-Means++ clustering algorithm that incorporates communication conditions to adapt network sharding. Additionally, we develop mechanisms for intra-shard consensus and cross-shard transaction processing. To accommodate node joins, exits, and significant topological changes, we introduce a slot–epoch coupling mechanism that dynamically adjusts the epoch length. We analyze the security of the proposed scheme and validate its performance through simulations. Experimental results demonstrate that our scheme not only enhances the throughput but also reduces energy consumption of the drone blockchain network.

Satellite communication networks operate under stringent computational constraints and are susceptible to sophisticated cyberattacks. This paper introduces a novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution. In the long-term loop, the ground segment processes historical data to estimate key statistical parameters of the task environment. Additionally, we incorporate the time-varying characteristics of satellite wireless links to account for the dynamic communication context. In the short-term loop, the satellite employs a receding horizon optimization that models dynamic task arrivals and maximizes a utility function considering detection rates and resource costs. To counter intelligent adversaries interception, we introduce a deception mechanism using Bayesian persuasion theory. By strategically manipulating the short-term action sequences in the telemetry downlink, we mislead an external attacker’s beliefs. We mathematically model the attacker’s optimal response under channel uncertainty and demonstrate that our framework significantly reduces attacker utility. The approach’s effectiveness is formally proven using Lyapunov theory.

This paper investigates a multi-satellite cooperative covert satellite communication (SatCom) scheme, where a positive covert rate is achieved in ultra-dense low Earth orbit (LEO) satellite constellations by exploiting both interference and directional uncertainty. Specifically, interference arises from aggregate sidelobe leakage from other satellite transmissions, while directional uncertainty stems from the random selection of the transmitting satellite among multiple accessible ones. To this end, we first propose a LEO satellite network model and formulate the corresponding hypothesis testing problem for the cooperative covert SatCom scheme. The power distribution of the aggregate interference is quantified and approximated using stochastic geometry. Next, by analyzing the detection error probability and outage probability under the impact of aggregate interference, we derive an approximate covert capacity expression for the case of a single accessible satellite, which maintains a positive covert rate even as the slot length approaches infinity. Furthermore, by leveraging directional uncertainty through hiding the signal’s angle of arrival, we analyze the multi-satellite cooperative covert SatCom scheme, leading to a concise approximate expression that reveals significant covert capacity improvement. Numerical simulations are performed to verify the superiority of the proposed scheme, suggesting that a positive covert capacity can be achieved with interference uncertainty and significantly enhanced by directional uncertainty as the number of satellites increases.

Motivated by the practical application of beam tracking of multiple devices in Multiple Input Multiple Output (MIMO) communication, we study the problem of non-adaptive twenty questions estimation for locating and tracking multiple moving targets under a query-dependent noisy channel. Specifically, we derive a non-asymptotic bound and a second-order asymptotic bound on resolution for optimal query procedures and provide numerical examples to illustrate our results. In particular, we demonstrate that the bound is achieved by a state estimator that thresholds the mutual information density over possible target locations. This single threshold decoding rule has reduced the computational complexity compared to the multiple threshold scheme proposed for locating multiple stationary targets (Zhou, Bai and Hero, TIT 2022). We discuss two special cases of our setting: the case with unknown initial location and known velocity, and the case with known initial location and unknown velocity. Both cases share the same theoretical benchmark that applies to stationary multiple target search in Zhou, Bai and Hero (TIT 2022) while the known initial location case is close to the theoretical benchmark for stationary target search when the maximal speed is inversely proportional to the number of queries. We also generalize our results to account for a piecewise constant velocity model introduced in Zhou and Hero (TIT 2023), where targets change velocity periodically. Finally, we illustrate our proposed algorithm for the application of beam tracking of multiple mobile transmitters in a 5G wireless network.

Low Earth Orbit (LEO) satellites are playing an important role in earth observation, providing valuable images for training machine learning (ML) models used in tasks such as environmental monitoring and pattern recognition. However, due to unstable communication links and limited downlink bandwidth, it is economically impractical to transmit all raw images to ground stations (GSs) for model training. Federated learning (FL), a privacy-preserving distributed machine learning method, can reduce the communication overhead by exchanging model parameters. Generally, FL needs a fixed central server to aggregate the global model, which is challenging in LEO satellite networks, given the dynamic nature of satellite orbits. To overcome this, we propose a decentralized federated learning (DFL) framework that enables efficient model aggregation through satellite collaboration. Specifically, the proposed framework, named FedHRA, is based on model-agnostic meta-learning (MAML), which jointly optimizes hyperparameters and resource allocation to mitigate straggler effect and address statistical heterogeneity. Extensive numerical results on MNIST and CIFAR-10 datasets demonstrate that FedHRA achieves shorter learning time and higher model accuracy compared to the benchmark frameworks.

With the exponential growth of smart devices connected to wireless networks, data production is increasing rapidly, requiring machine learning (ML) techniques to unlock its value. However, the centralized ML paradigm raises concerns over communication overhead and privacy. Federated learning (FL) offers an alternative at the network edge, but practical deployment in wireless networks remains challenging. This paper proposes a lightweight FL (LTFL) framework integrating wireless transmission power control, model pruning, and gradient quantization. We derive a closed-form expression of the FL convergence gap, considering transmission error, model pruning error, and gradient quantization error. Based on these insights, we formulate an optimization problem to minimize the convergence gap while meeting delay and energy constraints. To solve the non-convex problem efficiently, we derive closed-form solutions for the optimal model pruning ratio and gradient quantization level, and employ Bayesian optimization for transmission power control. Extensive experiments on real-world datasets show that LTFL outperforms state-of-the-art schemes.

Low Earth Orbit (LEO) satellite networks are expected to become a key component of Sixth Generation (6G) communication networks, to relieve the communication burden on ground networks. Driven by the rapid advancement of communication technologies and intelligent applications, dense traffic flow in the Internet of Vehicles (IoV) inevitably leads to a surge in task generation and an increased demand for network resources. The requirement for low latency further intensifies this challenge, making it difficult to rely solely on ground network resources to process tasks efficiently and promptly. Conversely, relying only on satellite networks for task processing results in high costs. Therefore, flexibly integrating LEO satellite links based on real-time traffic conditions, task demands, and the real-time state of ground network resources becomes an effective solution. However, achieving such a goal poses significant challenges in efficient allocation and balance between ground and LEO satellite network resources. Therefore, we propose a dynamic multi-task partial offloading algorithm based on LEO satellite-ground network collaboration to efficiently allocate resources between ground and satellite networks in real time. We first introduce the utility gain as a metric to evaluate task scheduling preference and design an improved iterative algorithm to jointly optimize the offloading ratio and channel allocation to maximize system utility. Finally, based on the real-world dataset of Shanghai (China), we demonstrate the significant advantages of the proposed strategy over representative methods in terms of delay, vehicle satisfaction, and system utility.

Autonomous aerial vehicle (AAV) target tracking technology is an essential component for enabling diverse low-altitude activities. Due to the constraints on energy and computing resources of AAVs, current approaches face challenges in balancing prolonged flight duration with precise tracking while avoiding high computational complexity. Therefore, this paper proposes an energy-aware formation control algorithm for multiple AAVs to cooperatively track a target while retaining a desired formation pattern. Firstly, to achieve a balanced outcome in terms of tracking performance and control effort, an actor-critic based learning predictive rule is explored to develop a near-optimal control protocol that stabilizes error dynamics and minimizes value functions for discrete-time AAV systems. By decomposing the infinite-horizon target tracking problem into a sequence of finite-horizon sub-problems, the reinforcement learning (RL)-based predictive control algorithm can achieve fast convergence in approximating the solution of Hamilton-Jacobi-Bellman (HJB) equation. Furthermore, by employing a delicately designed asynchronous policy iteration mechanism with adjustable learning intervals in RL, the cumbersome learning process can be effectively mitigated, thereby attaining both high learning efficiency and a reduced computational burden simultaneously. The involved errors are proven to be convergent and simulation results validate the optimality of our method.

With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements. The Space Computing Power Network (Space-CPN) offers a promising solution by providing onboard computing and extensive coverage capabilities for real-time inference. This paper presents a remote sensing artificial intelligence applications deployment framework designed for Low Earth Orbit satellite constellations to achieve real-time inference performance. The framework employs the microservice architecture, decomposing monolithic inference tasks into reusable, independent modules to address high latency and resource heterogeneity. This distributed approach enables optimized microservice deployment, minimizing resource utilization while meeting quality of service and functional requirements. We introduce Robust Optimization to the deployment problem to address data uncertainty. Additionally, we model the Robust Optimization problem as a Partially Observable Markov Decision Process and propose a robust reinforcement learning algorithm to handle the semi-infinite Quality of Service constraints. Our approach yields sub-optimal solutions that minimize accuracy loss while maintaining acceptable computational costs. Simulation results demonstrate the effectiveness of our framework.

Space-air-ground integrated networks (SAGINs) augmented with mobile edge computing (MEC) provide a unified yet heterogeneous substrate for latency-sensitive services. Deploying service function chains (SFCs) over satellites, aerial platforms, and ground nodes, however, is difficult due to hierarchical resource heterogeneity, time-varying network states, and stringent end-to-end (E2E) delay requirements. In this paper, we study online SFC embedding in a three-layer SAGIN-MEC architecture under coupled computing and networking constraints. We model the deployment as a two-stage process: (i) placing each virtual network function (VNF) onto feasible nodes subject to computing-capacity constraints, and (ii) mapping inter-VNF traffic onto feasible paths subject to bandwidth and delay constraints. To achieve adaptive decisions under dynamic states, we cast the problem as graph reinforcement learning by jointly encoding the substrate topology and each SFC into a unified graph state, and propose a structure-aware PPO agent that combines graph convolution with domain features and an action-masking mechanism to eliminate infeasible placement/routing actions. Extensive experiments in dynamic large-scale scenarios show that the proposed method consistently outperforms competitive baselines, improving the acceptance rate by 6.991% under high load, reducing the average E2E delay by 13.556%, and increasing the long-term revenue-to-cost ratio by 27.763% on average.

Flying ad hoc networks (FANETs) offer flexible, real-time wireless communication solutions for multi-drone systems by utilizing drones as network routers. However, FANETs’ unique characteristics, including high mobility, unstable network topology, and intermittent connectivity, pose significant challenges in designing efficient and reliable routing protocols. Traditional routing protocols for mobile ad hoc networks often fall short in highly dynamic airborne environments due to excessive control overhead, increased latency, and inefficient route maintenance. To address these issues, this paper proposes an enhanced on-demand predictive (EDP) routing protocol that integrates a neighbor coverage-based predictive flooding mechanism and an adaptive link quality-based route maintenance strategy. The flooding mechanism mitigates directional deafness by using a Kalman filter-based probabilistic forwarding model, while the route maintenance method optimizes path selection based on distance, traffic load, and link lifetime. Simulation results show that EDP significantly improves packet delivery rate, reduces network delay, and lowers overhead compared to benchmarks, making it well-suited for applications in FANETs.

With the explosive advancement of unmanned aerial vehicles (UAVs), the security of efficient UAV networks has become increasingly critical. Owing to the open nature of its communication environment, illegitimate malicious UAVs (MUs) can infer the position of the source UAV (SU) by analyzing received signals, thus compromising the SU location privacy. To protect the SU location privacy while ensuring efficient communication with legitimate receiving UAVs (RUs), we propose an Active Reconfigurable Intelligent Surface (ARIS)-assisted covert communication scheme based on virtual partitioning and artificial noise (AN). Specifically, we design a novel ARIS architecture integrated with an AN module. This architecture dynamically partitions its reflecting elements into multiple sub-regions: one subset is optimized to enhance communication between the SU and RUs, while the other subset generates AN to interfere with the localization of the SU by MUs. We first derive the Cramér-Rao Lower Bound (CRLB) for localization with received signal strength (RSS), based on which, we establish a joint optimization framework for communication enhancement and localization interference. Subsequently, we derive and validate the optimal ARIS partitioning and power allocation under average channel conditions. Finally, tailored optimization methods are proposed for the reflection precoding and AN design of the two partitions. Simulation results validate that, compared to baseline schemes, the proposed scheme significantly increases the localization error of MUs by approximately 37.65% with only a 3.69% reduction in the communication rate between the SU and RUs, thereby effectively protecting the SU location privacy.

To satisfy diverse security requirements of ground users in unmanned aerial vehicle (UAV) networks, we propose a channel inversion power control (CIPC) aided multi-user collaborative secret and covert uplink transmission strategy for UAV secure communication. Specifically, using the non-orthogonal multiple access (NOMA) technology, multiple ground covert users named Carlo, hide their weak covert signals in the strong secret signal from a secret user named Bob, and transmit to the UAV named Alice. An adversary Willie attempts to eavesdrop Bob’s confidential message and detect whether Carlo is transmitting or not. To evaluate the link reliability and security of secret and covert transmissions, we first derive closed-form expressions of the secret connection probability (SCP), secrecy outage probability (SOP), covert connection probability (CCP), and detection error probability (DEP) under perfect channel state information while accounting for the uncertainty of the adversary’s noise power. We then further incorporate the legitimate-link channel uncertainty into the analysis and characterize its impact on the key performance metrics, particularly the average values of SCP, SOP, and CCP. To characterize the theoretical benchmark of the proposed transmission strategy, we investigate the performance in both rotary-wing and fixed-wing UAV scenarios. Particularly, in the rotary-wing UAV scenario, we formulate an optimization problem to maximize the average effective sum covert rate subject to constraints of SCP, SOP, DEP, CIPC parameter, ground user’s transmission power, and the UAV’s altitude. Subsequently, we provide an optimal and a sub-optimal solution to the optimization problem. In the fixed-wing UAV scenario, we formulate an optimization problem to maximize the average covert rate subject to the constraints of SCP, SOP, DEP, CIPC parameter, user scheduling, and the UAV’s flight parameters. Furthermore, using the successive convex approximation (SCA) method, we propose an alternating optimization (AO) algorithm to obtain a high-quality feasible solution. Finally, our results reveal the influence of key parameters on the system performance, analytically and numerically.

This paper considers a passive intelligent reflecting surface (IRS)-assisted integrated radar and communication system for multi-target detection and multi-user communications. To balance the communication and sensing performance, we propose an alternating optimization algorithm to optimize the worst-case weighted sum of the radar waveform minimum mean square error (MSE) and Multiuser interference (MUI) in Communication, under the spectrum compatibility and power constraints. The proposed algorithm utilizes a novel Tchebycheff optimization framework that decomposes the multi-objective optimization problem into three subproblems by optimizing the radar transmitted sequences, communication transmitted sequences, and IRS phase configuration. We propose an alternating optimization algorithm which incorporates alternating direction penalty method (ADPM) and element-wise block coordinate descent (E-BCD) frameworks to efficiently solve the optimization problem. Extensive numerical simulations validate the effectiveness of the proposed method, demonstrating significant performance improvements in both minimizing radar MSE and communication MUI and better convergence speed.

Data-assisted channel estimation (DA-CE) faces unique challenges in Low Earth Orbit (LEO) scenarios with large Doppler, where phase distortion and pilot sparsity degrade the utility of data symbols for refinement. Moreover, myopic, context-agnostic reliability metrics often fail to identify data symbols that are truly useful for improving estimation accuracy. To resolve these challenges, we innovatively reformulate the data selection as a pixel-level utility masking problem, similar to semantic segmentation tasks, where each resource element (RE) is evaluated for its utility in channel refinement. We introduce an integrated framework rooted in physics-informed reinforcement learning (PIRL), addressed by two symbiotic components. First, a Physics-informed Subspace Basis Expansion Models-LMMSE (PiSBEM-LMMSE) algorithm acts as the perception layer of our framework, which yields a high-fidelity, physics-consistent state representation by expressing the dominant Doppler-induced dynamics via a low-rank complex-exponential basis and decoupling them from residual stochastic fading. Second, a lightweight U-Net-based deep reinforcement learning (DRL) agent, acting as the cognitive decision core, learns an optimal, context-aware masking policy upon this structured representation. The U-Net architecture, with its encoder-decoder structure and skip connections, is specifically chosen to process the multi-channel, image-like state representation, capturing both global channel dynamics and local perturbations. Extensive simulations demonstrate that our framework achieves significant performance improvements over state-of-the-art methods, exhibiting remarkable robustness in LEO scenarios where conventional approaches fail.

The flexible deployment of Unmanned Aerial Vehicle (UAV) swarms holds significant potential for low-altitude economy, but their communication security is severely threatened by malicious jamming. Generally, existing anti-jamming methods often overlook multi-user interference in swarm scenarios and fail to exploit the full potential of Intelligent Reflecting Surface (IRS) architectures. To solve the above challenges, we propose for the first time an anti-jamming framework for UAV swarm communications assisted by a Hybrid-IRS-assisted UAV (H-UAV). We jointly optimize the H-UAV’s trajectory, the hybrid IRS’s beamforming and active/passive element allocation of IRSs, and Non-Orthogonal Multiple Access (NOMA) communication strategy under imperfect jammer Channel State Information (CSI), to maximize average system transmission rate while minimizing communication energy consumption. To handle the formulated highly-coupled non-convex problem, we decompose it into three sub-problems. Specifically, we employ Successive Convex Approximation (SCA) to optimize the H-UAV’s trajectories. The IRS beamforming and element allocation are then transformed into a semi-definite programming problem by a designed penalty-based approach. Finally, the NOMA decoding order and power allocation are optimized via a dynamic ordering scheme and an SCA-based algorithm. Compared to existing representative schemes, the proposed framework can achieve higher average transmission rates and lower energy consumption.

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers. The results validate that the proposed framework consistently achieves superior performance compared to Proximal Policy Optimization (PPO), Soft Actor Critic (SAC), and ablated baselines in terms of reward, failure rate, and migration overhead, demonstrating the effectiveness of the proposed spatio-temporal modeling and hybrid reinforcement learning approach in dynamic satellite edge environments.

With the integrated development of global marine exploitation and 6G technology, building an all-domain marine wireless network has become crucial for supporting marine activities. However, the unique communication environment, varying collaboration of heterogeneous devices, and dynamic network changes pose technical bottlenecks for balancing real-time and efficient resource competition. To overcome those challenges, this paper proposes a novel integrated marine wireless network with multi-type unmanned device clusters across space-surface-submarine media. To address heterogeneous resource allocation, we consider channel capacity and device connection, modeling it as a three-sided matching framework with size constraints and cyclic preferences (TMSC). Building on this, we propose the satellite-prioritized restricted double-TMSC (SPR-DT) algorithm to solve optimal matching in quasi-static networks, aiming to maximize total backhaul revenue. To handle rapid dynamic network changes, we initialize the proximal policy optimization (PPO) with the stable solution of SPR-DT, thus addressing the challenge of acquiring real training data while accelerating algorithm convergence. Then, we propose a PPO-assisted multi-slot matching algorithm to enhance solution efficiency in large-scale dynamic scenarios. The simulation results show that the proposed algorithm achieves an optimal effect of 94.6% in quasistatic scenarios, with a complexity reduced to 3.2%. In dynamic scenarios, the results are 87.2% and 28.7%, respectively.

Advancements in artificial intelligence and low-earth orbit satellites have promoted the application of large remote sensing foundation models (FMs) for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large FMs, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links. To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time to 33% of on-board training time.

Due to the open nature of unmanned aerial vehicles (UAVs) communication, UAV applications face severe challenges in preserving location privacy. In open-space environments, illegitimate malicious nodes (MNs) can estimate the position of the source UAV (SU) through analysis of the signals they receive, which facilitates further attacks. Therefore, while ensuring efficient communication between UAVs, it is crucial to protect the location privacy of the SU. To address this issue, this work designs a scheme utilizing virtual partition of Active Reconfigurable Intelligent Surface (ARIS) to improve the communication rate of legitimate links while simultaneously reducing the localization accuracy of MNs with controllable artificial noise (AN) sources. Furthermore, we derive the Cramér-Rao Lower Bound (CRLB) for the illegitimate localization model based on received signal strength (RSS), and formulate the corresponding joint optimization problem. Finally, the optimal division of ARIS elements and power are derived. Meanwhile, we propose dedicated reflection matrix optimization algorithms for ARIS. Simulation results validate that the proposed scheme drastically reduces the localization accuracy of MNs, while preserving communication efficiency and reliability.

Cooperative multi-Unmanned Aerial Vehicle (UAV) systems for dynamic tasks, such as target tracking, face challenges in maintaining efficient coordination when agents become inactive upon task completion. This dynamic behavior introduces heterogeneous input streams to centralized state evaluation components (Critics) in multi-agent reinforcement learning frameworks, impairing coordination and increasing network resource demands, such as bandwidth and latency. This work proposes a novel Learned Inactive Feature Mixture-of- Experts (LIF-MoE) Critic to address the above issue, which jointly learns a compact inactive representation and applies expert-based specialization to diverse agent inputs. LIF-MoE replaces uninformative inactive observations with a learnable feature vector to provide meaningful representations for inactive states, while employing per-agent MoE processing with sparse routing to enable specialized handling of heterogeneous inputs. This approach enhances state representation for accurate value estimation, thus facilitating efficient coordination of the UAV swarms. Simulation results validate that LIF-MoE significantly improves task performance and reduces mission times compared to baselines, with pronounced advantages in complex scenarios.

Unmanned aerial vehicles (UAVs), which can leverage their integrated sensing, computation, and communication (ISCC) capabilities to enable distributed intelligence at the network edge, play a critical role in next-generation wireless networks. However, UAVs face significant challenges in decentralized model training, including limited computational resources, energy constraints, and inefficient communication. This paper introduces SFL-ISCC, a novel framework that combines model splitting with federated learning (FL)-supported UAV ISCC systems, designed to train a global machine learning (ML) model with minimal energy consumption across multiple UAVs. To our knowledge, this is the first attempt to integrate model splitting into FL-supported UAV ISCC systems. Within the framework, we first theoretically explore the effects of UAV deployment strategies, split layer selection, and client-side aggregation frequency on model convergence performance. Then, we formulate a joint optimization problem to minimize UAV energy consumption while guaranteeing target model convergence accuracy, and propose a low-complexity solution. Moreover, we incorporate semisupervised learning into the SFL-ISCC framework to handle the sparsity of labeled data in UAV networks. Experimental results show that our proposed scheme significantly outperforms baseline schemes in both energy efficiency and model convergence.

Recently, with the users' growing demand for communication rate and capacity in wireless networks, Unmanned Aerial Vehicles (UAVs) have attracted widespread attention due to their mobility, flexibility, and robust line-of-sight communication links. By equipping UAVs with multiple communication payloads, we can construct an aerial wireless network with three-dimensional coverage. However, due to the limitations of UAV onboard energy and communication resources, the lifetime and performance of UAV-assisted wireless networks are significantly constrained. This paper mainly focuses on equipping UAVs with mobile base stations to enhance wireless communication coverage and capacity. We propose a Joint Optimization of 3D Trajectory and Resource Allocation (JOTRA) scheme to maximize energy efficiency in complex scenarios with multi-user mobility and diverse requirements (e.g., UAV-assisted post-disaster search and rescue). Specifically, we apply Dinkelbach's iterative method and Block Coordinate Descent (BCD) method to solve the formulated multivariable and non-convex maximization problem. The algorithm's convergence has been analyzed. According to the simulation, the proposed algorithm can converge faster while maximizing energy efficiency in complex wireless communication scenarios.

Motivated by the practical application of beam tracking of multiple devices in Multiple Input Multiple Output (MIMO) communication, we study the problem of non-adaptive twenty questions estimation for locating and tracking multiple moving targets under a query-dependent noisy channel. Specifically, we derive a second-order asymptotic bound on resolution for optimal query procedures and provide numerical examples to illustrate our results. In particular, we demonstrate that a single threshold decoding rule achieves the asymptotic bound. The single threshold decoding rule has reduced the computational complexity compared to the multiple threshold method proposed for locating multiple stationary targets (Zhou, Bai and Hero, TIT 2022). Finally, we illustrate our proposed algorithm for the application of beam tracking of multiple mobile transmitters in a 5G wireless network.

Semantic communication (SC), by compressing raw data at the semantic level, significantly improves the information entropy of transmitted data and is considered as one of the key enabling technologies for the next-generation communication. However, most current research underestimates the impact of channel interference on SC systems. As an innovative generative artificial intelligence technique, the diffusion model (DM) has demonstrated remarkable performance in image denoising and enhancement. In this paper, we focus on the effects of wireless channels on SC image transmission and propose an unmanned aerial vehicle (UAV)-enhanced SC framework, termed diffusion joint source-channel coding (D-JSCC). Initially, we deploy a ground-to-air SC system on UAVs, utilizing the aerial advantage to provide favorable channels. Subsequently, we employ DM for intelligent signal processing, adaptively denoising channel interferences and optimizing received images with respect to numerical errors and perceptual loss. The results show that DJSCC consistently exhibits superior performance across various metrics over different channel conditions.

Semantic communication (SC) is considered a critical technology for breaking through the Shannon limit and achieving low-latency, high-capacity 6 G transmission. However, previous SC systems have typically employed a symmetrical architecture to enhance data recovery capabilities, resulting in a strong coupling between the encoder and decoder. In this paper, we introduce a novel asymmetric SC system, termed masked joint source-channel coding (M-JSCC), which significantly enhances the encoder's versatility by allowing it to adapt to different decoder models tailored to specific task requirements. Moreover, we abandon traditional convolutional neural networks and adopt the innovative transformer to increase model capacity further. Additionally, we empower the model with data generation capabilities to combat interference and distortion during wireless transmission, achieving robust semantic transmission. As a result, extensive experiments verify that our M-JSCC achieves better semantic understanding and performance across various tasks and different channel conditions.

The integration of blockchain technology with unmanned aerial vehicles (UAVs) offers considerable potential, enhancing cybersecurity and driving innovation within the UAV industry. However, due to the dynamic nature of UAVs and limited resources, existing blockchain consensus technologies cannot be directly applied to UAVs. To this end, we propose a chainbased optimized blockchain consensus protocol designed for UAV ad hoc networks, which employs the particle swarm optimization (PSO) algorithm to optimize chain consensus. We design several sub-protocols to cope with malicious nodes in the UAV network, node changes during UAV missions, topology changes. Numerical results show that our protocol increases throughput, reduces communication overhead, and enhances operation efficiency in UAV networks.

In space-air-ground integrated systems, radar signal analysis is crucial for effective spectrum management. In recent years, time-frequency transforms (TFT) have gained significant attention for radar signal detection and identification. However, challenges such as cross-term interference and low signal-to-noise ratio (SNR) limit the effectiveness in multicomponent signal analysis. Therefore, this paper proposes a novel multitask learning-based TFT framework, named One-Stage TFT (OSTFT), which directly generates high-quality time-frequency representations (TFR) from raw in-phase and quadrature signals. OSTFT incorporates a generative network combined with classification and localization tasks to enhance feature extraction and image clarity. Experimental results demonstrate that OSTFT achieves superior performance in TFR quality and radar signal recognition, with an 81.4% detection rate using the You Only Look Once (YOLO) frame-work, outperforming existing TFT methods under various noise conditions. Compared to the best-performing TFR-denoising method, OSTFT improves the detection rate by 2.7%.

Recent advancements in space computing power networks, particularly the integration of onboard computing capabilities in Low Earth Orbit (LEO) satellites, have paved the way for federated learning (FL) in satellite networks. Despite its potential, satellite FL faces unique challenges, such as the dynamic nature of satellite networks and the instability of inter-orbit communication links, which complicate global model aggregation. To address these challenges, we explore FL over multi-layer satellite networks, incorporating LEO, Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO) satellites. Specifically, by modeling the dynamic network as a series of time-varying graph snapshots, we propose a novel topology-aware FL framework. To optimize the aggregation routing in the multi-layer satellite network, we leverage the directed minimum spanning tree (DMST) problem in graph theory and introduce a communication-efficient satellite aggregation routing algorithm (CESAR), which effectively reduces communication overhead and aggregation delays, ensuring efficient training and model updates across the satellite network. Extensive experimental results validate the efficacy of the proposed framework, demonstrating its potential to overcome the inherent challenges of satellite FL and significantly advance the capabilities of multi-layer satellite networks.

With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments.

As a complement to terrestrial networks, low-Earth orbit (LEO) satellite networks are promising to provide ubiquitous and continuous services. To accommodate the dynamic topology of LEO satellite networks and increasing traffic demands, this article proposes a distributed routing approach to optimize the end-to-end delay relying on the multiagent deep reinforcement learning (MADRL), where each satellite node is deployed with an autonomous agent and makes its real-time next-hop decisions independently with local observation information. To promote the inner cooperation between decentralized agents, a centralized training scheme with a unified load-balancing reward is utilized by adopting a novel multiagent Transformer-MIX architecture. Moreover, to derive a better decision for each agent, we design an attention-involved agent network to capture more hidden information, and a Transformer-based parameter recurrent mechanism to generate the joint action-value function is used to enhance a more stable training. The simulation results indicate that our proposed scheme achieves faster convergence and demonstrates superior performance across several key performance metrics compared to existing benchmark schemes. Specifically, when the intersatellite link (ISL) failure rate in the network reaches 18%, our scheme achieves a reduction in end-to-end delay by 13.6% and an increase in packet successful delivery rate by 5.4% compared to the benchmark schemes.

Autonomous aerial vehicles (AAVs), as the key Internet of Things (IoT) devices, play a dominant position in low-altitude environments. Semantic communication (SC), as the next-generation communication technology, serves as a bridge for surpassing the Shannon limit toward the 6G wireless network. Establishing air-ground SC to provide intelligent IoT services is a crucial initiative for building future smart cities. In this article, we propose an AAV-based SC framework, named diffusion joint source-channel coding (D-JSCC). Abandoning traditional convolutional neural networks, we use transformers as the backbone and innovatively incorporate the diffusion model (DM) for image enhancement, achieving an optimal balance between image distortion and human perception. To accurately capture the numerical and perceptual loss induced by wireless channels and seamlessly amalgamate the DM with SC, we integrate channel states as strong prior information to refine the sampling process. Furthermore, we employ the gradient guidance strategy, which counteracts the randomness of sampling ensuring high robustness in harsh communication conditions. Additionally, we strike a balance between performance and sampling steps, ensuring both efficient computation and high-quality image enhancement. Comprehensive experiments demonstrate the advantages of D-JSCC across different communication environments.

Unmanned Aerial Vehicles (UAVs) are critical in modern emergency response due to their high mobility. However, limited computing resources and energy supplies necessitate the use of UAV networks for collaborative inference. UAV intelligent tasks are often Deep Neural Networks (DNN)-based, with DNN partitioning enabling collaborative inference. However, executing DNN partitioning in a highly dynamic UAV network faces two challenges that have not been addressed in existing research: the time gap between the state sampling and the execution of the corresponding action based on that state, and the unknown trajectories in advance. The time gap requires predictive action decision-making. To address this, we model DNN partitioning and edge offloading with hybrid action decisions in dynamic, energy-harvesting UAV networks as a Predictive Markov Decision Process (P-MDP). The rapidly changing and previously unknown network topology significantly impacts channel and data transmission energy consumption, affecting DNN partitioning decisions. To better solve the action prediction problem, we use the Transformer module to extract motion features from recent time slots in the proposed Transformer-enhanced Multi-Agent Hybrid Action Proximal Policy Optimization (TE-MHAPPO) framework. Simulation results show that TE-MHAPPO reduces the reward which comprehensively considers task delay and energy consumption, by at least 12.1% compared to the state-of-theart MHAPPO. Additionally, its reward performance degradation with the increase in prediction time is at most 55.2% of that observed in the baseline.

As the adoption of satellite-enabled Internet of Things (IoT) continues to rise, its intricate multi-domain architecture becomes increasingly susceptible to cross-domain cyber threats. Attackers can exploit compromised IoT devices, inject malicious packets into data streams aggregated at the IoT gateway for satellite backhaul, and potentially endanger the satellite network during transmission by exploiting the hardware, software, and protocol vulnerabilities. Compared to single-domain defenses, cooperative defense at the IoT devices, IoT access network, and satellite transmission network provides fine-granularity defense against cross-domain intelligent attacks. However, quantifying cross-domain impacts and tilting incentive misalignment among different participants remain significant challenges, making systematic cooperative defense development a complex task. To address this, we develop a tripartite security game framework to characterize the impacts of attacks and defense methods across both the terrestrial and satellite domains. Leveraging this game model, we devise flow pricing to optimally motivate the IoT Network Operator (IoT-NO) to prevent malicious packet infiltration into the satellite domain. Subsequently, we propose efficient learning algorithms enabling both the IoT-NO to ascertain their ideal flow sampling strategies and the Satellite Service Provider (SAT-SP) to determine optimal flow pricing. The simulation results corroborate the effectiveness of the consolidated game in counteracting cross-domain cyber attacks and facilitating cooperative defense between the IoT-NO and the SAT-SP with non-aligned incentives.

Nongeostationary orbit (NGSO) constellations characterized by seamless coverage and high throughput have been envisioned as a promising solution to 6G, where beam hopping (BH) technique plays an indispensable role to achieve on-demand service. However, owing to the dynamic topology and uneven terrestrial traffic, how to effectively manage multisatellite time-frequency-spatial beam resources to match the heterogeneous demands is challenging for NGSO satellite operators. This article proposes a novel return-link BH scheduling framework for NGSO mega-constellations, where the ground resource management center periodically executes BH scheduling for users in Earth-fixed cells to improve the long-term average service satisfaction (ASS) while reducing the intersatellite handover. Specifically, we decouple the NP-hard scheduling problem into three subproblems, namely, user-satellite association problem, BH pattern design problem and resource block (RB) allocation problem. User-satellite association problem is solved by a low-complexity heuristic algorithm to reduce the handover and avoid intersatellite interference. Furthermore, a graph-based maximum weight method and an iterative scheme based on successive convex approximation are developed to obtain the BH patterns and RB allocations, respectively. Simulation results demonstrate that compared with other benchmarks, such as the best channel association and round-robin BH schemes, the proposed method can reduce the satellite load unevenness by 60% and improve the ASS by 35% while maintaining a low handover overhead.

Due to the outstanding maneuverability, unmanned aerial vehicles (UAVs) garner increasing applications in the Internet of Things (IoT), such as data collection, environmental monitoring, emergency communication, search and rescue, and autonomous exploration is the foundation of these missions which can obtain a prebuilt map automatically. However, current methods suffer from low efficiency. To address this, we propose a hierarchical exploration framework for UAVs with limited field-of-view (FOV) sensor, encompassing frontier and viewpoint generation, global coverage path planning, and active perception trajectory generation. First, we employ the random seeds frontier generation and anisotropic Gaussian sampling for environment information update, which can efficiently utilize sensor’s sensing range. Then, we design an appropriate heuristic function to represent the connection cost between different viewpoints and solve the global coverage path as a traveling salesman problem (TSP) to balance the long-term and short-term information gain. Moreover, active perception trajectory planning is proposed to enhance flight safety, smoothness, and exploration efficiency. Simulation and real-world scenario results indicate that the proposed method achieves higher efficiency in frontier generation and viewpoint sampling, and the difficulty of solving global coverage path does not significantly increase with the environment scale. Our proposed method improves exploration efficiency by 17%–27% compared to the state-of-the-art (SOTA) method.

By decoupling substrate resources, network virtualization (NV) is a promising solution for meeting diverse demands and ensuring differentiated Quality of Service (QoS). In particular, virtual network embedding (VNE) is a critical enabling technology that enhances the flexibility and scalability of network deployment by addressing the coupling of Internet processes and services. However, in the existing deep neural networks (DNNs)-based works, the closed-box nature DNNs limits the analysis, development, and improvement of systems. For example, in the Industrial Internet of Things (IIoT), there is a conflict between decision interpretability and the opacity of DNN-based methods. In recent times, interpretable deep learning (DL) represented by deep neuro fuzzy systems (DNFSs) combined with fuzzy inference has shown promising interpretability to further exploit the hidden value in the data. Motivated by this, we propose a DNFS-based VNE algorithm that aims to provide an interpretable NV scheme. Specifically, data-driven convolutional neural networks (CNNs) are used as fuzzy implication operators to compute the embedding probabilities of candidate substrate nodes through entailment operations. And, the identified fuzzy rule patterns are cached into the weights by forward computation and gradient back-propagation (BP). Moreover, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. In addition, the DNFS-driven five-block structure-based policy network serves as the agent for deep reinforcement learning (DRL), which optimizes VNE decision making through interaction with the environment. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by simulation experiments.

Emerging real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning. Federated learning (FL) enables on-device training without raw data sharing, yet remains challenging in resource-constrained environments due to energy-intensive computation and communication, as well as limited and non-i.i.d. local data. We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks. FedDPQ integrates diffusion-based data augmentation, model pruning, communication quantization, and transmission power control to enhance training efficiency. It expands local datasets using synthetic data, reduces computation through pruning, compresses updates via quantization, and mitigates transmission outages with adaptive power control. We further derive a closed-form energy–convergence model capturing the coupled impact of these components, and develop a Bayesian optimization (BO)-based algorithm to jointly tune data augmentation strategy, pruning ratio, quantization level, and power control. To the best of our knowledge, this is the first work to jointly optimize FL performance from the perspectives of data, computation, and communication under unreliable wireless conditions. Experiments on representative CV tasks show that FedDPQ achieves superior convergence speed and energy efficiency.

With the rapid development of on-board computing technology, on-orbit information processing has become a new direction for reducing service response delays and improving the quality of space-based information services. Especially in space-air–ground integrated applications in 6G networks, remote sensing image processing tasks are highly important because of their critical role in applications such as environmental monitoring and public safety. However, the fluctuations in data volume due to significant scene differences, along with the limitations in individual satellite capabilities caused by size and power constraints, present new challenges for on-orbit image processing. To address these challenges, we model a data-driven on-orbit resource scheduling problem for space-air-ground integrated networks based on distributionally robust optimization, aiming to minimize the average image processing delay. We first construct an ambiguity set based on the Wasserstein distance and the historical distribution of image data, which helps transform the original upper-bound expectation problem into an explicitly expressed mixed-integer nonlinear (MINLP) problem. Furthermore, to reduce complexity and expedite the solution process, we decouple the MINLP problem into three subproblems using the block coordinate descent method and designed an iterative solving algorithm. The numerical results demonstrate that our proposed method achieves better fitting accuracy than traditional methods and reduces the average image processing delay.

Integrated sensing and communication (ISAC) technology is a promising contender for the future Internet of Underwater Things (IoUT). However, the complexity of underwater acoustic (UWA) channels and the randomness of ISAC signals may pose challenges to underwater communication and sensing. To address this issue, this paper investigates a novel communication-assisted bi-static sensing scheme capable of facilitating underwater multi-node collaboration using orthogonal frequency division multiplexing (OFDM), which refers to as UWA-OFDM-ISAC. Moreover, two efficient receivers are designed based on compressed sensing to enhance communication and sensing performance. In this paper, we first portray the UWA-OFDM-ISAC system model and emphasize that the Doppler and symbols estimated at the communication side are beneficial in enhancing the bi-static sensing performance. To estimate doubly spread UWA channels, an orthogonal matching pursuit-based interference cancellation channel estimation method is developed, which decouples Doppler and delay in OFDM signals and significantly reduces the parameter search dimension. Furthermore, we propose an enhanced detection algorithm based on matching pursuit, which can exploit sparse multipath information of echoes to improve target detection performance under doubly spread channels. The detection probability is improved by more than 30% at the 10−2bit error rate level compared with the energy detector. Finally, simulation results illustrate the effectiveness of the proposed UWA-OFDM-ISAC and demonstrate that the designed receivers have significant advantages relative to various existing algorithms.

Autonomous exploration is a fundamental challenge for numerous applications of mobile robots. Traditional methods often lead to impractical and discontinuous trajectories, which may substantially deteriorate the exploration time. In this work, we propose a rapid autonomous exploration framework with a field-of-view (FOV) expansion mechanism. We present a 1-degree-of-freedom (DOF) FOV expansion mechanism, coupled with a frontier-gravitation FOV direction planning method to decouple the direction of the sensor's FOV from the robot velocity direction. Our approach includes a rapid frontier viewpoint generation method utilizing principal component analysis (PCA). Moreover, we introduce a sliding window travelling salesman problem (TSP) for global coverage path planning, incorporating an attenuation coefficient to increase the exploration priority of independent small frontiers and reduce revisit probability. Finally, compared to state-of-the-art (SOTA) approaches, our proposed mechanism and framework beneficially reduce exploration time by 30%-44% and enhance the continuity of the robot movement in both simulation and real-world scenarios.

Reconfigurable intelligent surfaces (RISs) have been shown effective in strengthening the physical layer security of wireless systems, and the two-timescale design was proposed to tackle the challenges in channel estimation and phase-shift control. However, existing maximum ratio transmission (MRT) based precoding design is not efficient in mitigating information leakage. To this end, this paper considers the performance analysis and two-timescale design for RIS-aided multiple-input single-output (MISO) secure communications with regularized zero-forcing (RZF) and zero-forcing (ZF) precoding, which is not available in the literature. The major challenges come from the two-hop channel and the inverse structure in the precoding matrix. By utilizing random matrix theory, we first evaluate the fundamental limits of the considered system by deriving a closed-form expression for the ergodic secrecy sum rate (ESSR). Then, we determine the optimal regularization factor of the RZF precoder and evaluate the ESSR over independent and identically distributed (i.i.d.) channels in the high SNR regime. The results indicate that when the number of reconfigurable elements at the RIS is overwhelmingly larger than that of transmit antennas and users, the ESSR of the two-hop channel approaches that of the single-hop channel. Based on the performance analysis, we propose a two-timescale algorithm to maximize the ESSR by optimizing the regularization factor of RZF and the phase shifts of the RIS alternatively. Simulation results validate the accuracy of the theoretical analysis and the effectiveness of the proposed algorithm.

Underwater target hunting (UTH) is a critical and complex mission involving the search, monitoring, and hunting of targets in an underwater environment. However, the unpredictable trajectories and flexibility of these targets, along with complex underwater environments, significantly impede the efficiency and success of traditional schemes that depend solely on unmanned underwater vehicles (UUVs). Consequently, this paper presents the “3U network”, a novel heterogeneous framework integrating unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and UUVs for UTH. Within this framework, a UAV searches and monitors the target, a USV acts as a communication relay, and a swarm of UUVs hunts the target. Moreover, to improve the timeliness of target search, we propose the age of information (AoI)-based UAV search strategy. Additionally, we construct a constrained multi-objective optimization problem aiming to minimize energy consumption and mission duration by optimizing vehicles' trajectories, considering mobility limitations, safety, and connectivity constraints. To tackle this problem, we design an AoI- and energy-aware deep reinforcement learning (DRL) algorithm to optimize control policies for heterogeneous vehicles. The experimental results demonstrate that the proposed scheme outperforms the baseline schemes in terms of energy consumption, and mission duration and success rates.

The integrated satellite-terrestrial network with cascaded downlinks from satellites to wide-area mobile base stations and subsequently to terrestrial users enables global communication for terrestrial 4G/5G cellular users and is widely used in emergency rescue scenarios. However, in this network, satellites and wide-area mobile base stations are controlled by distinct resource scheduling systems with disparate packet queues, which means resources allocated by the satellite to the wide-area mobile base stations may not match the resources allocated by the wide-area mobile base stations to the terrestrial users, leading to coordination inefficiencies and resource wastage. To tackle this challenge, a resource collaborative scheduling mechanism based on cooperative game theory for cascaded downlinks is established, which effectively adapts to distinct resource scheduling systems with various QoS constraints. Then, the utility function of the Nash product is converted into a max-min problem, and a convex transformation method is proposed for the non-convex optimization problem. Simulation results demonstrate that the proposed collaborative scheduling mechanism effectively improves resource utilization and the transmission rate of cascaded downlinks.

Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet of Things (IoT) devices, but lacking terrestrial communication infrastructure. The massive data is conventionally transferred to the cloud server for centralized artificial intelligence (AI) models training, raising huge communication overhead and privacy concerns. To address this, we propose a hierarchical learning and computing framework, which leverages the low-latency characteristic of low-earth-orbit (LEO) satellites and the global coverage of geostationary-earth-orbit (GEO) satellites, to provide global aggregation services for locally trained models on ground IoT devices. Due to the time-varying nature of satellite network topology and the energy constraints of LEO satellites, efficiently aggregating the received local models from ground devices on LEO satellites is highly challenging. By leveraging the predictability of inter-satellite connectivity, modeling the space network as a directed graph, we formulate a network energy minimization problem for model aggregation, which turns out to be aDirected Steiner Tree (DST)problem. We propose a topology-aware energy-efficient routing (TAEER) algorithm to solve theDSTproblem by finding a minimum spanning arborescence on a substitute directed graph. Extensive simulations under real-world space-ground integrated network settings demonstrate that the proposed TAEER algorithm significantly reduces energy consumption and outperforms benchmarks.

With the growth of the magnitude of multiagent networks, distributed optimization holds considerable significance within complex systems. Convergence, a pivotal goal in this domain, is contingent upon the analysis of infinite products of stochastic matrices (IPSMs). In this work, the convergence properties of inhomogeneous IPSMs are investigated. The convergence rate of inhomogeneous IPSMs toward an absolute probability sequence $\pi $ is derived. We also show that the convergence rate is nearly exponential, which coincides with existing results on ergodic chains. The methodology employed relies on delineating the interrelations among Sarymsakov matrices, scrambling matrices, and positive-column matrices. Based on the theoretical results on inhomogeneous IPSMs, we propose a decentralized projected subgradient method for time-varying multiagent systems with graph-related stretches in (sub)gradient descent directions. The convergence of the proposed method is established for convex objective functions and extended to nonconvex objectives that satisfy Polyak-Lojasiewicz (PL) conditions. To corroborate the theoretical findings, we conduct numerical simulations, aligning the outcomes with the established theoretical framework.

Adaptive bitrate (ABR) streaming provides an effective way to improve the Quality of Experience (QoE) of video users and is now the de facto standard for video delivery. Meanwhile, mobile edge computing (MEC) has been applied to assist ABR streaming, improving the performance of mobile networks and enabling efficient video delivery. However, smooth ABR streaming relies on the bidirectional adaptation between bitrate selection and bandwidth allocation, as they operate on distinct timescales and have different optimization goals. Moreover, since the constrained wireless resources available within a cell are shared by multiple users, their QoE should be optimized not only jointly but fairly. To this end, we propose a QoE-fairness-aware bandwidth allocation (QFA-BA) method for MEC-assisted ABR video transmission. With a novel perspective on buffer occupancy modeling, the relationship between bitrate selection and bandwidth allocation is studied. An enhanced QoE evaluation model is then proposed to correlate bitrate selection with bandwidth allocation and facilitate QFA-BA. Finally, a soft actor-critic (SAC) framework improving both the QoE and QoE-fairness is presented for QFA-BA. Compared with the state-of-the-art methods, our QFA-BA can perceive fine-grained buffer occupancy and stabilize it near a preset value with relatively more and larger bitrate switchings, exhibiting smoother convergence, better QoE (50.29%) and QoE fairness (54.81%).

Federated learning (FL) has emerged recently as an appealing and promising technique to deal with distributed learning issues in the sixth generation (6G) communication systems. Recent studies focus on developing FL schemes for terrestrial radio networks, where the variation in transmission data rates caused by transmission distance changes is negligible over one communication round. However, this variation has considerable influences for underwater acoustic channels. In this paper, we propose an underwater dynamic federated learning (UDFL) scheme by jointly considering characteristics of underwater acoustic channels and moving behavior of autonomous underwater vehicles. Moreover, an energy consumption minimization problem is formulated based on the scheme. To meet the challenges of transmission latency and FL performances, we consider them separately and provide closed-form solutions to the two individual problems. Specifically, we theoretically characterize the connections between transmission power and FL performances, and derive the optimal transmission policy given transmission latency constraints. Based on the two solutions, a dynamic programming based online power control algorithm is proposed to determine the transmission power across all time slots. Numerical simulations are conducted to demonstrate that the designed scheme is effective and the proposed online algorithm can achieve latency constrained energy-efficient UDFL.

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments.

The Autonomous Underwater Vehicle (AUV)- assisted Underwater Internet of Things (UIoT) has received much attention due to its potential to develop marine resources with big data analysis. Given the low data rates and distributed data, decentralized federated learning (DFL) emerges as a promising avenue, enabling artificial intelligence integration and collaborative training within the underwater environment. In this paper, we combine DFL with the underwater scenario for the first time. A novel DFL algorithm with decaying aggregation weight is proposed to achieve fast convergence rate under non-independent and identically distributed (non-IID) data. In addition, the DFL algorithm is tailored to underwater acoustic channels, and we integrate adaptive compression, device connectivity, and time-varying topology considerations to enable practical deployment. We provide convergence analysis under convexity and connectivity assumptions. Simulation experiments validate the performance using the MNIST dataset, highlighting its effectiveness for practical UIoT applications.

Recently, Federated Learning (FL) has revolutionized the processing and analysis of vast volumes of data generated by wireless devices, effectively overcoming the traditional cloud computing constraints within Internet of Things (IoT) networks. However, practical challenges arise as data on edge devices dynamically changes, necessitating continuous learning capabilities known as Federated Continual Learning (FCL). One key challenge in FCL is the issue of catastrophic forgetting, which refers to preserving the training performance on old data while training on new data. While common strategies involve retaining a subset of old data to mitigate the issue, privacy concerns limit this approach, and the balance between emphasis on new and old data during the training process remains inadequately studied. To address the above challenges, we propose an Adaptive Federated Continual Learning (AdapFCL) method in heterogeneous environment, which eliminates the need for episodic memory in federated settings. Specifically, the server employs a Deep Convolutional Generative Adversarial Network (DCGAN) model with a data-free knowledge distillation technique, which enables the server to learn representations of old data and generate synthetic data involving only global model. Then clients perform local training by utilizing new data and synthetic data instead of storing old data. Furthermore, we quantify the degree of forgetting on old data for each client, allowing for adaptive adjustment of emphasis weights for old and new data during the training process. Simulation results validate that the proposed method can achieve superior average test accuracy while maintaining communication efficiency compared with baselines, especially in highly heterogeneous data scenarios.

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time.

Under the advancement of emerging communication technologies, the utilization of Internet of Things (IoT) is progressively expanding across diverse domains. The age of information (AoI) stands as a crucial measurement in evaluating the efficiency of IoT networks. For efficient and reliable data collection, Unmanned aerial vehicle (UAV) have been extensively applied in IoT networks. However, the escalating number of sensor nodes and random data sampling mode within IoT networks have made it challenging for UAV trajectory planning with the constraint of energy consumption. In response to this challenge, our solution entails an attention-based actor-critic algorithm for multi-UAV path planning in a pre-clustered IoT network, which takes into account both the average AoI of clusters and the energy consumption of each UAV. The simulation outcomes validate that our algorithm achieves a trade-off between the information freshness and energy consumption in the multi-UAV data gathering scenario.

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite.

The rapid development of the Internet of Medical Things (IoMT) has brought about an enormous amount of healthcare data. Effectively and securely processing this sensitive data has become a significant challenge for green communication and privacy protection of the IoMT. As a decentralized learning framework, Federate learning (FL) enables model training without directly aggregating users' raw data, thus ensuring user privacy protection. Moreover, numerous studies have put forth various approaches to enhance the efficiency of FL by minimizing communication costs, yet they may not fully account for the unique characteristics of IoMT. Specifically, the efficiency and performance of model training are closely related to patient life and health. Meanwhile, existing research has indicated that reducing communication costs can result in a decline in training accuracy, which may be critical to patient health. Therefore, aimed at green communication and ensuring the model accuracy, we design a communication-efficient personalized federated learning framework, namely pFedCAS. Specifically, we introduce a control unit, which enables adaptive sparsity of local models, to reduce training costs. Furthermore, a selection unit based on communication quality is added into the global aggregation, which can select suitable clients for model updating. Simulation results validate that the proposed method can significantly reduce communication costs while ensuring the model accuracy. Additionally, The simulation results also validate the excellent robustness of our method to non-iid healthcare data.

This paper focuses on the design of intelligent game strategy for multi-autonomous underwater vehicle (multi-AUV) underwater network system. The challenge lies in ensuring the coordination and stability between AUVs in complex underwater environments. To meet underwater data sharing requirements, we formulate an intelligent game strategy incorporating communication delays by formulating the problem as a partially observable Markov decision process (POMDP). Additionally, to address the issue of sparse rewards during exploration in multi-agent reinforcement learning (MARL) models and improve the coordination among AUVs, we propose a heuristic and artificial potential field (APF)-assisted multi-agent proximal policy optimization (HA-MAPPO) algorithm. Our proposed scheme addresses the issue of sparse rewards in MARL by using APF as path planner and subsequently utilizes heuristic algorithm for task scheduling to achieve optimal goal allocation. Simulation results demonstrate that our proposed HA-MAPPO algorithm outperforms current mainstream MARL algorithms regarding convergence speed while maximizing the winning rates.

This paper investigates covert communication in ultra-dense low Earth orbit (LEO) satellite systems, where the uncertainty of the aggregated interference formed by sidelobe leakages of satellite transmissions can be exploited to hide wireless signals from being detected by a warden. Specifically, we first propose a LEO satellite network model and the corresponding hypothesis testing problem for the covert satellite communication scheme, where the power distribution of the aggregated interference is quantified and then approximated by using stochastic geometry. After that, to analyze the impact of the aggregated interference, the covert transmission problem under constraints of the detection probability at Willie and the outage probability at Bob is formulated, where the achievable covert capacity is analyzed by considering different number of satellites N. Finally, numerical simulations are performed to verify the derived analytical results, which demonstrate that the covert capacity initially increases as$N$increases, while it becomes approximately proportional to$1/\sqrt{N}$for sufficiently large N.

Autonomous underwater vehicles (AUVs) play a crucial role in data collection for underwater acoustic sensor networks (UWASNs). The limited capacity of individual AUV and the need for low-latency data collection necessitate the deployment of AUV swarms to achieve efficient and secure cooperative data collection. However, most existing works assume prior knowledge of sensor node locations, which is impractical in real-world AUV networks. Additionally, continuous data collection needs to be considered due to the sustained operation of sensors and cluster head replacement. To address these challenges, we propose a target uncertainty map assisted data collection scheme for AUV swarms based on the multiagent proximal policy optimization (MAPPO) algorithm. Specifically, the target uncertainty map is established by leveraging current and past search and collection results, guiding the AUV swarm to prioritize areas with higher probabilities of containing sensor nodes. Moreover, a digital pheromone mechanism incorporating repulsive and attractive pheromones is designed to establish an artificial potential field for adjusting the target uncertainty map. To further enable a comprehensive exploration of unknown environments, we introduce the Age of Information (AoI) as an indicator. Additionally, we consider the energy consumption associated with data collection to strike a balance between collection and energy efficiency, and derive a lower bound on the policy improvement achieved by the MAPPO algorithm. Simulation results have validated that the proposed scheme has a superior performance compared to the baselines, achieving an approximately 15% increase in the collection rate while reducing the energy consumption of data collection and AoI as well.

Efficient data processing is crucial for industrial Internet of Things (IIoT) applications, but the limited energy and computing resources in IIoT devices (IIoT-Ds) pose constraints. This article utilizes a unmanned aerial vehicle (UAV) as a computing server for enhanced IIoT mission execution. Specifically, the energy consumption of IIoT-Ds and the UAV, as well as the weighted cost of the communication and computing scheduling strategy in the UAV-aided IIoT, are jointly taken into account. An optimization problem based on the system energy consumption is built under the constraints of UAV motion, computing offloading, and transmitting power allocation. A problem decoupling-based alternating optimization method is proposed to solve the minimization problem by decomposing it into three subproblems: 1) UAV motion optimization; 2) computing offloading configuration; and 3) transmitting power allocation. Through comparing the proposed communication and computing scheduling strategy with existing methods, simulation results illustrate its attainment of quasi-optimal performance, thereby validating the effectiveness of the alternating optimization method.

The efficient and secure management of resources within flying ad-hoc networks (FANETs) poses formidable challenges. FANETs constitute a pivotal element of the space-air–ground-integrated network (SAGIN), employing network virtualization (NV) technology in tandem with service function chain (SFC) to facilitate end-to-end network services, akin to terrestrial networks. Nonetheless, the transient, dynamic nature of FANETs coupled with their susceptibility to network attacks engenders considerable complexity in the placement of SFCs within these networks. To address the rationality and security of resource allocation for SFC placement, this article proposes a reinforcement learning algorithm that sets strict security-level restrictions on the placement process and fully extracts the key features in FANETs. Additionally, a multilayer policy network is devised to dynamically perceive alterations in the FANET environment and compute an optimal SFC placement strategy. The proposed algorithm exhibits real-time adaptability to the dynamic environment, quantifies influential factors during placement, and achieves dynamic SFC placement. To assess the efficacy of the algorithm, three evaluation metrics—namely, SFC placement success rate, long-term average revenue, and long-term revenue cost ratio—are formulated and extensively evaluated through a plethora of experiments. Comparative analysis against alternative algorithms demonstrates enhancements of 20.6%, 15.3%, and 12.1% in the aforementioned metrics, respectively. The experimental findings substantiate both the convergence and efficiency of the proposed algorithm.

Federated learning (FL), utilizing data from the edge devices (EDs) while protecting user privacy has gained much attention. Its efficacy is substantially influenced by both the quantity of connected devices and the quality of wireless communications. Network congestion, resulting from multiple access and signal attenuation caused by physical obstacles may severely impact the convergence of the FL model. To address these issues, this article employs nonorthogonal multiple access (NOMA) for uplink transmission and designs a two-tier FL framework consisting of ground devices and unmanned aerial vehicles (UAVs) to ensure the construction of Line of Sight (LoS) channels from EDs to the base station. Moreover, we construct a multiobjective joint optimization problem to minimize the FL convergence time considering constraints, such as the NOMA uplink latency, ED selection strategy, local training latency, and energy consumption. We also deduce the theoretical upper bound of the convergence time and transform the proposed multiobjective problem into a solvable form by eliminating the discrete variables determined by the ED selection. In turn, we utilize the proximal policy optimization (PPO) algorithm to solve this optimization problem. Finally, the extensive experimental results demonstrate the advantages of our proposed algorithm in terms of latency and energy consumption, while yielding a high robustness and scalability.

In recent years, unmanned aerial vehicles (UAVs) are considered to be integrated into wireless communication systems because of their tremendous advantages in mobility, cost, maneuverability, etc. In some real UAV-assisted communication scenarios, the dynamics of the environment, such as the roaming of served users, make it hard to obtain an optimal trajectory before the UAV is dispatched. Implanting an intelligent control policy into UAVs for distributed task execution is necessary to complete the task. In this paper, a UAV trajectory design problem is investigated for an orthorgonal-frequency-division-multiplexing (OFDM) wireless sensor network, which is dynamic because mobile sensors may randomly roam within a certain range. The UAV is expected to balance task efficiency with the safety constraint with a pre-trained onboard control policy. Compared to prior works, this work requires the policy to adapt to randomly generated obstacle maps, and also assumes that the UAV has no prior knowledge of the obstacles before it is dispatched, which brings about challenges to the problem. The motivation comes from adversarial environments without the specific obstacle distribution beforehand, such as a disaster area. The problem is formulated as a constrained Markov decision process (CMDP) model, which incorporates the safety constraint compared to basic MDP. Due to the assumption of randomized obstacle distribution and lack of prior knowledge, existing algorithms for CMDP can not be applied directly. To tackle this issue, we enhance reinforcement learning (RL) algorithm with a safety control mechanism to derive our novel safe reinforcement learning (Safe RL) algorithm, which is based on the framework of Lagrangian method. Compared to former algorithms about CMDP, our algorithm eliminates the premise that the safety model is known, the agent is able to learn safety judgement from scratch through its interactions with the environment. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithm under the problem’s setup.

Autonomous underwater vehicles (AUVs) can function as sensor nodes in Internet of Underwater Things (IoUT), contributing to ocean exploration and monitoring by collecting and transmitting data to the base station. Most of the routing algorithms applied to IoUT require the participation of stationary nodes and seldom consider the fluctuations of network topology, which cannot be directly applied to the IoUT composed of AUVs. The focus of this research is to examine the problem of packet routing in a dynamic AUV-assisted IoUT, with the ultimate goal of ensuring the effective transmission of underwater information. We analyze the transmission pattern of underwater acoustic signals and the consequent communication disruption between AUVs, which helps establish the Age of Information (AoI) and bit error rate (BER) of data through modeling. A routing algorithm that utilizes the branch-and-bound (BB) technique has been suggested, alongside the introduction of the Value of information (VoI) to enable the joint optimization of the AoI and BER. We describe two nearly optimal heuristic algorithms for networks with a high number of AUVs. The AFA-ACO-BB strategy is designed based on the above algorithms and the influence of AUV motion on link reliability is considered. Moreover, we have developed a power regulation mechanism that can effectively minimize the occurrence of network packet loss and energy waste. The simulation results demonstrate that the proposed scheme outperforms certain classically related schemes in terms of AoI and BER, while simultaneously maintaining superior packet loss rate (PLR) and energy consumption.

In recent years, the Internet of Underwater Things (IoUT) has garnered significant attention owing to its potential in ocean exploration and monitoring. However, environmental erosion and limited energy can cause node failures, leading to routing voids, communication congestion, and even IoUT breakdowns. Addressing these challenges, this work considers a node repair scheme for multiple autonomous underwater vehicles (AUVs) to search and repair faulty nodes to ensure the stable operation of the IoUT networks. Moreover, AUVs should adapt automatically to the unknown environment, working in cooperative or separative modes to balance repair efficiency and coverage. We propose a multiagent reinforcement learning-based AUV-assisted node repair (RANR) scheme, which considers limited underwater communication and scheduling between AUVs. To further enhance work efficiency, we introduce area information entropy to reduce redundant coverage among AUVs. Simulation results demonstrate that the RANR scheme is highly applicable to different working conditions.

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from “compute-after-communicate” to “compute-when-communicate”. By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.

As an alternative positioning, navigation and timing (PNT) method, integrated Doppler positioning is important in emerging direct satellite-to-phone communication and satellite-based remote Internet of Things (IoT) systems to help locate ground terminals. In this work, we offer a fundamental characterization of Doppler positioning by proposing a new scalable and analytical Doppler positioning performance bound that can be used for fast estimation of the Doppler positioning dilution in place of the legacy position dilution of precision (PDOP) expression. Then, to maximize the Doppler estimation accuracy, a Doppler estimation algorithm based on the whole signal packet is proposed, in which the Doppler rate is also considered. Finally, based on mathematical analysis, a Doppler positioning receiver architecture is proposed. Simulations are conducted with a 288-satellite Walker-$\delta ~800$km low-Earth-orbit (LEO) satellite constellation to corroborate the mathematical analysis. The results show that the median relative error of the proposed performance bound for Doppler positioning is less than 2.5% and that the proposed Doppler estimation algorithm outperforms other Doppler estimation algorithms for Doppler positioning and approaches the Cramér-Rao lower bound (CRLB).

Virtual network embedding (VNE) refers to the process of mapping virtual networks onto physical networks, which can improve the utilization and flexibility of network resources. However, due to the complexity of VNE problems and the requirement for network security, how to efficiently complete VNE and ensure network security is important. In this article, we analyze the characteristics of the cloud–edge–terminal collaborative vehicular network and design a resource allocation mechanism based on VNEVNE, abbreviated as DRLS-VNE. First, we establish a multidimensional heterogeneous network model and design a five-layer neural network as the deep reinforcement learning (DRL) agent. The DRL agent can adaptively extract network feature attributes, thereby improving the performance of DRLS-VNE. Second, we introduce a dynamic trust evaluation mechanism, which can evaluate the trustworthiness of each node in the virtual network in real time and set embedding constraints based on the evaluation results. Finally, we conduct experiments to verify the practicality and effectiveness of DRLS-VNE. The experimental results show that our solution can significantly enhance the performance of the VNE solution.

The emergence of integrated positioning, communication, and computing (IPC2) technology has paved the way for advanced capabilities in physical-digital spatial positioning, intelligent communication, and computing. This article delves into an in-depth exploration of a federated learning-assisted multidimensionality fusion IPC2 system. Within this system, edge nodes collaboratively harness their locally distributed multidimensionality positioning and communication data to coordinate edge computing resources for model training. Throughout the process of fully distributed collaborative training, we focus on addressing two specific security concerns: 1) data tampering and 2) model tampering attacks. In pursuit of bolstering the system’s resilience against potential attacks, we introduce a novel federated-blockchain edge learning (FLBC) framework. This framework capitalizes on the inherent features of the blockchain, namely, its nontampering and traceability attributes. In addition, we present a meticulously designed verification algorithm tailored for the parameters aggregation process. Specifically, an aggregation algorithm is developed to enhance the efficiency and accuracy of the training model’s fitting. To assess the effectiveness of our proposed approach, comprehensive simulations are conducted using an openly accessible wireless artificial intelligence (AI) data set. The outcomes of these simulations clearly demonstrate that the proposed scheme adeptly combats data tampering attacks initiated by multiple malicious nodes and high-intensity model tampering attacks, all while maintaining minimal accuracy loss.

With the rapid development of cloud-edge–terminal (CET) technology, ubiquitous sensing devices are able to collaborate with edge terminals, enabling real-time, intelligent environmental awareness. For device-free sensing systems, the number of each human gesture category may vary (class imbalance), which makes previously distributed device-free sensing algorithms ineffective. In this article, we propose a novel monitoring scheme for device-free human action sensing for CET collaboration under class-imbalance conditions. Specifically, the body-coordinated velocity profile (BVP) features of wireless fidelity (WiFi) signals are used to detect human actions. To recognize human gestures, we develop a convolutional neural network (CNN) using a monitor to detect gradient changes under class imbalance. To mitigate the effects of class imbalance, a corresponding correction is applied to the loss function. To validate the effectiveness of the proposed method, we conduct numerical experiments under class-imbalance conditions. Different parameter settings and proportions of participating nodes are explored for their effects on experimental results. Additionally, numerical experiment results demonstrate that the proposed method improves recognition accuracy by 3.85%–34.1% compared to baseline algorithms. Overall, the proposed method addresses the challenge of distributed device-free sensing under class-imbalance conditions and achieves superior recognition accuracy performance.

In this paper, we investigate delay-optimized edge caching in the integrated satellite-terrestrial network with diverse content popularity distribution and user access modes. Based on the cooperation among the base stations, the satellite and the gateway, we propose a three-layer caching architecture to provide content service for both base station access users and satellite access users. Considering diverse content preferences for users in different areas, we formulate the content placement problem with the objective to minimize the average content retrieving delay of the network. By introducing the concept of the delay reduction gain and the caching benefit, we first derive the optimal caching strategy for base stations in different areas separately. Then, we propose two algorithms to calculate the cooperative caching strategy of the network, in which reduced search space is applied based on theoretical analysis. While the dynamic programming algorithm can achieve the optimal solution of the content placement problem, the submodular optimization based algorithm can provide guaranteed performance with relatively low complexity. Simulation results show that the proposed caching strategies can effectively improve the network delay performance.

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In this tutorial, we focus on FL via over-the-air computation (AirComp), which is proposed to reduce the communication overhead for FL over wireless networks at the cost of compromising in the learning performance due to model aggregation error arising from channel fading and noise. We first provide a comprehensive study on the convergence of AirComp-based FEDAVG (AIRFEDAVG) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity. Through convergence and asymptotic analysis, we characterize the impact of aggregation error on the convergence bound and provide insights for system design with convergence guarantees. Then we derive convergence rates for AIRFEDAVG algorithms for strongly convex and non-convex objectives. For different types of local updates that can be transmitted by edge devices (i.e., local model, gradient, and model difference), we reveal that transmitting local model in AIRFEDAVG may cause divergence in the training procedure. In addition, we consider more practical signal processing schemes to improve the communication efficiency and further extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes. Extensive simulation results under different settings of objective functions, transmitted local information, and communication schemes verify the theoretical conclusions.

Metaverse is envisioned to merge the actual world with a virtual world to bring users unprecedented immersive feelings. To ensure user experience, federated learning (FL) has been expected as a critical enabler to provide metaverse users with high-quality sensing, communicating, and rendering. However, considering the limitation of wireless communication resources and the stringent requirements of users, collaborating with massive metaverse users to realize FL still has tremendous challenges. Most pioneer works on improving the performance of FL assume that the system states are static, which is unsuitable in the metaverse. Because the FL in the metaverse is always a complicated long-term iteration process, where the fluctuations of channel status and available computing resources of users are inevitable, a changeless strategy may lead to poor results. Therefore, this paper proposes an efficient FL scheme relying on dynamic user selection, gradient quantization, and resource allocation. Specifically, we derive the convergence error bound to reveal the impact of user selection, wireless transmission error, and gradient quantization error of each iteration on FL’s convergence. Based on the theoretical analysis, we jointly and dynamically optimize the user selection, gradient quantization, and resource allocation to minimize the error bound with time and energy consumption budgets. Furthermore, to make the formulated sequential decision-making problem tractable, we transform it into a Markov decision process and design a soft actor-critic-based solution. Extensive experiments validate that our proposed scheme has superior performance compared to conventional schemes in dynamic-changing network environments.

The distributed subgradient (DSG) method is a widely used algorithm for coping with large-scale distributed optimization problems in machine-learning applications. Most existing works on DSG focus on ideal communication between cooperative agents, where the shared information between agents is exact and perfect. This assumption, however, can lead to potential privacy concerns and is not feasible when wireless transmission links are of poor quality. To meet this challenge, a common approach is to quantize the data locally before transmission, which avoids exposure of raw data and significantly reduces the size of the data. Compared with perfect data, quantization poses fundamental challenges to maintaining data accuracy, which further impacts the convergence of the algorithms. To overcome this problem, we propose a DSG method with random quantization and flexible weights and provide comprehensive results on the convergence of the algorithm for (strongly/weakly) convex objective functions. We also derive the upper bounds on the convergence rates in terms of the quantization error, the distortion, the step sizes, and the number of network agents. Our analysis extends the existing results, for which special cases of step sizes and convex objective functions are considered, to general conclusions on weakly convex cases. Numerical simulations are conducted in convex and weakly convex settings to support our theoretical results.

In recent years, unmanned aerial vehicles (UAVs) have been widely used in ocean target tracking and image acquisition for processing. Due to the limited energy of the UAV and the high computational complexity associated with image processing tasks, a lightweight energy-saving target tracking scheme is designed for the UAV, and the unmanned surface vehicle (USV) based mobile edge computing (MEC) networks are adopted to share the computing load of the UAV. Due to the randomness of the environment, we formulate data processing, computation offloading, resource allocation, and target-tracking as a joint stochastic optimization problem. This paper investigates a two-stage optimization scheme to address the problem. Firstly, we employ a Lyapunov-based approach to convert the stochastic optimization problem into a deterministic per-time slot problem under communication and computing resources constraints. Then, we develop a real-time target tracking scheme for the UAV based on the Elman neural network. Numerical results validate that the designed tracking scheme can effectively minimize propulsion energy consumption while maintaining a high success rate in tracking. Furthermore, the proposed method balances data-related energy consumption, image detection accuracy, and stability of the data storage queue.

The space-air-ground-ocean integrated network (SAGOI-Net) has become the focus of research in recent years, which has the characteristics of wide coverage and strong adaptability. However, due to the influence of multiple heterogeneous network segments, this network is unable to provide excellent quality of service (QoS). Based on the software-defined network and virtual network architecture, we abstract SAGOI-Net as a three-layer heterogeneous physical network resource, and propose a multi-domain virtual network embedding solution to optimize QoS. Specifically, before virtual network embedding, we collected SAGOI-Net's resource information through software-defined network and modeled it. In the virtual network embedding process, we first classify the virtual network request through K-means, and dynamically adjust the reward function to use reinforcement learning to solve the optimal virtual network embedding strategy. Finally, simulation experiments verify the effectiveness of the scheme.

The development of Unmanned Aerial Vehicles (UAVs) offers new prospects for emerging applications in the Industrial Internet of Things (IIoT) networks. With the assistance of Digital Twin (DT), a real-time understanding of physical entities can be constructed for dynamic perception and decision-making. However, DT modeling requires distributed data aggregation, resulting in privacy disclosure and communication burden. Therefore, we propose the digital twin edge network by integrating the DT technology and edge computing, which leverages an over-the-air computation enabled federated learning architecture for an efficient and secure DT model construction. Specifically, we propose a heterogeneity-aware and energy-conscious device scheduling mechanism, considering the update importance, channel condition, and computation capacity based on a probabilistic scheduling framework. To enhance energy efficiency, we introduce a virtual queue to track the difference between the cumulative energy consumption and budget. Additionally, we design a low-complexity scheduling algorithm to solve the optimization problem. Simulation results validate the superiority of our proposed mechanism in improving the test accuracy and energy efficiency in a heterogeneous and energy-constrained environment. Moreover, the proposed mechanism demonstrates significant advantages when employed to highly heterogeneous datasets, and exhibits a certain level of robustness to mapping errors arising from the utilization of DT technique.

Non-geostationary orbit (NGSO) constellations enabled by beam hopping (BH) technology are characterized by wide coverage and high spectrum efficiency. However, how to efficiently schedule multi-satellite beam resources to satisfy the heterogeneous and uneven terrestrial traffic demands remains a huge challenge for satellite operators. This paper proposes a satellite-terrestrial coordinated multi-satellite BH scheduling framework, where the complex multi-satellite BH problem is formulated into a long-term and a short-term subproblems. The long-term subproblem is cell-satellite association problem, which is solved by a low-complexity iterative algorithm executed in network operation control center (NOCC) to minimize the traffic load gap among satellites while considering the interference avoidance. The short-term subproblem is multi-satellite traffic-driven BH problem and we propose a multi-agent deep reinforcement learning (MADRL) architecture where each satellite can cooperatively make real-time BH decisions using the well-trained model by QMIX algorithm to adapt to time-varying and heterogeneous traffic. Simulation results demonstrate that the traffic load gap and network delay have been reduced by 70% and 50% respectively compared with non-load-balancing scheme. Besides, the proposed algorithm outperforms other benchmarks in terms of the network throughput under various traffic load cases and the average network delay is kept within 4 ms. Furthermore, the proposed QMIX-BH can be applied to real-time scheduling since the execution time is less than 1 ms.

Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wireless vertical FL to support high-dimensional model aggregation with full device participation. In this paper, we propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation by leveraging over-the-air computation (AirComp) and alleviating communication straggler issue with cooperative model aggregation among geographically distributed edge servers. However, the model aggregation error caused by AirComp and quantization errors caused by the limited fronthaul capacity degrade the learning performance for vertical FL. To address these issues, we characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed. We conduct extensive simulations to demonstrate the effectiveness of the proposed system architecture and optimization framework for vertical FL.

The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low datarate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate.

Unmanned aerial vehicles (UAVs) associated with federated learning (FL) have been deemed as a prospective framework by utilizing private data generated in the edge devices. However, despite various privacy-preserving and cryptography technologies adopted at the data level, FL still faces a range of security threats to raw data considering the broadcast nature of wireless channel. In this paper, to facilitate the communication-efficiency and privacy-preservation capability, we propose a UAV-enhanced covert federated learning architecture over mmWave massive multiple input multiple output (MIMO) channel, where we harness the covert communication technique in FL in order to avoid eavesdropping of illegal wardens. To achieve a trade-off between the security performance and training cost, we formulate a joint optimization problem involving the UAV’s trajectory, transmitting power, analog beamforming, and the required accuracy of FL. Furthermore, we propose the multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the above-mentioned problem. Numerous simulations have been performed to demonstrate both the effectiveness and convergence of the proposed algorithm.

Satellite communications play an indispensable role in serving aviation user. However, since the number of satellite beams is much less than the number of users, aviation users need to share beams by time division multiplexing when the number of users is large, which inevitably leads to the situation that the satellite frequently requires the user to report location information for beam scheduling so as to prevent users from deviating from the coverage of satellite beams. Frequent user location updates result in high interaction overhead between the satellite and aviation users. In such a case, how to free users from frequent location updates and realize low interaction overhead beam scheduling are the key in satellite aviation communications. To solve such a problem, we first propose a dynamic spatio-temporal approximation (DSTA) model to provide large spatial-temporal scale mobility tolerance for users within the time frame permitted by the satellite system, then a novel beam collaboration scheduling algorithm based on this new model is further proposed, aiming to realize low-overhead satellite multi-beam scheduling. Simulation results show that the proposed method with moderate complexity reduces interaction frequency and interaction overhead between the satellite and users by at least 56.9% and 47.1% compared with benchmark approaches respectively, which demonstrates the superiority of our proposed algorithm.

With the explosive growth in demands for mobile video services, the focus of cellular networks is evolving from the core network to the edge network to facilitate resource-intensive services and mitigate backhaul burdens. However, the overlapping in coverage and the user similarity in preferences lead to substantial cache redundancy. In addition, the mobility of users poses a significant challenge to smart devices for device-to-device (D2D) communications, which affects the content richness and hit ratio of the edge cache. In this paper, an adaptive cache partitioning and placement (ACPP) strategy is proposed for mobile edge computing (MEC) networks to minimize the average cost of content access. A practical two-layer collaborative caching model is presented, which comprises 5G base station (gNB) clusters and D2D caching. Besides, a public and private cache partitioning method is designed for gNBs to improve the content richness of local cache, and a static and dynamic cache partitioning method is developed for user devices to address the varying mobility patterns. Simulation results demonstrate the effectiveness of the proposed ACPP strategy in delivering content at a lower average cost, and achieve a better hit ratio with a relatively high energy consumption at user ends.

We consider the uplink connectivity for massive machine-type communications (mMTC) assisted by intelligent reconfigurable surfaces (IRSs), where device activity detection (DAD) and channel estimation (CE) are challenging due to limited pilot sequences. Moreover, differentiation among device types causes channels to deviate from the assumed characteristics, leading to performance degradation of conventional compressive sensing (CS) algorithms. To this end, two innovative networks driven by the hybrid of model and data are proposed exploiting the iterative frameworks and deep neural networks. We first present a hybrid driven iterative shrinkage thresholding algorithm, dubbed HISTA-Net, where a dual attention network (DAN) is embedded within the iterations to adaptively suppress iterative noise and enhance sparsity properties. Subsequently, we encapsulate data driven network and the intrinsic channel matrix knowledge, and derive a hybrid driven approximate message passing network (HAMP-Net) to further improve the sparse recovery performance. Our experiments demonstrate that the proposed networks outperform existing CS methodologies and deep learning strategies in accuracy, convergence, and generalization ability. Remarkably, the proposed HAMP-Net reduces pilot overhead by 30%, and achieves an NMSE gain of 3 dB for signal-to-noise ratios exceeding 15 dB.

Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink wideband channel estimation for IRS aided multiuser multiple-input single-output (MISO) systems with hybrid architectures. Combining the structure of model driven and data driven deep learning approaches, a hybrid driven learning architecture is devised for joint estimation and learning the properties of the channels. For a passive IRS aided system, we propose a residual learned approximate message passing as a model driven network. A denoising and attention network in the data driven network is used to jointly learn spatial and frequency features. Furthermore, we design a flexible hybrid driven network in a hybrid passive and active IRS aided system. Specifically, the depthwise separable convolution is applied to the data driven network, leading to less network complexity and fewer parameters at the IRS side. Numerical results indicate that in both systems, the proposed hybrid driven channel estimation methods significantly outperform existing deep learning-based schemes and effectively reduce the pilot overhead by about 60% in IRS aided systems.

With the rapid development of intelligent vehicles, heterogeneous in-vehicle networks (HetIVNets) applying heterogeneous access technologies in terms of cellular and in-vehicle WiFi, are widely employed to provide stable and ubiquitous network environments for intelligent vehicles and their passengers. Most existing studies on the optimization of heterogeneous networks (HetNets) focus on user association, channel and power allocation. Additionally, these studies typically employ a single task metric to characterize the quality of service (QoS) requirements of the devices. In order to achieve green and energy-efficient intelligent vehicles, our work considers a HetIVNet composed of WiFi and cellular networks, and further optimizes the bandwidth allocation and energy consumption of WiFi access point (AP). Furthermore, to achieve more accurate resource allocation for different tasks in HetIVNet, we establish the QoS requirement model of various tasks for in-vehicle devices. Since the proposed optimization problem is non-convex and NP-hard, we formulate the objectives and constraints of user association and resource allocation (UARA) in HetIVNets as a markov decision process (MDP) and propose a proximal policy optimization (PPO) algorithm for in-vehicle intelligent resource allocation to uniformly schedule network resources. Simulation results validate that the proposed algorithm can achieve high task success rates under low-energy consumption conditions for WiFi AP. We also compare our algorithm with the state-of-art baselines to highlight its efficiency and stability.

Recent years have envisioned the widespread adoption of machine learning (ML) in unmanned aerial vehicle (UAV) swarms for task execution. However, it is hard for the traditional centralized ML approaches to be applied in UAV swarms due to the large latency, communication cost, and privacy disclosure when transmitting the raw data. As an alternative, over-the-air computation (AirComp)-enabled federated learning (FL) is expected as a communication-efficient solution by harnessing the interference. However, the benefit of AirComp is at the cost of compromised training performance due to the channel distortion caused by the fading channel and noise and straggler issues resulting from the aligned parameters. In addition, the limited energy budget and dynamic environment incorporating the mobility characteristic of UAVs and time-varying channel conditions make these issues more complex. To solve problems aforementioned, this work proposes an energy-and-communication-efficient device scheduling scheme for AirComp-enabled FL system in the UAV swarms. Specifically, we first derive the optimality gap to characterize the impact of channel distortion and device selection on training performance. Then based on this result, we formulate an optimization problem to minimize the optimality gap by scheduling an appropriate number of competent following UAVs in each round, considering the transmission and computation energy consumption as well. Simulation results validate that the proposed scheme can achieve superior training performance in terms of test accuracy compared with baselines, and shows robustness with the increasing scale of UAV swarm.

In recent years, mobile edge computing (MEC) has been proposed as a promising technique to alleviate the challenges faced by delay and computation-intensive applications. However, users in remote and mountainous areas continue to face difficulties obtaining reliable computation services. To overcome this obstacle, unmanned aerial vehicles (UAVs) equipped with MEC servers have emerged as a popular solution. In such a multi-UAV network, the coverage areas of the UAVs might overlap, which would result in resource wastage and interference. To address this issue, we investigate a collaborative UAV-assisted MEC system for both aerial users (AUs) and ground users (GUs) in this work. Specifically, each user is covered by multiple UAV servers, and the resources of UAVs are dynamic over time. The main objective of this work is to reduce the average delay and improve the service success rate by jointly designing the UAV server-user association, bandwidth, and computing resource allocation strategy. To address the non-convex optimization problem mentioned above, we formulate a multi-agent extension of Markov decision processes (MDPs) for the system and design a cooperative Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach for each UAV server to make decisions using a centralized training approach with distributed execution. Simulation results validate that the proposed approach can achieve a superior success service rate with a lower delay compared with baselines.

In recent years, autonomous underwater vehicles (AUVs) have been widely applied to collect data in underwater acoustic sensor networks (UWASNs). Limited by the capacity of a single AUV, as well as the low-latency requirement of data collection, the intelligent swarm consisting of multiple AUVs is expected to execute the secure and efficient data collection tasks in a cooperative manner. However, most of the existing works assumed that the locations of sensor nodes are already known, which is impractical in a real AUV network. In addition, the security issues are not well considered in underwater searching and transmission tasks. To improve the searching efficiency in an unknown underwater area where locations of sensor nodes cannot be obtained precisely, this work proposes a data collection scheme via a target uncertainty map based multi-agent reinforcement learning algorithm for AUV swarms. Specifically, the target uncertainty map is established based on the current and past searching and collection results, which can guide the AUV swarm to search the areas with higher probabilities to find sensor nodes waiting for data collection. Moreover, to mitigate the potential security risk of data leakage, we design a multi-agent deep deterministic policy gradient (MADDPG) algorithm for each AUV in the swarm to make its searching and data collection strategies through a manner of centralized training with distributed execution. Simulation results validate that the proposed scheme can achieve a high collection rate with low energy consumption. In addition, the security referring to data protection can be also guaranteed in AUV swarms.

Integrated sensing, computing and communications (ISC2) is a promising technology to enable both physical-digital spatial sensing, intelligent communication and computing. This paper studies a federated learning-assisted ISC2system, in which edge nodes coordinate edge computing resource for model training based on their local integrated sensing and communications (ISAC) data. In the process of a completely distributed collaborative training, sharing and transmission of local parameters may lead to a serious Byzantine attack. To improve the system's anti-attack capability, we design a blockchain-federated edge learning framework, which utilizes the non-tampering and traceability features of the blockchain, and design a verification algorithm for federated aggregation. Particularly, an aggregation algorithm is designed to improve the fitting efficiency and accuracy of our model. Experiments based on the measured ISAC data show that the proposed scheme can effectively resist up to 30% of data tampering and up to 30% of model tampering attacks.

It is significant to design a task scheduling scheme for the multi-objective task of autonomous underwater vehicle (AUV) network for target hunting and environmental exploration. Due to the limited communication and detection conditions, it is difficult for each individual AUV in the network to accurately obtain all environmental information without a central control node. Therefore, most centralized scheduling schemes are infeasible to a fully distributed AUV network. To address the aforementioned issues, a distributed multi-agent proximal policy optimization (DMAPPO) scheme is proposed in this work, where AUVs are efficiently scheduled to achieve target hunting and environmental exploration. The distributed scheduling scheme is able to adjust the number of AUVs for each task according to practical requirement. In addition, we design an intra-network cooperative multi-AUV environmental exploration method by introducing the age of information (AoI). Simulation results validate that the proposed algorithm can achieve an effective task scheduling in the distributed AUV network.

In this article, we aim to design a task scheduling scheme for the underwater multiobjective task of target hunting and environmental search. A distributed autonomous underwater vehicle (AUV) network is deployed to perform the task, where AUVs equipped with sensors can cooperatively search the environment and hunt the target by sharing local information. To achieve efficient exploration of the overall environment by the AUV network, we design an intranetwork cooperative searching approach based on the Age of Information (AoI). Besides, it is critical to conceive an energy-efficient mechanism due to the energy constraints of AUVs and the difficulty of sustainable energy supply. To address the aforementioned issues, we propose an energy-efficient distributed multiagent proximal policy optimization (DMAPPO) scheme to perform real-time AUV target hunting and environment searching in underwater turbulent fields. The proposed scheme can adjust the number of AUVs assigned to each objective according to practical requirement and residual energy. Distributed AUVs can make decisions autonomously and cooperatively complete the task efficiently through limited information interaction. In addition, we derive a lower bound on the policy improvement of MAPPO. Moreover, our simulation results demonstrate that the proposed scheme outperforms the standard algorithms in terms of hunting efficiency, degree of searching, and network energy efficiency.

The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users’ Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network’s state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.

To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and privacy-preserving. To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels. However, the fading and noise of wireless channels can cause aggregate distortions in AirComp enabled federated learning. In addition, the quality of collected data and energy consumption of edge devices may also impact the accuracy and efficiency of model aggregation as well as convergence. To solve these problems, this work proposes a dynamic device scheduling mechanism, which can select qualified edge devices to transmit their local models with a proper power control policy so as to participate the model training at the server in federated learning via AirComp. In this mechanism, the data importance is measured by the gradient of local model parameter, channel condition and energy consumption of the device jointly. In particular, to fully use distributed datasets and accelerate the convergence rate of federated learning, the local updates of unselected devices are also retained and accumulated for future potential transmission, instead of being discarded directly. Furthermore, the Lyapunov drift-plus-penalty optimization problem is formulated for searching the optimal device selection strategy. Simulation results validate that the proposed scheduling mechanism can achieve higher test accuracy and faster convergence rate, and is robust against different channel conditions.

With the development of commercial off-the-shelf hardware, low Earth orbit (LEO) satellites are promising to provide flexible edge computing services. In this paper, we investigate a digital twin (DT)-empowered satellite-terrestrial cooperative edge computing network, where computation tasks from terrestrial users can be partially offloaded to the associated base station (BS) edge server, the associated LEO satellite edge server, and an adjacent LEO satellite edge server. We formulate a multi-tier computation offloading optimization problem to minimize the weighted sum of total system delay and satellite energy consumption, where a LEO-layer problem and a DT-layer problem are involved. The LEO-layer problem optimizes the three-tier computation resource allocation and task splitting ratio. From the multi-satellite network perspective, the DT-layer problem optimizes how many resources will be shared between adjacent satellites. We then propose a multi-agent double actors twin delayed deterministic policy gradient (MA-DATD3) algorithm to optimize the LEO-layer problem, and adopt a centralized training and decentralized execution (CTDE) paradigm. The proposed MA-DATD3 algorithm is extended to solve the DT-layer problem in a centralized way, and the resource sharing between adjacent satellites is optimized to maximize the time-averaged reward. Simulation results show that our algorithm achieves a better performance than the MADDPG algorithm, and effectively improves the computation offloading performance while balancing the energy consumption and the total delay.

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multi-tier computing in the integrated satellite-terrestrial network, in which the computation tasks of users are processed by leveraging the cooperation of devices, edge nodes, and cloud servers. Based on the proposed three-tier computing framework, we formulate the cooperative edge-cloud offloading problem to minimize the total delay of the network. Considering the computation task is dividable, we propose the optimal task splitting strategy based on the partial offloading model, in which the closed-form solution is derived for each computation task. With the optimal task splitting strategy obtained, the original optimization problem is reformulated as the problem of the time slot allocation strategy and the computation capacity allocation strategy. Then, we further propose the cooperative edge-cloud computing strategy to optimize the delay performance of the network. Finally, numerical results are presented to demonstrate the performance of the proposed three-tier computing architecture and the offloading strategies.

Passive sensing technology enables Wi-Fi-based human activity recognition (HAR), which has been widely noted in recent years. This letter presents a novel Wi-Fi-based class-incremental human activity recognition system that allows for the gradual addition of new activity categories. To the best of our knowledge, this is the first attempt to recognize all previously learned activities under the constraint of limited samples for both the original and newly added activity classes. It is challenging in 1) how to prevent catastrophic forgetting of old activities and 2) how to leverage as few samples as possible to accurately recognize new activities. Therefore, a phased training and update strategy is proposed to avoid the knowledge-forgetting issue. Furthermore, to alleviate the unsatisfactory performance problem caused by insufficient samples of new categories, we design an amplitude-phase enhanced convolution neural network, which integrates an attention mechanism and dual loss function to enhance the feature discrimination and the generalization capability of the model. Extensive experiments show that our system can operate with promising perceptual accuracy in different datasets.

Distributed multi-task learning (MTL) can jointly learn multiple models and achieve better generalization performance by exploiting relevant information between the tasks. However, distributed MTL suffers from communication bottlenecks, in particular for large-scale learning with a massive number of tasks. This paper considers distributed MTL systems where distributed workers wish to learn different models orchestrated by a central server. To mitigate communication bottlenecks both in the uplink and downlink, we propose coded computing schemes for flexible and fixed data placements, respectively. Our schemes can significantly reduce communication loads by exploiting workers’ local information and creating multicast opportunities for both the server and workers. Moreover, we establish information-theoretic lower bounds on the optimal downlink and uplink communication loads, and prove the approximate optimality of the proposed schemes. For flexible data placement, our scheme achieves theoptimaldownlink communication load, and theorder optimaluplink communication load that is smaller than 2 times of the information-theoretic optimum. For fixed data placement, the gaps between our communication load and the optimum are within the minimum computation load among all workers, regardless of the number of workers. Experiments demonstrate that our schemes can significantly speed up the training process compared to the traditional approach.

Due to the non-uniform distribution of the ground traffic demand and the high mobility of non-geostationary orbit (NGSO) satellites, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for NGSO communication systems. In order to achieve efficient spectrum utilization, the combination of full frequency multiplexing and beam hopping is a major trend in future satellite communication systems. However, conventional beam hopping methods are mainly based on geostationary satellites, which do not take into account the interference between satellites. This paper proposes a multi-satellite beam hopping algorithm based on load balancing and interference avoidance, which takes advantage of the multiple coverage features in the NGSO constellation and avoids intra-satellite interference and inter-satellite interference by designing beam-hopping patterns with spatial isolation characteristics. In particular, we decompose the multi-satellite beam hopping problem into three sub-problems, which are the multi-satellite load balancing problem, the single-satellite beam hopping pattern design problem, and the multi-satellite interference avoidance problem. Simulation results demonstrate that the proposed method reduces the load gap among satellites by about 72.5% and the average traffic satisfaction rate can reach 81.4%. Besides, our method has the lowest unmet capacity compared with other benchmarks, achieving better offered-requested data match.

Network Virtualization (NV) is an emerging network dynamic planning technique to overcome network rigidity. As its necessary challenge, Virtual Network Embedding (VNE) enhances the scalability and flexibility of the network by decoupling the resources and services of the underlying physical network. For future multi-domain physical network modeling with the characteristics of dynamics, heterogeneity, privacy, and real-time, the existing related works perform unsatisfactorily. Federated learning (FL) jointly optimizes the network by sharing parameters among multiple parties and is widely used to address data privacy and data silos. Aiming at the NV challenge of multi-domain physical networks, this work is the first to propose using FL to model VNE, and presents a VNE architecture based on Horizontal Federated Learning (HFL) (HFL-VNE). Specifically, combined with the distributed training paradigm of FL, we deploy local servers in each physical domain, which can effectively focus on local features and reduce resource fragmentation. A global server is deployed to aggregate and share training parameters, which enhances local data privacy and significantly improves learning efficiency. Furthermore, we deploy the Deep Reinforcement Learning (DRL) model in each server to dynamically adjust and optimize the resource allocation of the multi-domain physical network. In DRL-assisted FL, HFL-VNE jointly optimizes decision-making through specific local and federated reward mechanisms and loss functions. Finally, the superiority of HFL-VNE is proved by combining simulation experiments and comparing it with related works.

In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multi-cluster networks, where multiple UAVs simultaneously perform different WDA tasks via over-the-air computation (AirComp) without terrestrial base stations. This work focuses on maximizing the minimum amount of WDA tasks performed by each cluster by optimizing the UAV trajectory and transceiver design as well as cluster scheduling and association, while considering the WDA accuracy requirement. Such a joint design is critical for interference management in multi-cluster AirComp networks, via enhancing the signal quality between each UAV and its associated cluster for signal alignment while reducing the inter-cluster interference between each UAV and its non-associated clusters. Although it is generally challenging to optimally solve the formulated non-convex mixed-integer nonlinear programming, an efficient iterative algorithm as a compromise approach is developed by exploiting bisection and block coordinate descent methods, yielding an optimal transceiver solution in each iteration. The optimal binary variables and a suboptimal trajectory are obtained by using the dual method and successive convex approximation, respectively. Simulations show the considerable performance gains of the proposed design over benchmarks and the superiority of deploying multiple UAVs in increasing the number of performed tasks while reducing access delays.

Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as a promising paradigm for dealing with the massive amounts of data generated by intelligent devices. Nevertheless, although FL has natural advantages in data security protection, eavesdroppers can also deduce the raw data according to the shared parameters. Existing works mainly focused on encrypting the content of uploaded parameters, but we believe that it can improve security further by hiding the presence of parameter updating. Therefore, in this paper, we conceive a UAV-enabled covert federated learning architecture, where the UAV is not only responsible for orchestrating the operation of FL but also for emitting artificial noise (AN) to interfere with the eavesdropping of unintended users. To strike a balance between the security level and the training cost (including time overhead and energy consumption), we propose a distributed proximal policy optimization-based strategy for the sake of jointly optimizing the trajectory and AN transmitting power of the UAV, the CPU frequency, the transmitting power and the bandwidth allocation of the participated devices, as well as the needed accuracy of the local model. Furthermore, a series of experiments have been conducted to validate the effectiveness of our proposed scheme.

Global navigation satellite systems (GNSSs) are used in numerous fields, but their vulnerability is a global problem that has yet to be solved. A promising way to effectively address this problem is by integrating navigation into emerging dense nongeosynchronous orbit (NGSO) megaconstellations. To maximize the downlink efficiency for users, an integrated satellite navigation and communication (ISNAC) framework is proposed in this work, in which a necessary number of packetized bursty downlink satellite communication signals are used directly for navigation purposes. Then, the user terminal estimates the time of arrival (TOA) of these ISNAC signals regardless of their coding and modulation type. To this end, a TOA estimation algorithm is proposed. Specifically, the user terminal first constructs a template signal with demodulated data bits to replicate the transmitted ISNAC signal and then calculates the cross-correlation function (CCF) of the template and the received signal for TOA estimation, based on which the TOA is finally estimated. The performance bound of the proposed TOA estimation algorithm is analyzed. Simulations are conducted to corroborate the mathematical analysis and The results show that the proposed algorithm outperforms other TOA estimators for comparsion by approaching the Cramer-Rao lower bound (CRLB) with a much lower computational complexity.

Artificial intelligence (AI) is expected as a revo-lutionary technology to be widely used in Internet-of- Things (IoT) networks for computationally intensive tasks. However, the traditional centralized training framework imposes large latency, network burdens and high risk of privacy disclosure. As a promising distributed solution, federated learning involves the collaborative model training among edge devices, with the orchestration of a server to carry the capacities of low-latency and privacy preservation for AI -driven networks. To further improve the communication efficiency, over-the-air computation (AirComp) is capable of computing while transmitting data by exploiting the superposition property of wireless channels to harness the interference. However, gradient aggregation suffers from channel distortion induced by channel fading and noise, which may degrade the training performance. Moreover, it is beneficial to schedule the informative edge devices in federated learning under limited energy resources. In this work, we propose a dynamic device scheduling scheme for AirComp enabled federated learning systems. In this scheme, a proper number of qualified edge devices with channel inversion based power control are scheduled to participate the model training, where local updates diversity, channel condition and energy consumption are exploited jointly. Inspired by the Lyapunov drift-plus-penalty method, we formulate the optimization problem to attain the device selection strategy. Simulation results validate that the proposed scheme can achieve a close-to-optimal test accuracy with fast convergence rate, and present good performance of robustness under different channel conditions.

Given the increasing attention paid to the security of underwater communications, covert communication system has been envisaged as a key enabler for empowering the marine information networks to address the challenge of ever-increasing demand of anti-eavesdropping. However, dynamic underwater hydrology environment and ambient noise make it substantially difficult to reduce the decoding error probability as much as possible on the premise of meeting the concealment requirements. In this paper, we first build up underwater covert communication (UCC) system and analyze its secrecy performance at the physical (PHY) layer. Moreover, we propose a dynamic power-threshold based bargaining game model to preserve the receiver’s concealment, while ensuring high receiver-side signal-to-interface-noise ratio (SINR). The simulation results show the detection probability at the equilibrium of our model is optimal, and thus it is capable of both overcoming the dynamic changes and of increasing the system’s stability significantly by analyzing the time discount factor.

With the development of space-based networks, the disadvantage of traditional centralized ground control including low invulnerability and high propagation delay cannot be ignored, and the capabilities of distributed resource management and control become more and more important. Although there are many distributed resource allocation methods, the computational complexity of them is too heavy with expensive communication costs and frequent parameter interaction causing a heavy burden to satellite systems. To solve such problems, we proposed a lightweight distributed satellite resource allocation method based on contract theory with one-time interaction. In our contract mechanism, a two-sided relationship is considered between resource requesters and resource providers. Satellites only need to sign a communication contract including a combination of the CPU resources that providers could offer and the payments that requesters could pay. Simulation results show that the proposed contract mechanism is feasible and the performance of our mechanism is better than the baseline mechanism under our scenario.

With the development of marine exploitation, underwater acoustic sensor networks (UWA-SNs) have become a hot research field. However, the harsh environment poses a threat to the security of underwater communication, as most routing protocols ignore the curve transmission of acoustic wave, which are more susceptible to transmission interference with higher transmission delay. To cope with these problems above, this work exploits a model under the assumption that the sound curve propagation relies on positive sound speed gradient. In order to find the path with the shortest delay, we design a routing scheme inspired by artificial fish swarm (AFS) and ant colony optimization (ACO) algorithms. Furthermore, we establish the path comprehensive benefit (PCB) to make a tradeoff between transmission delay and the lifetime of network. The simulation results validate that the algorithm proposed in this work is capable of improving the system performance compared to the benchmark algorithms in terms of both transmission delay and load-balance, and meanwhile ensuring paths reliability and security of the entire network.

This work considers designing an unmanned target hunting system for a swarm of unmanned underwater vehicles (UUVs) to hunt a target with high maneuverability. Differential game theory is used to analyze combat policies of UUVs and the target within finite time. The challenge lies in UUVs must conduct their control policies in consideration of not only the consistency of the hunting team but also escaping behaviors of the target. To obtain stable feedback control policies satisfying Nash equilibrium, we construct the Hamiltonian function with Leibniz’s formula. For further taken underwater disturbances and communication delay into consideration, modified deep reinforcement learning (DRL) is provided to investigate the underwater target hunting task in an unknown dynamic environment. Simulations show that underwater disturbances have a large impact on the system considering communication delay. Moreover, consistency tests show that UUVs perform better consistency with a relatively small range of disturbances.

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and space-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems , which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system.

Satellite Internet (SI) dramatically expanded the ground-based Internet, and it is also the future direction of 6G. However, due to limited computing power and bandwidth resources, Distributed Denial-of-Service (DDoS) attacks can cause more severe damage to SI, and even paralysis of the entire network. Current DDoS defense mechanisms are built on abundant computing power and bandwidth resources, making applying in the SI scenario challenging. Aiming at protecting SI from DDoS attacks, a blockchain-based distributed collaborative entrance defense (DCED) framework is proposed, in which network traffic characteristics can be recorded and aggregated at the entrances of SI. The proposed framework consists of a distributed detection digesting procedure, a digest virtual aggregation procedure, and an entrance control strategy. The former procedure detects and extracts multidimensional characteristics of DDoS attacks and pushes them onto the blockchain. The latter procedure collects block data and aggregates attack features using the MapReduce algorithm and then compares them with baseline and gives an alert. The strategy completes the filtering and interception of traffic. Experiments use the IXIA platform to generate malicious traffic, and results show that the framework can accurately identify attack traffic within 1500 ms, reaching an area of 0.99 under the receiver operating characteristic curve. The proposed framework is more effective than other similar DDoS methods, protecting the precious SI bandwidth resources.

The collision of ranging signals in the process of massive concurrent access ranging seriously affects the networking speed of satellite communication networks. Random access schemes are usually used to reduce the collision probability, but their effects are limited due to the limitation of ranging access rules and the network size. To further improve the performance of access ranging, the idea of optimal access ranging parameters is introduced in this article. A multiobjective anti-collision algorithm (MOACA) is proposed to optimize the concurrent access ranging process of high-capacity nodes of Internet of Things (IoT) to multifrequency time-division multiple-access (MF-TDMA) satellite communication systems. The model of the ranging measurement process is put forward and the expressions of ranging measurement time, collision probability, and the number of ranging channels are derived. On this basis, a multiobjective optimization problem (MOP) of the access ranging process is established. In MOACA, an ideal point database is formed based on the solutions of the MOP problem with typical inputs, and the negotiation curves of objectives are introduced to obtain the most appropriate ideal point from the database under certain requirements and system states. Besides, MOACA also contains a single-objective problem (SOP) for achieving the ideal point in the situation where no matching pattern can be found in the database. The simulation result shows that MOACA can improve the performance of the concurrent access ranging process.

Satellite communications play an important role in future Internet of Things (IoT) networks, and megasatellite constellations can further provide global coverage and high-quality services for IoT communications. In the megaconstellation, large-scale satellites are launched to enhance the capacity. However, the dense distribution of satellites brings intraconstellation interference, limiting the performance. In order to evaluate the restriction of interference caused by system parameters, such as the scale of constellation or the frequency reuse factor, we investigate uplink intraconstellation interference and performance of the megasatellite constellation. First, a multibeam polar constellation with uplink spatial frequency reuse is assumed. Then, the interference model is constructed considering the antenna gain of interfering user terminals and multibeam satellites, where the details of the satellite-fixed frequency reuse scheme and coordinates of co-frequency cells are provided. To evaluate the performance, expressions of outage probability, ergodic capacity, and sum ergodic capacity are driven. The analytical results disclose the impact of system design on the performance, and the accuracy of analysis results is obtained through extensive simulation evaluation. The results show that sum ergodic capacity achieves highest in the case of full frequency reuse for the frequency-limited constellation system, and it gets a linear growth at first but then keeps flat with a trend of fluctuating downward as the scale increases; therefore, the impact of the scale should be considered when constructing megaconstellations.

The development of intelligent cyber–physical systems (ICPSs) in the virtual network environment is facing severe challenges. On the one hand, the Internet of Things (IoT) based on ICPSs construction needs a large amount of reasonable network resources support. On the other hand, ICPSs are facing severe network security problems. The integration of ICPSs and network virtualization (NV) can provide more efficient network resource support and security guarantees for IoT users. Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources, and security constraints to ensure the rationality and security of resource allocation in ICPSs. In particular, we use the reinforcement learning (RL) method as a means to improve algorithm performance. We extract the important attribute characteristics of the underlying network as the training environment of the RL agent. The agent can derive the optimal node embedding strategy through training, so as to meet the requirements of ICPSs for resource management and security. The embedding of virtual links is based on the breadth first search (BFS) strategy. Therefore, this is a comprehensive two-stage RL-VNE algorithm considering the constraints of computing, storage, and security 3-D resources. Finally, we design a large number of simulation experiments from the perspective of typical indicators of VNE algorithms. The experimental results effectively illustrate the effectiveness of the algorithm in the application of ICPSs.

With the increasing global communication demands and the development of Internet of Things (IoT), extending the connectivity to rural and remote areas has become imperative for future networks. The sixth-generation (6G) network is expected to provide heterogeneous services and seamless network coverage for everyone and everything. Combining the advantages of both satellite and terrestrial networks, the integrated satellite-terrestrial network architecture is promising to provide global broadband access for all types of users, which has drawn much attention from both the academia and industry. In this article, we present a comprehensive survey of the state-of-the-art of integrated satellite-terrestrial networks toward 6G. First, an executive classification and summary of the integration architecture is presented from network design to performance optimization. Then, typical applications of the integrated satellite-terrestrial network are discussed based on the architecture. By considering the unique characteristics of the two networks, main challenges are pointed out when performing integration, such as the long propagation delay, complex link conditions, and high dynamics of the network topology. Finally, some promising future techniques are explored from the perspective of the integrated architecture. A detailed survey of the potential integration architectures is of great importance to enable more flexible network design and construction in future 6G networks. This article will provide a valuable guideline on future research and development of integrated satellite-terrestrial networks.

Non-orthogonal multiple access (NOMA) is a promising technology for next generation multiple access (NGMA). However, traditional NOMA detection methods cannot cope with challenges of NGMA in satellite high-mobility communications. On the one hand, due to the high mobility and heterogeneity of terminals in terms of the velocity and acceleration, time-varying Doppler shifts caused by high-mobility terminals are relatively higher and different for each user, which degrades the demodulation performance seriously and makes multi-user interference cancellation become the bottleneck. On the other hand, owning to wide distribution of high-mobility terminals, the arriving time of users’ signals is different at the receiver, which further incurs more difficulties for NOMA detection. To solve such a problem in satellite high-mobility communications, we propose a novel multi-user detection method based on the new three-dimensional (3D) factor graph for high-mobility environments, named the turbo iterative detection (TID) algorithm. Specially, the proposed algorithm consists of the interference cancellation loop, the Doppler elimination loop and the decoding loop. By means of message passing along edges in the proposed 3D factor graph, these three iterative loops can interact with each other to effectively eliminate time-varying Doppler shifts of heterogeneous high-mobility terminals and interference among multiple users. Simulation results show that the proposed algorithm improves the bit error ratio (BER) performance more than 0.9 dB with less computational complexity compared with traditional algorithms, which demonstrates the superiority of this algorithm in terms of the BER performance and computational complexity.

Gesture recognition is the central enabler of human-computer interaction (HCI). In addition to the semantic information contained in gestures, gesture-based user identification can effortlessly enhance HCI system security. Recently, the Wi-Fi-integrated sensing and communication (ISAC) technology has shown great potential in a field hitherto occupied by computer vision and radar sensing. In this work, leveraging Wi-Fi sensing, we propose a system called WiGesID that achieves joint gesture recognition and human identification (JGRHI). The basic idea behind WiGesID is to identify personalized spatiotemporal dynamic patterns from the gestures of different users. Moreover, we develop an effective approach to recognize new categories of gestures and users by computing relation scores between the features of the new category samples and the support samples. To evaluate the performance, we implemented WiGesID and conducted extensive experiments. The results demonstrate that our system outperforms the state-of-the-art method for cross-domain sensing and accurately recognizes new categories, which promotes the use of this application of Wi-Fi sensing in HCI.

Network traffic classification has become an important part of network management, which is beneficial for achieving intelligent network operation and maintenance, enhancing the network quality of service (QoS), and for network security. Given the rapid development of various applications and protocols, more and more encrypted traffic has emerged in networks. Traditional traffic classification methods exhibited the unsatisfied performance since the encrypted traffic is no longer in plain text. In this work, we modeled the time-series network traffic by the recurrent neural network (RNN). Moreover, the attention mechanism was introduced for assisting network traffic classification in the form of the following two models, the attention aided long short term memory (LSTM) as well as the hierarchical attention network (HAN). Finally, relying on the ISCX VPN-NonVPN dataset, extensive experiments were conducted, showing that the proposed methods achieved 91.2 percent in accuracy while the highest accuracy of other methods was 89.8 percent relying on the same dataset.

Recently, the term ‘Internet of Things’ (IoT) has garnered great attention. As a trusted, dependable, and decentralized approach, blockchain has already been used in IoT. However, the existing blockchain has a number of drawbacks that prevent it from being used as a generic platform for IoT. The nodes in IoT are heavily resource-limited, especially computing and networking resources. Unfortunately, they are necessary for the blockchain to solve complicated puzzles and propagate blocks. In this paper, we propose agent mining and cloud mining approaches to solve the above problem in the blockchain-enabled IoT. To be specific, miners act as mining agents for nodes in IoT, offload mining tasks to cloud computing servers, and use networking resources dynamically. Furthermore, in order to enhance the performance, the access selection of users, computing resources allocation, and networking resources allocation are formulated as a joint optimization problem. We then propose a dueling deep reinforcement learning approach to address this problem. Numerical results justify the effectiveness of our proposed scheme.

We investigate multiuser detection under uplink asynchronous scenario, where the triangular successive interference cancellation (T-SIC) detection scheme is exploited. As the existing analysis was conducted under some ideal assumptions for simplicity, the asynchronous scenario is analyzed under rigorous assumptions in this paper so that important insights are revealed for practical systems. Specifically, the average symbol error rate (SER) formulas are derived for a two-user system with arbitrary$M$-ary quadrature amplitude modulation ($M$-QAM) over Rayleigh fading channels. These solutions are shown to have higher accuracies compared to the existing solutions. Furthermore, a novel iterative detection algorithm is proposed, i.e., the parallel T-SIC method, which is more efficient and effective compared to the iterative T-SIC method. Insightful discussion is conducted in terms of the potentials of the concerned iterative detection method. Simulation results show that the most significant improvement of the iterative detection reflects on the SER for the strongest user, and the reduction is always within 50% of the SER compared to the primary detection. The gains obtained by the iterative detection is negligible for higher order$M$-QAM cases.

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and geostationary earth orbit (GEO)-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems, which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system and approximates the upper bound performance.

The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people’s lives in all directions. The rapid development of the IoT technology accelerates the process of the era of “Internet of everything” but also changes the role of terminal equipment at the edge of the network. It has changed from a single data user to a dual role of both producing and using data. And collaborative edge computing (CEC) has been born in time. CEC itself can not only solve the problem of computing and storage but also combines with the deep learning (DL) model to make full use of edge computing ability. However, as the core of DL, the robustness of neural network is often not high. In addition, edge devices of CEC are facing a highly dynamic environment, which can easily cause the edge network to be attacked by malicious devices. Therefore, user privacy protection and security issues for CEC deserve more attention. To avoid privacy leakage and security crisis of CEC in social IoT systems, a data protection method based on data disturbance method and adversarial training viewpoint is introduced in this article. Besides, a new adversarial sample generation method based on the firefly algorithm (FA) is proposed. This method reduces the time complexity of traditional by an order for magnitude compared with traditional generative adversarial network (GAN) generation. Since sentences, information on CEC in the IoT system is characterized by a large amount of data, strict confidentiality, and high-security requirements, and they are usually high-risk information on privacy leakage. The proposed method is conducted to the sentence similarity analysis model based on a convolutional neural network (CNN) in the CEC scene to test the feasibility of the method. Compared with the original CNN, the accuracy of the model using the confrontation training method is improved by 4.8%. At the same time, the security value of our model is 2.1% higher than that of the simple CNN model, and it has the best security performance among the four comparison models. Further experiments have demonstrated that the model performs better in its capacity of resisting disturbance and can effectively help multiple organizations to implement data usage and sentence information on the requirements of user privacy protection, data security, and government regulations.

Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98$\%$. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.

Relying on offloading computation tasks to the network edge, ultra dense networks (UDNs) are capable of providing delay-aware service to nearby users. Meanwhile, software defined networking (SDN) is deemed as an effective technology to ease the management of infrastructure plane and control plane in UDNs, which is termed as SDN-based ultra dense networks. Specifically, the centralized SDN controller is capable of managing the whole network globally. With the increasing demands for various applications as well as the limitation of computation, storage and communication resource, how to allocate spectrum resource appropriately is imperative. In this article, we mainly show solicitude for spectrum sharing and edge computation offloading problems in SDN-based ultra dense networks, constituted of various macro base stations (MBSs), small-cell base stations (SBSs) and user equipments (UEs). To address this issue, we propose a second-price auction scheme for ensuring the fair bidding for spectrum rent, which enables the MBS edge cloud and SBS edge cloud to occupy the channel in cooperative and competitive modes. Moreover, the MBS edge cloud is termed as the buyer, and the SBS edge clouds are the sellers who sell the offloading resource to the MBS edge cloud. To be specific, the spectrum sharing and computation offloading scheme is executed in the SDN controller, and the controller is responsible for distributing spectrum allocation instructions to the infrastructure plane. Finally, experimental results validate the effectiveness of our proposed scheme in SDN-based ultra dense networks.

Space-air-ground integration to support seamless coverage of ground, satellite, airborne, and marine communications, is likely to be a key trend in the 6G era. One of several key challenges in such space-air-ground integration networks (SAGINs) is to design efficient scheduling approaches for multi-dimension network resources. Due to the inherent heterogeneity characteristics, we demonstrate how can transform the network resource allocation problem in SAGINs into a multi-domain virtual network resource allocation problem, as well as proposing a reinforcement learning assisted bandwidth aware virtual network resource allocation algorithm (RL-BA-VNA). Specifically, RL-BA-VNA leverages reinforcement learning and uses a policy network as an agent to perform the node embedding. In order to support users’ exacting bandwidth requirements, we prefer to select virtual network requests with large bandwidth for embedding. Experiment findings show that the proposed algorithm RL-BA-VNA outperforms respectively the other three conventional virtual network resource allocation algorithms RL, DRL and BASELINE by an average of 2.06%, 4.93%, 11.07% in terms of long-term average reward, acceptance rate, and long term reward/cost.

Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users’ service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation.

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multilayer edge caching in the integrated satellite-terrestrial network to reduce the communication delay, in which the base station cache, the satellite cache, and the gateway cache cooperatively provide content service for ground users. We first propose the three-layer cooperative caching model of the network, based on which we analyze the content retrieving process and derive the cache hit probability for different caching locations. Considering limited cache sizes, we formulate the content placement problem to minimize the average content retrieving delay of users. Then, two caching strategies, the non-cooperative caching strategy and the cooperative caching strategy, are proposed with exhaustive theoretical analysis. By introducing the concept of delay reduction gains, the optimal caching strategies are obtained based on the proposed iterative algorithms. Finally, numerical results are presented to demonstrate the performance of the proposed cooperative caching architecture and the caching strategies.

With the emergence of more and more latency-sensitive applications and mobile devices pumped to the edge of network, the burden on the backhaul network is getting heavier and heavier due to the limited transmission resources. Mobile edge computing (MEC) considered as a promising technology becomes more and more popular, which can provide services at the edge of the network. In this paper, we take into account the mobility of users and focus on the problem of service migration. Most of existing works modeled a Markov Decision Process (MDP) model with a high-dimensional state space, and have to solve it by deep reinforcement learning. To tackle this issue, we propose a distributed two-layer decomposition model and generate a series of new MDP problem with Low-dimensional in order to replace original High-dimensional MDP. In our model, the size of state space is reduced from M2Nto N × M2 by decomposing the original optimization problem. Simulation results show that the performance of the proposed two-layer decomposition model is better than the baseline models.

Ensuring communication for Public Safety Users (PSUs) in all events, and specifically in disaster situations, is one of the main challenges of Cellular Networks (CNs). Various types of communications, such as in-band and outband communications, can be used to integrate the Public Safety Network (PSN) into the CN. In this paper, we focus on in-band overlay Device-to-Device (D2D) communication, which effectively reduces the interference caused by Cellular Users (CUs), and ensures the availability of Resource Blocks (RBs) for PSUs at any time and for different events. Furthermore, by using a Non-Orthogonal Multiple Access (NOMA) based system and the Particle Swarm Optimization (PSO) algorithm, the user throughput and the resource wastage problem are improved. Our goal is to provide the necessary resources to PSUs and, at the same time, to maximize the use of these resources. Compared to the traditional Orthogonal Frequency-Division Multiple Access (OFDMA) system, the simulation results show the efficiency of our proposed PSO-based NOMA system in terms of average user throughput and user sum-throughput, and also show that the relation between throughput and fairness among users is a requirement-dependent tradeoff, where we can achieve optimal fairness by decreasing the total throughput.

The autonomous underwater vehicle (AUV) has gradually become an important platform for performing various underwater tasks. Due to the shortcomings resulting from a single AUV's poor detection, information processing and moving capabilities, more and more tasks are completed in a cooperative manner by multiple AUVs. However, most of the existing works do not consider security factors in the process of multi-AUV cooperation. In this paper, we propose a novel cooperative tracking scheme towards an underwater moving target, performed by an intelligent AUV swarm. In this scheme, a cooperative multi-agent reinforcement learning (MARL) based tracking algorithm is proposed following a centralized training with distributed execution (CT-DE) manner. After centralized training in the designed secure private network, no information sharing is required during the mission execution. This feature ensures the security of the whole system, especially in a complex confrontation scenario. In addition, we build models of the AUV underwater dynamics and the target sonar detection, which make the algorithm applicable to real target tracking enabled AUV swarms. Then, based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we design an end-to-end AUV control algorithm. Simulation results validate that the proposed algorithm can achieve competitive performance in tracking success rate and tracking stability against baselines, while ensuring the security of the entire system.

Autonomous underwater vehicles (AUVs) have been envisaged as a key enabler for empowering the Internet of Underwater Things (IoUT) networks to address the challenge of ever-increasing demand of ocean exploration. However, the energy constraint of AUVs' movement makes it challengeable to obtain full-space movement and extravagant information exchange considering complex underwater environment and hostile acoustic channel characteristics. For the sake of enhancing the sustainability of the power supply, it is significant to design a green underwater information collection scheme for beneficially utilizing the maneuverability of AUVs. In this paper, we propose a heterogeneous multi-AUV aided underwater information collection scheme for optimizing the unit energy consumption under the constraint of the age of information (AoI). Moreover, the limited service M/G/1 vacation queueing system is used to model the process of information exchange, where the steady-state distribution and the waiting time of queue are derived. Finally, simulation results show the effectiveness of our proposed scheme and low-complexity solution, which outperform single-AUV scheme in terms of both energy efficiency and AoI.

In recent years, unmanned aerial vehicles (UAV) have been widely adopted to support complex target tracking tasks for military and civilian applications, especially in open and unknown environments. In practical cases, the moving trajectory of the target cannot be known to the UAVs in advance, which brings great challenges to UAVs to realize real-time and effective tracking. In addition, the limited tracking ability of a single UAV can hardly meet the requirements of a high tracking success rate. To deal with these problems above, this paper establishes a multi-UAV cooperative target tracking system. Besides, a deep reinforcement learning (DRL) based algorithm is designed to enable UAVs to make flight action decisions intelligently to track the moving air target, according to the past and current position information of the target only. To further increase the detection coverage of the UAV network when tracking, spatial information entropy is introduced to the reward designing in this algorithm. Simulation results validate that the proposed algorithm yields impressive target tracking performances, and significantly outperforms several common DRL baselines in terms of the tracking success rate. The convergence of the algorithm is also verified by the simulations.

At present, the development and exploration of the ocean are blossoming, but the maritime communication coverage still remains limited. By deploying unmanned aerial vehicle (UAV) mounted relay nodes between shore base stations and vessel users, the off-shore communication coverage and transmission efficiency can be substantially enhanced. Considering the specific transmission characteristics of air-sea and of air-shore channels and time-varying traffic of maritime information services, we formulate a minimum-maximization optimization problem of link capacity, where both the deployment of UAV-mounted relay node and the configuration of communication resources are optimized. To address this non-convex problem, we propose a particle swarm based algorithm, which is capable of three-dimensional position, antenna direction and time slot allocation scheme joint optimization. The simulation results demonstrate the high efficiency and reliability of our proposed algorithm in diverse offshore relay scenarios with different coastal environments, vessel distributions and network traffic.

With the emergence of computationally intensive and delay sensitive applications, mobile edge computing(MEC) has become more and more popular. Simultaneously, MEC paradigm is faced with security challenges, the most harmful of which is DDoS attack. In this paper, we focus on the resource orchestration algorithm in MEC scenario to mitigate DDoS attack. Most of existing works on resource orchestration algorithm barely take into account DDoS attack. Moreover, they assume that MEC nodes are unselfish, while in practice MEC nodes are selfish and try to maximize their individual utility only, as they usually belong to different network operators. To solve such problems, we propose a price-based resource orchestration algorithm(PROA) using game theory and convex optimization, which aims at mitigating DDoS attack while maximizing the utility of each participant. Pricing resources to simulate market mechanisms, which is national to make rational decisions for all participants. Finally, we conduct experiment using Matlab and show that the proposed PROA can effectively mitigate DDoS attack on the attacked MEC node.

Wireless sensing can enable human identification by quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric system, WirelessID, that explores the human fine-grained behavior and body physical signatures embedded in channel state information by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to identification performance. Thus, to extract robust features, we introduce an attention mechanism into our system. Particularly, commercial Wi-Fi devices are used for prototyping WirelessID in a laboratory with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.

With the development of robotics, the underwater robot platform has been widely used in the Internet of Underwater Things (IoUT). An underwater robot platform equipped with multiple sensors is used as a mobile collector to build a reliable information collection system for IoUT. This article presents the mechatronic design, fabrication, modeling, control, simulation, and experiments of a robot IoUT platform to enable the smart ocean. Inspired by the design of both fin-actuated swimming of fish and buoyancy-driven gliding of underwater glider, a novel multilink gliding fish robot is proposed. The multilink gliding fish robot, which is called FishBot in this article, can swim flexibly and glide energy efficiently in three dimensions. In the FishBot, the body and/or caudal fin (BCF) with three degrees of freedom and buoyancy-driven system was equipped as the main propulsion device. Besides, a pair of pectoral fins was equipped to assist in regulating the gliding attitude and enhance the FishBot maneuverability in the vertical plane. The dynamic model that consists of cruise swimming motion, pure-pitching swimming motion, and 3-D swimming motion for control is established. Moreover, a behavioral control framework is developed to achieve a variety of fish-like swimming behaviors and gliding motion. Meanwhile, we proved the stability of the linear quadratic regulation controller and the locomotion controller is provided with exponential stability. The validity of the proposed model and the designed controller is demonstrated by numerical simulations. Finally, a series of experiments involving different fish-like behaviors and gliding motion elucidates the powerful locomotion ability of the FishBot.

To a large extent, the deployment of edge computing (EC) can reduce the burden of the explosive growth of the Internet of Things. As a powerful hub between the Internet of Things and cloud servers, edge devices make the transmission of cloud to things no longer complicated. However, edge nodes are faced with a series of problems, such as a large number, a wide range of distribution, and complex environment, the security of EC should not be underestimated. Based on this, we propose a tactic to improve the safety of EC by virtualizing edge nodes. In detail, first of all, we propose a strategy of edge node partition, virtualize the edge nodes dealing with different types of things into various virtual networks, which are deployed between the edge nodes and the cloud server. Second, considering that different information transmission has different security requirement, we propose a security tactic based on security level measurement. Finally, through simulation experiments, we compare with the existing advanced algorithms which are committed to virtual network security, and prove that the model proposed in this article has definite progressiveness in enhancing the security of edge computing.

Currently, developments in wireless sensing technologies have shown that wireless signals can be employed to transmit information between wireless communication devices and are also able to realize passive target wireless sensing. Wireless sensing has diverse Internet-of-Things applications in indoor human detection, such as in device-free localization, activity recognition and fall detection, respiration detection, gait recognition, user identification, and so forth. Deep learning (DL), with the latest breakthroughs in machine learning (ML) and artificial intelligence (AI), seems to be a feasible technique for device-free wireless sensing (DFWS) and human detection in a more intelligent and autonomous manner. Although DL has attracted wide spread attention in computer vision (CV), AI games, speech recognition, automated vehicles, and other fields, its application in wireless sensing systems (WSSs) is relatively new, and little attention has been paid to it. Motivated by these developments, this article clarifies the motivation and mechanism of the DL-aided WSSs for human detection. First, we survey the most advanced architecture of DL that may be powerful for WSSs. We also review conventional ML and DL approaches to human detection based on red green blue (RGB)/depth camera and radar: one reason is to introduce the successful experience in these areas to the field of wireless sensing and another reason is that the possibility of combining and fusing information from the heterogeneous types of sensors is expected to improve the overall performance of practical human detection systems. We provide a comprehensive survey of the state-of-the-art research on wireless sensing for human detection with a focus on WSSs. Furthermore, a general structure of the DL-based WSS is introduced in detail for hitherto unexplored applications and future wireless sensing scenarios. We also discuss some open research issues in wireless sensing for human detection, including data acquisition for DL model training, calibration of signals from commercial devices, multimodal sensing, simultaneous user identification and activity recognition, multiuser human detection, and generalization ability of DL models, to indicate future research directions.

Nowadays the privacy issue arising in data mining applications has attracted much attention. In the context of distributed data mining, a major concern of the participant is that its privacy may be disclosed to other participants or a third party. To protect privacy, one can apply a differential privacy approach to perturb the data before sharing them with others, which generally causes a negative effect on the mining result. Thus there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a mediator builds a classifier based on the perturbed query results returned by a number of users. We propose a game theoretical approach to analyze how users choose their privacy budgets. Specifically, interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. Experimental results demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the accuracy of the classifier and the preserved privacy.

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This article studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST, and CIFAR-10 datasets to represent the data generated by IIoT. During the experiment, we employ the deep neural network model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.

Wireless network virtualization is basically the abstraction, isolation, and sharing of wireless resources among different entities. Consequently, virtualization provides great flexibility and higher network efficiency, and enables easier migration to new technologies in wireless networks. Traditionally, a wireless network virtualization controller manages the virtual resources (including radio resources and infrastructure resources) known as slices which are available to the Service Providers (SPs). The SPs then allocate their purchased resources to serve their subscribed mobile users. Such a centralized allocation decouples the Quality-of-Service (QoS) management by the SPs from the virtual resource management by the controller. In this paper, we propose a matching based wireless network virtualization resource allocation mechanism: a distributed three-sided (3D) matching between radio resources, physical infrastructure and mobile users. The Restricted Three-sided Matching with Size and Cyclic preference model (R-TMSC) is implemented to obtain a stable solution. Simulation results show that our proposed spectrum-oriented and user-oriented algorithms outperform the traditional resource allocation schemes. The spectrum-oriented algorithm enhances the user throughput and the system performance, within a lesser run time. Furthermore, for an increasing number of users, the proposed algorithms serve more users than traditional methods.

Replacing base stations with unmanned aerial vehicles (UAVs) to serve the communication of ground users has attracted a lot of attention recently. In this paper, we study the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirements. To handle the user scheduling problem, a heuristic algorithm based on matching and swapping theory is proposed first to allocate users that access UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem using logarithmic approximation. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem and a joint optimization algorithm is presented with the algorithm’s convergence and computational complexity. Finally, numerical results are provided to support the rationality of the proposed algorithm.

In this paper, we mainly studied the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirement. To track the joint optimization problem, a heuristic algorithm based on matching theory in cellular networks is proposed firstly to allocate users which connect the UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem by logarithmic approximation, and solved to get an optimal solution. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem for its near-optimal solution. Finally, numerical results are provided to support the rationality of the proposed algorithm.

The communication satellite equipped with phased array antennas can produce high power density by narrow spotbeams and thus can achieve high data rates. A large number of narrow spotbeams are required when the satellite provides multicast service for a group of distributed ground users, which limits the system capacity. In this paper, we propose a new satellite multicast scheme as taking advantage of beams with flexible direction and beamwidth generated by phased array antennas. Users are partitioned into multiple groups and an appropriate beam is allocated to each group, where all users in the same group are located in the mainlobe of the beam. It is formulated to jointly optimize user grouping and beamwidth for maximizing the average data rate of satellite and guaranteeing the quality-of-service (QoS) for users. Moreover, we develop an iterative algorithm to solve the problem with low complexity. Finally, simulation results show that our proposed method is superior to other schemes in terms of average data rate.

In this paper, we propose a hierarchical information acquisition system composed of a marine stationary sensor layer and an autonomous underwater vehicle (AUV) motion layer. Specifically, in the sensor layer, we design an energy-efficient clustering protocol based on the improved K-Means algorithm (ECBIK), which can implement uniform classification and select the cluster head dynamically according to energy awareness. Compared with the traditional K-Means and LEACH algorithm, our method achieves lower energy consumption and higher node survival rate, which can balance the energy load effectively to extend the life of the network. Additionally, in the AUV motion layer, we define the rotation-angle of AUV and analyze its influence quantitatively for the AUV information collection. Meanwhile, a novel Ant Colony (ACO) algorithm based on Markov Reward Process (MRP) is proposed for AUV path planning. As the simulation experiments indicate, our algorithm can achieve shorter distance, smaller angle, and faster convergence speed in path optimization.

This paper considers value based information collection for underwater acoustic sensor networks (UWASNs). In the considered system, the sensor nodes collect, store and update monitoring information with an initial value related to associated events. The value of information (VoI), however, decays with time. An autonomous underwater vehicle (AUV) is dispatched to retrieve data from the sensor nodes through acoustic communication. Our objective is to find the optimal traversal path for the AUV to maximize the VoI of the whole network. To achieve this goal, we first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environment, based on which the expression of the total VoI is derived. Then, we formulate the problem as a combinatorial optimization problem. We provide an optimal solution for this problem based on the branch and bound (BB) method, in which the lower bound (LB) and upper bound (UB) calculation strategies are specifically designed. A near-optimal heuristic algorithm based on the ant colony method is also adopted for further reducing computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

As a promising technology to improve the computation experience for mobile devices, mobile edge computing (MEC) is becoming an emerging paradigm to meet the tremendous increasing computation demands. In this paper, a mobile edge computing system consisting of multiple mobile devices with energy harvesting and an edge server is considered. Specifically, multiple devices decide the offloading ratio and local computation capacity, which are both in continuous values. Each device equips a task load queue and energy harvesting, which increases the system dynamics and leads to the time-dependence of the optimal offloading decision. In order to minimize the sum cost of the execution time and energy consumption in the long-term, we develop a continuous control based deep reinforcement learning algorithm for computation offloading. Utilizing the actor-critic learning approach, we propose a centralized learning policy for each device. By incorporating the states of other devices with centralized learning, the proposed method learns to coordinate among all devices. Simulation results validate the effectiveness of our proposed algorithm, which demonstrates superior generalization ability and achieves a better performance compared with discrete decision based deep reinforcement learning methods.

The shipping lane is one of the most important information of vessels, which is useful in maritime communications. This paper presents a cache-enabled maritime communication network architecture where the shipborne base stations with the cache storages serve as the relay points between the user ships and the shore based networks. Under this architecture, we promote the total network throughput by exploiting the specific characteristics of the maritime shipping lane information. In particular, we use the shipping lanes to calculate the large-scale channel state information (CSI) and predict the future user associations. We propose a shipping lane aware cross-layer resource allocation algorithm to optimize the system throughput in terms of the caching strategy, power allocation and bandwidth allocation, which is a mix-integer non-convex programming problem. We divide the original problem into a capacity optimization problem and a caching placement problem. The capacity optimization problem is relaxed into a large-scale-CSI-only convex problem and solved by Lagrangian dual method. The caching placement problem is solved by a greedy algorithm. The numerical simulation results show the effectiveness of the shipping lane information in optimizing the system throughput compared with the existing algorithms.

Satellite communication systems usually work in low signal-to-noise ratio (SNR) circumstances owning to the limited satellites' link budgets. Large doppler frequency offset in low-SNR satellite communication systems severely influences the performance of frequency synchronization, while the frequency offset correction still remains an open problem under low SNR condition especially for short burst transmission. To solve such a problem in satellite communications, we present a novel method named GP-MASO-MLE, which comprises a coarse correction based on the objective function with Gaussian Process (GP) search and a fine correction based on Maximum Likelihood Estimation (MLE) jointly with turbo iterations. Specifically, the proposed method is appropriate for non-data-aided frequency offset correction in satellite communication systems. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal frequency offset correction within 0.1 dB, moreover, the proposed algorithm has lower computational complexity compared with traditional multi-step search algorithms.

This paper proposes a distributed power control scheme based on the constrained model predictive control (MPC) for the underlay cognitive satellite terrestrial networks (CSTNs), where the primary satellite communication network coexists with the secondary terrestrial mobile network. We model this power control problem as a closed-loop dynamic control system with the inner loop and outer loop. On the basis of combining target power control (TPC) algorithm in the inner loop and tracking of flexible target signal to interference plus noise ratio (SINR) in the outer loop, we develop a corresponding state space expression of the problem where the fluctuation of each channel power gain is formulated as the exogenous disturbance input so that we do not need the accurate instantaneous channel state information (CSI). Then we design a SINR regulator in the outer loop, which is a constrained model predicted controller with rolling optimal operation subject to the interference temperature constraint obtained by calculating a linear matrix inequality. Finally, we obtain our constrained model predictive power control algorithm. In contrast to the previous static power control schemes based on the optimization theory that highly depend on the known instantaneous CSI and large signalling exchanges, the proposed scheme only needs locally measured information and outdate feedbacks. The performance of the proposed algorithm is shown to be effective through computer simulations.

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogenous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the capacity performance of the three-layer heterogenous satellite network. We first construct the network model and the capacity model of the network with detailed design of the network parameters. Then, taking time structure into account, we propose a time structure based augmenting path searching method, which can significantly reduce the computing complexity. Finally, based on the model and method proposed, we analyze the capacity performance of the three-layer heterogenous satellite network with numerical results.

Recently, the technology of underwater wireless sensors networks (UWSNs) has received more attention on the exploitation of marine resources. However, underwater acoustic communication is still the only reliable means of ocean communication, which is entirely different from the terrestrial scene. In this paper, we propose Q-learning aided ant colony routing protocol (QLACO) to address the issues of energy-efficiency and link instability in UWSNs, which uses both the reward mechanism and artificial ants to determine a global optimal routing selection. QLACO uses the reward function to adapt to the dynamic underwater environment and enhance the packet delivery ratio (PDR). Moreover, we propose an anti-void mechanism to solve the void region dilemma. Simulation results show that QLACO outperforms Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR) and the depth-based protocol (DBR) in terms of PDR, energy consumption and latency.

Providing reliable wireless communications for high-mobility terminals remains one of the main challenges faced by satellite high-mobility communication systems. Because the high Doppler frequency offset and Doppler rate caused by the high-mobility nature of the mobile terminal, and low signal-tonoise ratio (SNR) circumstances caused by limited satellites' link budgets degrade the system performance seriously. To solve such a problem in high-mobility satellite communications, we propose a novel modulation method named second order time-frequency (SOTF) modulation, which consists of index modulation (IM) and liner frequency modulation (LFM). Simulation results show that the bit error ratio (BER) performance of the proposed modulation method with different parameters is better than traditional modulation methods. Specially, the BER performance loss is about 0. 05dB in high-mobility communication scenarios, which demonstrates that the proposed modulation method is insensitive to Doppler frequency offset and Doppler rate. Overall, the proposed method can be well applied in high-mobility satellite communication systems for its good performance with moderate complexity.

The Internet of Underwater Things (IoUT) shows great potential in realizing the smart ocean. Underwater acoustic sensor networks (UWASNs) are the main existing form of IoUT but face with reliable data transmission problems. To tackle this issue, this article considers using the autonomous underwater vehicle (AUV) as a mobile collector to construct a reliable hierarchical information collection system while the Value of Information (VoI) is used as a main metric to measure the Quality of Information (QoI). We first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environments. Then, to construct a hierarchical architecture, we design a sink node (SN) selection scheme by jointly considering VoI conservation and energy load balancing. After that, we focus on AUV path planning with the objective of maximizing the VoI of the total network. We formulate the problem as a combinatorial optimization problem and provide an integer linear programming (ILP) model for this problem. An optimal algorithm based on the branch-and-bound (BB) method is proposed for seeking for the optimal solution, in which the lower bound and upper bound calculation strategies are specifically designed. Two near-optimal heuristic algorithms based on the concepts of the ant colony algorithm (ACA) and the genetic algorithm (GA) are also provided for further reducing the computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

In recent years, we have witnessed the compelling application of the Internet of Things (IoT) in our daily life, ranging from daily living to industrial production. On account of the computation and power constraints, the IoT devices have to offload their tasks to the remote cloud services. However, the long-distance transmission poses significant challenges for latency-sensitive businesses, such as autonomous driving and industrial control. As a remedy, mobile edge computing (MEC) is deployed at the edge of the network to reduce the transmission delay. With the MEC joining in, how to allocate the limited computing resource of MEC is a critical problem to guarantee efficient working of the whole IoT system. In this article, we formulate the resource management among MEC and IoT devices as a double auction game. Also, for searching the Nash equilibrium, we introduce the experience-weighted attraction (EWA) algorithm performing behind each participant. With this AI method, auction participants acquire and accumulate experience by observing others' behavior and doing introspection, which accelerates the trading policy's learning process of each agent in such an opaque environment. Some simulation results are presented to evaluate the convergence and correctness of our architecture and algorithm.

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

Satellite communication systems are able to provide diverse services for ground terminals in ubiquitous global coverage, which play a vital role in high-mobility communication environments. Existing technologies developed primarily for satellite communications cannot be readily applied to satellite high-mobility communication scenarios, since high Doppler frequency offset caused by the fast movement of wireless terminals, and low signal-to-noise ratio (SNR) circumstances caused by limited link budgets in satellites incur more difficulty of the synchronization, especially for short burst transmission. To solve such a problem in satellite high-mobility communications, we propose a novel method named GP-MASO-MLE, which consists of a coarse estimation algorithm based on the Gaussian process (GP) model and Newton-Raphson method, and a fine correction algorithm based on the improved maximum likelihood estimation (MLE) jointly with turbo decoding iterations. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal Doppler frequency offset correction within 0.1 dB, which can be well applied in code-aided (CA) satellite high-mobility communication systems for its good performance. In addition, the computational complexity of the proposed algorithm is lower than other traditional turbo synchronization algorithms.

Transmit antenna selection (TAS) is an emerging technology in physical layer security. To provide new insights into the achievable secrecy performance of TAS in practical communication systems, this paper investigates the average secrecy rate (ASR) and secrecy outage probability (SOP) under practical modulation schemes in TAS aided multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channels over Rayleigh fading. Particularly, this research concentrates more on the square M-ary quadrature amplitude modulation (M-QAM). Furthermore, in the considered MIMOME channel, a single antenna is selected to transmit the secret message, and selection combining (SC) or maximal-ratio combining (MRC) is utilized at the legitimate receiver and the eavesdropper. Based on this system model, novel expressions for the ASR and SOP are formulated to characterize the secrecy performance of the finite-alphabet driven MIMOME channel. Besides exact analysis, an asymptotic analysis is performed using the considered performance metrics in high signal-to-noise ratio (SNR) regime. Theoretical analyses suggest that the asymptotic ASR and SOP converge to finite constants in high SNR regime due to the discrete constellation constraint, and we find that the asymptotic behaviour of discrete inputs differs from that of Gaussian inputs. Furthermore, we derive concise expressions to characterize the rate of convergence (ROC) of the ASR and SOP, respectively. To unveil more system design insights, we discuss the relationship between the ROC and several important system parameters such as the antenna number and the modulation order.

To analyze the achievable performance of antenna selection (AS) in practical multi-antenna systems, this paper studies the receive antenna selection (RAS) in single-input multiple-output (AS-SIMO) systems under discrete inputs. We first propose an approximate expression to evaluate the instantaneous mutual information (MI) of M-ary quadrature amplitude modulation (M-QAM) signaling over additive white Gaussian noise (AWGN) channels. Then, by exploiting this approximate formula, we develop a closed-form formula for the ergodic MI in AS-SIMO systems with M-QAM signaling. Additionally, we also analyze the asymptotic MI for a large number of receive antennas Nr. This asymptotic analysis suggests that the scaling rate of the MI with Nr becomes zero rate in contrast to the double logarithmic rate under Gaussian inputs. Besides, our result is also extended to discuss the mutual information of multiple-input multiple-output (MIMO) systems having discrete inputs with receive antenna selection, and an upper bound for the MI is derived. Finally, the derived result is applied to analyze several performance measures of the discrete inputs driven ASSIMO systems. Specifically, it is first used to discuss the relationship between the ergodic MI and the number of active antennas. Our investigation shows that this relationship follows Pareto principle, i.e., 80% of the MI of full-antenna selection can be achieved via 20% of the total antennas. Then, our proposed approximation is employed to analytically study the effective MI which takes channel estimation (CE) into consideration, indicating that CE is a main limit of large-scale systems. Moreover, the energy efficiency (EE) is explored on the basis of our results, and we find there exists an optimal number of active antennas to maximize the energy efficiency. In addition to theoretical derivations, all the analytical results are validated by numerical simulations.

In this paper, we investigate a multicast beamforming terrestrial-satellite cooperation system to optimize the communication capacity and quality of service. Different from traditional link-based terrestrial network, we design the terrestrial and satellite beamforming vectors cooperatively based on the required contents of users in order to realize more reasonable resource allocation. Meanwhile, the backhaul links between content provision center and satellite and base stations are limited, and the users always need high quality of service, considering these, our object is maximizing the sum of user minimum ratio under the constraints of resource allocation, backhaul link and quality of service in reality. We first formulate the optimization problem and propose a joint optimization iterative algorithm to design the beamforming vectors of satellite and base stations cooperatively. Then, to obtain the global optimum solution, we propose a Bound-based algorithm and solve the optimization problem by shrinking the upper bound and lower bound of the optimization feasible region. To decrease the complexity, we then design a heuristic scheme to solve the problem. The simulation results show that, our proposed cooperative optimization algorithms have better performance than non-cooperative methods, and the heuristic scheme has little poor performance but has significant advantage in low complexity.

Relying on the computation offloading technology, edge computing has shown potential in countless tasks processing in the industrial Internet of Things (IIoT), which is composed of multiple edge clouds and multiple IIoT devices. Nevertheless, with increasing demands for computation service, how to design reliable transmission mechanism and allocate proper computation resource has become bottlenecks. In this article, we propose a computation offloading mechanism based on two-stage Stackelberg game to analyze the interaction between multiple edge clouds and multiple IIoT devices. To be specific, the edge clouds are denoted as leaders who set the appropriate price for their computation resource. Besides considering the payment cost, the IIoT devices which are termed as the followers formulate their utility function by considering the social interaction information from the potential IIoT devices. The existence and uniqueness of the Stackelberg equilibrium are analyzed considering two possible cases, i.e., complete information and incomplete information. Moreover, two dynamic iterative algorithms are invoked for solving both problem models, respectively. Finally, experimental results show that our proposed scheme is conducive to seeking the appropriate price and computation requirement. Besides, social interaction information plays an important role in achieving a reasonable computation requirement for IIoT devices.

To achieve the goal of “Industrial 4.0,” cellular network with wide coverage has gradually become an intensely important carrier for industrial Internet of Things (IIoT). The fifth generation cellular network is expected to be a unifying network that may connect billions of IIoT devices for the sake of supporting advanced IIoT business. In order to realize wide and seamless information coverage, heterogeneous network architecture becomes a beneficial method, which can also improve the near-ceiling network capacity. In order to guarantee the quality of service (QoS) as well as the fairness of different IIoT devices with limited network resources, the network association in IIoT should be performed in a more intelligent manner. In this article, we propose a distributed Q-learning aided power allocation algorithm for two-layer heterogeneous IIoT networks. Moreover, we discuss the spirit of designing reward functions, followed by four delicately defined reward functions considering both the QoS of femtocell IoT user equipments and macrocell IoT user equipments and their fairness. Also, both fixed and dynamic learning rates and different kinds of multiagent cooperation modes are investigated. Finally, simulation results show the effectiveness and superiority of our proposed Q-learning based power allocation algorithm.

Mobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users.

Network Virtualization (NV) techniques allow multiple virtual network requests to beneficially share resources on the same substrate network, such as node computational resources and link bandwidth. As the most famous family member of NV techniques, virtual network embedding is capable of efficiently allocating the limited network resources to the users on the same substrate network. However, traditional heuristic virtual network embedding algorithms generally follow a static operating mechanism, which cannot adapt well to the dynamic network structures and environments, resulting in inferior nodes ranking and embedding strategies. Some reinforcement learning aided embedding algorithms have been conceived to dynamically update the decision-making strategies, while the node embedding of the same request is discretized and its continuity is ignored. To address this problem, a Continuous-Decision virtual network embedding scheme relying on Reinforcement Learning (CDRL) is proposed in our paper, which regards the node embedding of the same request as a time-series problem formulated by the classic seq2seq model. Moreover, two traditional heuristic embedding algorithms as well as the classic reinforcement learning aided embedding algorithm are used for benchmarking our prpposed CDRL algorithm. Finally, simulation results show that our proposed algorithm is superior to the other three algorithms in terms of long-term average revenue, revenue to cost and acceptance ratio.

Recently, the emergence of blockchain has stirred great interests in the field of Internet of Things (IoT). However, numerous non-trivial problems in the current blockchain system prevent it from being used as a generic platform for large-scale services and applications in IoT. One notable drawback is the scalability problem. Lots of projects and researches have been done to solve this problem. Nevertheless, they do not consider different users' conditions, only using a single consensus protocol as the best fit one, as well as the IoT system is heavily constrained by computing and networking resources. In this article, we study a permissioned blockchain-based IoT architecture. In order to improve the scalability of the blockchain system and meet the needs of different users, we propose a service-oriented permissioned blockchain, where different consensus protocols are launched according to users' quality of service (QoS) requirements. Specially, we quantify a few popular consensus protocols. Additionally, we select block producers, which need a great number of computation resources, as well as dynamically allocate network bandwidth to the blockchain system. We formulate consensus protocols selection, block producers selection, and network bandwidth allocation as a joint optimization problem. We then use a dueling deep reinforcement learning approach to solve the problem. Simulation results demonstrate the effectiveness of our proposed scheme.

To meet the ever-increasing demands for mobile video streaming, proactive caching over the network edge has been proposed as a promising solution for next generation wireless networks. In this paper, we consider the trace-driven cache-enabled video streaming design in the scenario of a metropolis to boost the spectral efficiency on the system side and the quality of experience (QoE) on the user side. A novel scheme to jointly provide proactive caching, power allocation, user association and adaptive video streaming is designed via the formation of a QoE-aware throughput maximization problem. Specifically, the caches are refreshed in the content placement phase according to the resource status and expected traffic, which is obtained by exploring the traces collected over a big city. In addition, users need to be associated with a proper small base station (SBS) in the content delivering phase to provide the highest attainable rate. We demonstrate the effectiveness of the proposed scheme via experiments conducted over real user trace datasets.

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogeneous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the problem of capacity management in the three-layer heterogeneous satellite network. We first construct the model of the network and propose a low-complexity method for calculating the capacity between satellites. Based on the time structure of the time expanded graph, the searching space is greatly reduced compared to traditional augmenting path searching strategies, which can significantly reduce the computing complexity. Then, based on Q-learning, we proposed a long-term optimal capacity allocation algorithm to optimize the long-term utility of the system. In order to reduce the storage and computing complexity, a learning framework with low-complexity is constructed while taking the properties of satellite systems into account. Finally, we analyze the capacity performance of the three-layer heterogeneous satellite network and also evaluate the proposed algorithms with numerical results.

Standard blind interference alignment (sBIA) suffers from noise accumulation which severely deteriorates received signal-to-noise ratio (SNR) and significantly reduces transmission rate. A noise accumulation factor is proposed to describe the loss between the user's received SNR, and the final post processing SNR which determines the performance of the decoding of the encoded data streams (EDSs). A heterogeneous semi-BIA (H-SBIA) framework where users with different noise accumulation factors can be flexibly allocated effective EDSs (E-EDSs) is constructed. Relying on the H-SBIA framework, a heuristic H-SBIA algorithm is designed for maximizing the overall E-EDSs considering both fairness and coherence time constraints. Extensive simulations demonstrate that H-SBIA produces great fairness performance improvement at a limited cost in the achievable sum rate. The Jain's fairness index is about 2.2 times greater than that for SNR-SBIA proposed in previous work, at the cost of sacrificing 10% of the achievable sum rate.

The tremendous increase of computation-heavy applications has posed great challenges in terms of enhanced service coverage and high-speed data processing in the Fifth Generation (5G) networks. As responding, the integrated fog and cloud computing (FCC) system has been expected as an efficient approach to support low-latency and on-demand computing services. This work considers the computing resource market in an FCC system operated by one cloud computing service provider (CCP) and multiple fog computing service providers (FCPs), in which the CCP shares its cloud computing resource among FCPs and itself to serve users with computational tasks. To facilitate the resource trading between the CCP and FCPs, a Stackelberg differential game based resource sharing mechanism is proposed. In this mechanism, performance discrepancy is introduced as a penalty factor to denote the mismatch between the resource supply and demand, which will encourage all computing providers (CPs) to make their trading decisions that can truthfully reflect their resource capacity and requirements. In addition, an evolutionary game based replicator dynamics is established to analyze the users' service selection among CPs. Based on the established hierarchical game framework, interactions between user selection and computing resource sharing are investigated. The performance of the designed resource sharing mechanism is validated in the simulations, which also reveal the convergence and equilibrium states of user selection, resource pricing and resource allocation.

Random access is a key technology in satellite communication, as a large number of machine- type communication (MTC) terminals accessing the satellite makes it difficult to guarantee the access quality. Irregular repetition slotted ALOHA (IRSA) is one random access protocol relying on transmitting irregular number of replicas in multiple time slots, achieving a peak throughput at 0.8 in practical implementations. However, the probability of sending a certain number of replicas stays the same when given degree distribution, without considering the effects of different loads, which means there are extra useless packets sent and brings power waste in IRSA. Therefore, enhanced irregular repetition slotted ALOHA (EIRSA) based on tracking degree distribution control (TDDC) algorithm is proposed in this paper with adaptive degree distribution adjustment scheme to reduce the number of replicas while maintaining the same access performance with adaptation. Simulation results show that proposed protocol can achieve higher performance at the same power level and it is adaptive to load change.

In ultra-dense networks, the increasing demand for wireless services has led to severe energy consumption problem. In this paper, we mainly focus on green communication and computation offloading in ultra-dense networks, constituted of different macro base stations and small-cell base stations. This paper jointly considers edge energy consumption and delay under the limited network resource for multiple users.To address this issue, we propose an efficient computation offloading scheme in multi- user multi-task scenario, and a cuckoo search algorithm is invoked for solving the computation offloading problem. To be specific, the global convergence analysis presents the validity of this computation offloading scheme. Finally, experimental results validate that our proposed scheme is conducive to improving the efficiency of entire system in ultra-dense networks.

Due to the easy deployment, low cost and high maneuverability, unmanned aerial vehicles (UAVs) serving as aerial base stations can be efficiently deployed according to realtime situations for providing high-quality coverage, which can improve the communication efficiency and meet the requirements of green communications. However, due to the finite flight energy, a single UAV has limited capability of providing seamless long-term service to ground users. Therefore, the cooperation of multiple drones relying on sophisticated recharging and reshuffling schemes is necessary. In this paper, we investigate an energy- efficient cooperation strategy of multi-UAVs for providing seamless long-term coverage, where the positioning and the flight strategy are jointly considered. We first introduce a novel UAV power model, based on which we derive the cyclic UAV recharging and reshuffling constraint in order to satisfy the seamless long-term coverage requirement. For maximizing the energy-efficiency, we introduce a two-stage joint optimization algorithm for solving both the optimal UAV deployment as well as the cyclic UAV recharging and reshuffling strategy (CRRS). Finally, the efficiency of our proposed algorithm is shown by the simulation results.

The multibeam satellite system (MBSS) has great potential for mobile communications in 5G era due to its superiority in terms of extensive coverage, large capacity and real-time service. In order to integrate the resource allocation in multiple dimensions and maximize the system capacity of the MBSS, the scenario of a sparse network with dense users is selected to investigate the resource allocation method in time dimension, frequency dimension, space dimension and power dimension. We first propose a multilevel clustering algorithm and a cross-cluster grouping algorithm to realize the beam scheduling, by which the interference is reduced in time dimension and space dimension. Based on the beam scheduling scheme, we further explore the relationship between the system capacity and the resource allocation in frequency dimension and power dimension, where a joint power allocation and subchannel selection algorithm is proposed to optimize the spectral efficiency. Our simulation results show that the proposed multiple-dimension resource allocation method is superior to the existing methods in system capacity and convergence, which is not only applicable for the resource allocation in the MBSS but also provides an efficient approach to solve the coupling resource allocation problem.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. However, some of them are incompetent in tackling with computation-intensive tasks due to limited processing capability and battery life. In this paper, we combine the mobile edge computing and traditional cloud computing techniques for offloading the tasks from multi-UAV systems. Specifically, we jointly optimize the task scheduling and resource allocation in the heterogeneous cloud architecture, where we strike a power-delay trade-off of the system relying on the queue theory and Lyapunov optimization, followed by its optimal strategy analysis in each time slot. Moreover, we conceive an iterative algorithm with a closed-form solution at each iteration round in order to reduce the computational complexity. Finally, numerical results demonstrate both the feasibility and effectiveness of our proposed scheme. This paper validates that the heterogeneous cloud structure can be the beneficial for improving quality-of-service performance of multi-UAV systems.

Satellite image is an important resource for weather forecast. It can indicate the evolution of weather systems and is beneficial in terms of guiding people to make accurate weather forecasting. However, the use of satellite images is encountered with the dilemma of such as small data volume and of poor real-time performance. Hence it is important to make accurate prediction for satellite images. The goal of satellite image prediction is to predict the next few images of the image sequence. Essentially, it is a a spatiotemporal sequence prediction problem, where the prediction of satellite images is difficult due to its large-scale observation area. In this paper, we propose a generative adversarial networks-long short-term memory (GAN-LSTM) model for the satellite image prediction by combining the generating ability of the GAN with the forecasting ability of the LSTM network. For evaluation, we conduct our experiments on the FY-2E satellite cloud maps. In addition, we use a score correct rate (CR) to measure the degree of similarity between predictions and ground truth. Experiment results show that the proposed GAN-LSTM network is capable of efficiently capturing the evolution rules of weather systems, which outperforms the traditional autoencoder-LSTM.

Nowadays, more and more researchers are paying their attention to green routing. In this paper, we consider power consumption as a kind of QoS (quality of service) and apply a new learning-based approach for energy efficient transportation and QoS routing. Compared with traditional rule-based methods, the proposed method can learn additional information from the networks to improve routing performance, and have the flexibility to meet different QoS requirements. First, we propose a new identification of network nodes, namely node vectors, and a basic routing algorithm using node vectors is designed accordingly. Then, energy efficient transportation and QoS routing are proposed by adding QoS constraints into the routing decision. Link attributes such as power consumption, bandwidth and delay can be learned from these node vectors with neural networks. The learned link attributes together with the estimated distance can be used for routing decisions with QoS constraints. Simulation results show that the proposed method is reliable in routing tasks, and can achieve a remarkable performance when compared with the state-of-the-art work on the delay constrained least cost path (DCLC) problem.

In this paper, we propose a distributed detection system for hierarchical information acquisition based on autonomous underwater vehicle (AUV) and underwater fixed sensor networks. Different from the previous information collection systems, where the AUV traverses each node to obtain information, we propose a layered network architecture in this work, which is composed of an underwater fixed sensor networks layer and an AUV information acquisition layer. Such information acquisition system does not need to modify the original underlying fixed sensor networks, resulting from its flexible deployability. Additionally, because of the power sensitivity of sensor nodes in underwater fixed sensor networks, an improved algorithm based on classical low energy adaptive clustering hierarchy (Leach) algorithm is proposed in this work. Simulation results validate that the proposed algorithm can effectively improve the life cycle of sensor networks. At the same time, for the AUV information acquisition layer, we propose an angle optimization path planning algorithm based on the ant colony algorithm, which effectively takes the angle and path length as joint optimization objects. Experiments show that introducing the angle optimization jointly not only helps to optimize the AUV rotation angle, but also contributes to improving the convergence of the algorithm.

Recently, increasingly heterogeneous wireless networks are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing technology paradigms. By achieving an efficient spectrum sharing among heterogeneous networks (HetNets), traffic offloading is a promising solution for boosting the capacity of traditional macro-cell networks. In this paper, a cognitive spectrum sharing and traffic offloading mechanism is proposed to realize the cooperation and competition between the macrocell base station (MBS) and small-cell base stations (SBSs). Under the cooperation mode, the MBS stops occupying a corresponding channel, and a selected SBS helps offload the traffic from the MBS by exclusively using this channel. To facilitate the offloading negotiation between the MBS and SBSs, we design a secondprice auction mechanism, which presents positive allocative externalities, i.e., other uncooperative SBSs can benefit from the cooperation between the MBS and the SBS performing offloading. Meanwhile, the unique optimal biding strategies for different SBSs to achieve the symmetric Bayesian equilibrium are derived and obtained in this paper. The performance of the proposed cognitive traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MBS to achieve the maximum utility.

Recently, caching techniques have been regarded as efficient approaches to alleviate the data traffic loaded over backhaul channels, which can reduce the transmission delay and improve the quality and experience of video services. This work investigates a small-cell based caching system composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, different VSPs have their caching requirements, and the MNO, who manages and operates its small base stations (SBSs), will assign these SBSs' storage to VSPs for placing videos. Considering different video popularities and MUs' preferences of VSPs, the caching service brings different utilities to VSPs, as well as that providing caching service to different VSPs causes distinct costs to the MNO. However, such privacy information of utility and cost cannot be aware of among VSPs and the MNO. In addition, malicious VSPs may break the fairness of caching systems by requesting undeserved caching resource. Concerning these problems above, this paper designs a secure caching mechanism based on double auction, which can encourage both the MNO and VSPs to truthfully report their acceptances and requirements of caching resource, respectively. Moreover, the proposed caching mechanism ensures the efficient operation of market by maximizing the social welfare. The performance and economic properties of the designed caching mechanism are validated with simulation results.

With the development of Next-generation Wireless Networks (NWNs), delay-sensitive traffic triggered by mobile applications (such as video stream and online games) will become an important part of the NWNs. With the increasing demand for massive video content transmission and good quality of users' experience, NWNs have to face up to some serious challenges. As a remedy, efficient routing schemes are capable of achieving load balance. In this article, we propose a load balance routing based on machine learning. First, a dimension-reduced vector matrix can be obtained from the original adjacency matrix of the network topology by Principal Component Analysis (PCA). Then, a neural network is used for the prediction of the network queue status, which can be used as a metric for making intelligent routing decisions. Finally, a load balance routing algorithm considering Queue Utilization (QU) is designed accordingly. Simulation results show the performance of our proposed machine learning-based routing scheme compared to the shortest path algorithm (Bellman-Ford (BF)) and its variant (QUBF) in terms of the packet loss ratio, the throughput and the delay.

Physical (PHY)-layer security has received considerable interest as a way to safeguard data confidentiality and achieve security and privacy in wireless networks. Authentication between two devices is a challenging problem. In this paper, a machine learning algorithm is proposed to detect and identify rogue transmitters relying on a low-dimensional channel feature vector that is obtained by the Karhunen-Loeve transform (KLT). Specifically, a Linde-Buzo-Gray algorithm is designed for improving the reliability and robustness of the proposed scheme, where a Gaussian Mixture Model (GMM) is employed to learn and track the changes of physical layer properties. Simulation results demonstrate that the proposed authentication scheme achieves a higher spoofing detection rate compared to other existing methods.

Unmanned aerial vehicle (UAV) communication is a promising technology for Internet of Things (IoT) systems. In this paper, we combine UAV communication and nonorthogonal multiple access (NOMA) for constructing high capacity IoT uplink transmission systems, where UAVs are used as aerial base stations for collecting data from IoT nodes while NOMA is invoked for uplink transmission. We aim to maximize the system capacity by jointly optimize the subchannel assignment, the uplink transmit power of IoT nodes, and the flying heights of UAVs. We commence by proposing an efficient subchannel assignment algorithm relying on the classic K-means clustering method and matching theory. Then, we determine both the distributed uplink transmit power of IoT nodes and flying heights of UAVs based on successive optimization approach. An alternative optimization algorithm is also proposed for finding the near-optimal solutions. Finally, the numerical results demonstrate the superiority of our proposed scheme.

Due to their high flexibility, high maneuverability, and line-of-sight (LOS) predominant channel, unmanned aerial vehicles (UAVs) serving as flying base stations have received a lot of interest in emerging Internet of Things (IoT) networks. This article studies the energy-efficient cooperative strategy of rechargeable multi-UAVs for providing seamless coverage and long-term information services for IoT nodes. Considering the limited cruising duration of the UAV, multiple rechargeable UAVs are capable of constructing a closed chain for the sake of alternately supporting IoT nodes. Moreover, a joint IoT node assignment and UAV configuration optimization problem is proposed in order to maximize the energy efficiency of the system. Since the proposed problem is a mixed-integer nonconvex problem, we divide it into three subproblems, namely, node assignment scheduling, UAV trajectory planning, and transmit power control. By exploiting sequential convex optimization techniques, we reformulate the nonconvex subproblems into three convex optimization problems which can be solved within the polynomial time. A block coordinate descent-based iterative algorithm is proposed for solving these energy-efficiency oriented subproblems. Finally, the simulation results corroborate the effectiveness of our proposed method.

With the developments of communication technologies and smart manufacturing, Industrial Internet of Things (IIoT) has emerged. Software-defined networking (SDN), a promising paradigm shift, has provided a viable way to manage IIoT dynamically, called software-defined IIoT (SDIIoT). In SDIIoT, lots of data and flows are generated by industrial devices, where a physically distributed but logically centralized control plane is necessary. However, one of the most intractable problems is how to reach consensus among multiple controllers under complex industrial environments. In this paper, we propose a blockchain (BC)-based consensus protocol in SDIIoT, along with detailed consensus steps and theoretical analysis, where BC works as a trusted third party to collect and synchronize network-wide views between different SDN controllers. Specially, it is a permissioned BC. In order to improve the throughput of this BC-based SDIIoT, we jointly consider the trust features of BC nodes and controllers, as well as the computational capability of the BC system. Accordingly, we formulate view change, access selection, and computational resources allocation as a joint optimization problem. We describe this problem as a Markov decision process by defining state space, action space, and reward function. Due to the fact that it is difficult to solve this joint problem by traditional methods, we propose a novel dueling deep Q-learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In this paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

With rapid development of compelling application scenarios of the Internet of Things (IoT), such as smart cities, it becomes substantially important to strengthen the management of data traffic in IoT networks. Traffic classification is beneficial in terms of both ensuring network security and improving quality of service. Traditional IoT traffic classification methods separate the classification algorithm and the design of feature engineering, which includes feature extraction and feature selection. Then, traffic identification or classification is performed by combining both. This paper proposes an end-to-end IoT traffic classification method relying on a deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection, and classification model. Our proposed traffic classification method beneficially eliminates the process of manually selecting traffic features, and is particularly applicable to smart city scenarios. To the best of our knowledge, this is the first time that capsule networks have been used in the context of traffic classification. Experimental results show the feasibility and effectiveness of our proposed traffic classification mechanism, which yields high classification accuracy.

In order to beneficially exploit the scarce wireless spectral resources, spectrum sharing between communication and radar systems has become a promising research topic. However, traditional network association strategies may not result in efficient hybrid communication and radar systems. We circumvent this problem by formulating a partially observable Markov decision processes (POMDP) aided network association scheme, where the radar user acts as the primary user (PU), while the cognitive communication user is the secondary user (SU). For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, the communication user is configured for adaptively selecting its underlay or overlay access mode. Moreover, a low-complexity near-optimal reinforcement learning algorithm is proposed for the co-design by considering both its complexity and feasibility. Finally, we quantify the performance of our proposed POMDP based network association scheme.

Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. Recent researches on UAV systems feature in the dramatic augment of the variety and number of equipped sensors, which results in such an issue that multiple UAVs cannot afford to handle the big data generated by a range of sensors in the air. Considering this practical problem, in this paper, we propose a cloud-based UAV system which incorporates the computing capability of the terrestrial cloud into the UAV systems. Relying on proposed cloud-based UAV system, one critical theoretic issue is how to acquire the big data generated by the sensors while guaranteeing a stable operation state of the system. First, we analyze the cloud-based system's on-demand service ability as well as its impact on UAVs' control procedure. Second, the UAV cloud control system is modeled as a network control system. Moreover, the stable condition of the UAV cloud control system is derived, which reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of our theoretical analysis.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and the cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a power-delay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme.

Index modulation (IM) is an attractive concept for next generation communication systems. However, as an emerging technique, there are still some challenges need to be tackled in this frontier. In this paper, we consider two aspects of IM, which are diversity and detection complexity. Specifically, we propose a linear precoding assisted index modulation (LPIM) scheme for orthogonal frequency division multiplexing (OFDM) systems. We commence by analyzing the diversity and coding gains of the proposed scheme. Moreover, a detailed codebook design criterion is proposed. Then, our LPIM codebook is designed based on the maximum diversity and coding gain criteria. In contrast to the signaling model of the existing full diversity precoder designed for OFDM, we introduce a modeling method to link the zero-valued IM symbols to the origin of a Lattice for implementing our full diversity precoder designed for OFDM-IM. Both analytical and computer simulation results are provided for characterizing the attainable performance of OFDM-LPIM, demonstrating that it is capable of achieving full diversity as well as an attractive coding gain. However, the maximum-likelihood (ML) detection complexity of OFDM-LPIM is excessive, hence a low-complexity generalized iterative residual check detector (GIRCD) is proposed, which is inspired by the existing sparse recovery algorithms. Finally, computer simulation results are provided for demonstrating that GIRCD can provide a beneficial trade-off between bit error ratio performance and complexity.

Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.

With the development of online applications based on social networks, many different approaches have emerged to evaluate the service that these applications provide. Reports made by end users regarding the consumer's experience or opinion are commonly used to rate the quality of different online services. Therefore, ensuring the authenticity of the users' reports, and the detection of malicious users' dishonest reports, have both become important issues to achieve accuracy in the rating of such services. In this paper, we propose and evaluate a private-prior peer prediction-based trustworthy service rating system, which requires users to report their prior and posterior beliefs regarding whether their peers will report a high-quality opinion of the service. The reports are made to a data processing center which evaluates the users' trustworthiness by applying a strictly proper scoring rule, and removes reports received from users whose trustworthiness rating is low. This peer prediction method is compatible with incentives to motivate users to report honestly. In addition, an unreliability index is proposed to identify malicious users, and malfunctioning or unreliable users who have a high error rate in making judgments about quality. Thus, reports with high unreliability values will also be excluded from the service rating system. By combining trustworthiness and unreliability, malicious users face the dilemma that they cannot receive both a high trustworthiness and low unreliability rating simultaneously when their reports are false. Simulation results indicate that the proposed peer prediction-based trustworthy service rating can identify malicious and unreliable behaviors effectively and motivate users to report truthfully, and that a relatively high service rating accuracy is achieved by the proposed system.

Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals' privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users' privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users' opinions, and the values are updated according to users' privacy loss. Moreover, the user can make a tradeoff between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others' privacy, and the proposed bandit approach can bring the user a high payoff.

Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

As more mobile users become subscribers of content services, their subscribed content can be directly pushed from the content provider into the user equipment after the content is generated. In current and future network paradigms, a joint wired and wireless transmission design for this pushing is needed to guarantee the user experience without the extra deployment of communication infrastructures or consumption of resources. In this paper, we investigate a joint wired and wireless content delivery system that incorporates wired and wireless multicast. The users in the same group are served by wireless multicast from a base station (BS), while the BSs of the same content form a multicast tree in a backbone wired network. The sum of wired and wireless traffic is minimized by a joint design of user grouping, subchannel allocation, wired routing, and wired link usage. Exploiting the monotonicity of wired and wireless traffic with regard to the wired hop count, the original problem is converted for searching the optimal hop count vector that achieves the minimum sum of both types of traffic, which is solved by a monotonic optimization (MO)-based iterative algorithm. Compared with existing schemes and according to the numerical results, a reduction in total traffic of 43% can be achieved by our approach.

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs' storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs' caching utilities and MNO's caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results.

Pushing contents from content providers (CPs) directly to user equipments (UEs) during off-peak hours can significantly reduce the incurring traffic during peak hours. Both wired and wireless transmission costs are considerable in this scenario, especially in current cellular networks where the backhaul is regarded as a bottleneck of transmission. In order to alleviate the traffic pressure over the network without loss of users' quality of experience, this paper presents a joint wired and wireless transmission scheme which considers grouping, subchannel allocation, wired routing, and wired bandwidth allocation. An iterative algorithm is proposed to achieve the tradeoff between those two kinds of traffic. The users in the same group are served by a single multicast transmission from a base station (BS), while the BSs which multicast the same content form a multicast tree in backbone wired network. Compared with traditional unicast scheme, our approach can reduce the wired, wireless, and total traffic by 23%, 46%, and 34% at most according to the simulation results.

With growing human maritime activities, supporting low-cost and high-speed information services for users at sea has become an imperative focus. In this paper, we consider a maritime relay multicast system including a shore-based base station and several offshore relay nodes, and propose an energy efficient relay design scheme. Specifically, we formulate the relay design problem as a power minimization problem under users' quality-of-service (QoS) constraints, and the problem is approximated and solved using the feasible point pursuit successive convex approach. Furthermore, an iterative algorithm is proposed with exponential complexity. In order to reduce the computational complexity, a low-complexity distributed algorithm is conceived and its closed-form solution is derived. Finally, simulation results show that our proposed scheme is beneficial in terms of achieving a higher communication rate as well as of yielding a better energy efficiency.

Large scale network systems such as Internet, smart grids and social networks become an indispensable part of our daily life. However, due to their inherent vulnerability as well as the limited management and operational capability, these network systems are constantly under the threat of malicious attackers. In such attack-defense scenarios, it is particularly significant to make the best use of defenders' limited resources and capability. In this paper, we propose a networked Colonel Blotto game, where the attackers and defenders allocate the limited resources on network nodes, and their utility depends on certain network performance metrics, which are defined for evaluating the performance of the whole network system. Furthermore, considering the complexity of the equilibrium analysis in large scale network systems, a co-evolution based algorithm is proposed for obtaining the practical action sets as well as achieving the mixed-strategy Nash equilibrium. Finally, relying on three real- world network systems, i.e., computer networks, Internet of vehicles and online social networks, simulation results show the effectiveness and feasibility of our proposed model, which is conducive to the design, management and maintenance of real-world network systems.

The increasing maritime activities with the exclusive economic zone lead to increasing demands for wideband communications in recent years. This paper presents a maritime communication network architecture where the onshore high-tower base station provides wireless backhaul for the shipborne base stations, while the shipborne base stations serve as the mobile access points for the user ships. Under this architecture, considering the out-of-band full-duplex (OBFD) backhauling mode, we propose a joint backhaul and access link resource management scheme in respect to the wireless backhaul power allocation, access link power allocation and user association, for the sake of maximizing the network capacity. Specially, this scheme takes the inter-cell interference and the multiuser association into account in order to model the realistic maritime communication scenarios. The optimization problem is solved by the successive convex approximation (SCA) method and the numerical simulation results show the effectiveness of the algorithm in terms of the network capacity and the iteration convergence.

Unmanned aerial vehicles (UAVs) cooperating with satellites and base stations (BSs) constitute a space-air-ground three-tier heterogeneous network, which is beneficial in terms of both providing the seamless coverage as well as of improving the capacity for the users. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks. In our paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem. Furthermore, Lagrange dual decomposition and concave-convex procedure (CCP) method are used to solve this problem. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

In the scenario of frequency coexistence between the GEO (geostationary) and NGEO (non-geostationary) satellite networks, the NGEO system should not incur harmful interference to the GEO system according to the policy of the Radio Regulations. Therefore, spectrum sensing as a promising solution is applied widely in this scenario. With the increasing number of NGEO satellites in the space, one NGEO system could be affected by other NGEO systems while sensing the signal from the GEO system. Given these preconditions, the cognitive radio (CR) scenario considered in this paper is that: the GEO system is regarded as the primary user, one NGEO system is regarded as the secondary user, while another NGEO system is regarded as the interfering user. Meanwhile, all the satellite systems are supposed to operate with more than one discrete transmit power levels which is practical and fits the concept of adaptive power control. In our context, we propose a spectrum strategy using hypothesis testing as well as maximum a posterior (MAP) to differentiate the GEO signal from the interfering NGEO and noise, and then identify the specific power level utilized by the GEO system. Moreover, we derive the closed-form expressions for threshold of verifying the status of the GEO, and for decision regions to determine its power level. Finally, extensive simulations are provided to verify the proposed studies.

Relying on mini wearable or implantable biosensors, the wireless body area network (WBAN) is capable of efficiently collecting as well as of analyzing human physiological information. It has shown great potential in terms of beneficially improving healthcare quality. However, due to stringent resource constraints of biosensors, traditional security schemes, i.e. the encryption and the authentication, may not do well in countering security threats. Moreover, they are not competent in protecting the network from inside attacks and deny of service (DoS) attacks. In this paper, we propose a sink node assisted lightweight intrusion detection mechanism for WBAN, where the sink node can periodically monitor the packet transmission and record the abnormality for further analysis. Our lightweight mechanism results in a very high true positive rate and an ultra-low false positive rate. Extensive analysis and simulations based on Castalia are conducted and verify the validity and efficiency of our proposed mechanism.

In order to beneficially exploit wireless spectral resources, spectrum sharing between communication systems and radar systems has become a popular research topic. However, traditional network association strategies may not result in an efficient co-system. We circumvent this problem by formulating a partially observable Markov decision process (POMDP) aided network association scheme. For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, communication users are capable of adaptively selecting underlay or overlay access mode. Moreover, a near-optimal reinforcement learning algorithm is proposed considering both the computational complexity and feasibility. Finally, simulations are conducted in order to evaluate the effectiveness of our proposed POMDP based network association scheme.

In the tracking and data relay satellite system, users compete for the limited inter-satellite link antenna beam (ILAB) resource by excessive resource requests. This can lead to serious user request conflicts and considerably degrade the QoS (quality of service) of the system. Since those selfish users have no incentive to cooperate in a one-shot resource request, in this paper we construct a cooperation mechanism to maximize users' payoffs and meanwhile to reduce the conflict relying on a repeated game framework. Moreover, an efficient punishment and forgiveness strategy is proposed to prevent any user from breaking the cooperation. Simulation results confirm that the proposed scheme can significantly improve the system's operation performance. Compared with the payoffs under the Nash equilibrium in the one-shot game which is the traditional scheme, our repeated ILAB resource allocation game can acquire 1.36 to 3.46 times gain in respect to 2 to 10 users, respectively.

In this paper, we propose an architecture of cloud based integrated terrestrial-satellite networks, in which satellite and terrestrial networks that belong to the same operator cooperatively provide seamless coverage for mobile users. Meanwhile, a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Then, based on the delay constraint of users, we formulate the resource allocation problem for the operator to minimize the energy consumption. By decomposing the optimization problem into two subproblems and utilizing the theory of multidimensional knapsack problem, we eventually obtain the optimal resource allocation strategies for the operator. Furthermore, numerical results are provided to evaluate the performance of the proposed strategies.

The unprecedented growth of the volume of mobile data calls for novel approaches that improve the sharing of data allowances among mobile users with diverse needs. Specifically, the Wi-Fi hotspot function of current smartphones allows mobile-to-mobile offloading, but requires fast and efficient transactions between mobile users. Thus we propose an auction-based approach to allow the transfer of data allowances between mobile users with excess and deficits of data allowances, together with a cognitive approach to access the needed information about the system. The objective is to optimize the income of “sellers” and satisfy the needs of the other mobile users. Analytical and simulation results are presented, showing that by taking advantage of mobile users’ behaviors, and of varying demands of data allowance selling and buying, the cognitive auction and data allocation mechanism can significant improve the overall performance of the mobile data allowance transaction system.

Currently, The unprecedented increasing of mobile data traffic challenges the performance of current cellular networks. To meet this explosive demands of mobile traffic, the mobile data offloading technology has been proposed to alleviate the traffic load by moving traffic load of cellular networks to other wireless networks provided by infrastructures such as small-cell base stations. In this work, an infrastructure-free offloading method is proposed, which realizes the data transaction among mobile users by applying the hotspot function of smartphones. In this transaction, mobile users with redundant data perform as accessible Wi-Fi hotspots, and sell their mobile data to users with data requirements. Considering the scenarios with multiple data sellers, a networked auction model is introduced to model the process of data transaction. Additionally, high efficient data allocation mechanisms are designed in this work, which decide how to schedule the data transaction in different time slots, based on the establish edauction model. Simulation results indicate that introducing the prediction information of user behaviors can effectively improve the performance of data allocation, and achieve a high efficient data transaction operation.

In remote mobile social networks, caching is a very promising technique to alleviate the burden of space cloudlet (e.g., cache-enabled satellite user terminal) and to improve subscribers' user experience in terms of content retrieval latency. In this paper, we proposed a social relationship aware caching placement approach for remote mobile social networks. Social relationships between users are used to designate a set of helpers with caching capability, which can cache popular files proactively when the cloudlet is idle. Furthermore, the caching placement problem is formulated as an optimization problem to minimize the average content retrieval latency. Then, we reformulate the problem into a monotone submodular optimization problem with a partition matroid constraint; moreover, an efficient greedy algorithm with 1-[1/e] approximation ratio is proposed to solve it. Simulation results show that the proposed social aware greedy caching placement approach significantly outperforms the traditional approaches in terms of content retrieval latency and hit ratio.

In view of the compelling applications in both military and civilian fields, wireless sensor networks (WSNs) have attracted an unprecedented focus on their easy configuration and low cost. Due to the openness of wireless media and constrained resources of WSNs, it is of paramount importance to timely discern the malicious intrusion and unauthorized manipulation. In this paper, we engage in providing an intrusion detection mechanism relying on a novel multi-criteria game. In our model, the interaction between potential attackers and defenders is formulated as a two-player non-zero-sum multi-criteria game, where multiple objectives, i.e. the information security, reputation and energy consumption, are considered when searching for the Pareto equilibrium. Moreover, a light weighting strategy is proposed in order to construct the payoff vector. Finally, simulation results and theoretical analysis show the effectiveness and feasibility of our proposed mechanism.

The wireless body area network (WBAN) is a new-type wireless sensor network which has a steep demand for improving energy efficiency and reducing packet delay. However, in a multi-priority environment, the current IEEE Std. 802.15.6 MAC protocol for WBAN may result in excess transmission delay and power consumption due to the selfishness of high-priority sensor nodes. To overcome the deficiency, in this paper, a contention-oriented node sleeping MAC protocol is proposed. The MAC protocol utilizes a contention orientation mechanism between different contention levels to achieve a fair resource allocation. Furthermore, the sleeping scheme of redundant nodes yields energy efficiency. Finally, simulation results show that our proposed protocol outperforms 802.15.6 MAC, AD-MAC as well as DTD-MAC protocols in terms of both packet delay and energy efficiency.

With the growing popularity of mobile social networks, point-of-interest (POI) recommendation, which utilizes users' check-in data to suggest interesting places for users, has attracted much attention in recent years. The check-in data, containing time and location information, are closely related to the user's personal life. Due to privacy concerns, users are reluctant to share check-in data with the service provider (SP), which causes a negative effect on recommendations. It is important for the user to find a balance between privacy and recommendation quality. In this paper, we consider a POI recommendation scenario where an adversary can access the data that a user reports to the SP. The user sequentially decides whether to check in for the POI he has visited. A stochastic game model is proposed to analyze the interaction between the user and the adversary. To find a good policy for the user, two value iteration algorithms are applied. The proposed game has a large state set, which makes it difficult for policy learning. To deal with this problem, we use some tricks when implementing the minimax Q-learning algorithm, and a set of neural networks are trained to approximate the Q-functions. To evaluate the performance of the learning algorithms, we conduct a series of simulations by using real-world check-in data. Simulation results show that the proposed learning algorithms can help the user to make good decisions, in the sense that the user can get a high long-term return.

Recently, hybrid satellite-terrestrial networks (H-STNs) are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing and interference control technology paradigms. By achieving an efficient spectrum sharing among H-STN, traffic offloading is a promising solution for boosting the capacity of traditional cellular networks. In this paper, a software-defined network-based spectrum sharing, and traffic offloading mechanism is proposed to realize the cooperation and competition between the ground base stations (BSs) of the cellular network and beam groups of the satellite-terrestrial communication (STCom) system. Assume that all BSs are operated by the same mobile network operator (MNO). Under the cooperation mode, all the BSs stop occupying a corresponding channel, and a selected beam group of the satellite helps offload the traffic from the BSs by exclusively using this channel. To facilitate the offloading negotiation between the MNO and satellite, we design a second-price auction mechanism which presents positive allocative externalities, i.e., other uncooperative beam groups of the satellite can benefit from the cooperation between BSs and the beam group performing offloading. Meanwhile, the unique optimal biding strategies for different beam groups of the satellite to achieve the symmetric Bayesian equilibrium as well as the expected utility of the MNO are derived and obtained in this paper. The performance of the proposed traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MNO to achieve the maximum expected utility.

In this paper, we consider a scenario where the satellite-terrestrial network is overlaid over the legacy cellular network. The established communication system is operated in the millimeter wave (mmWave) frequencies, which enables the massive antennas arrays to be equipped on the satellite and terrestrial base stations (BSs). The secure communication in this coexistence system of the satellite-terrestrial network and cellular network through the physical-layer security techniques is studied in this paper. To maximize the achievable secrecy rate of the eavesdropped fixed satellite service, we design a cooperative secure transmission beamforming scheme, which is realized through the satellite's adaptive beamforming, artificial noise, and BSs' cooperative beamforming implemented by terrestrial BSs. A non-cooperative beamforming scheme is also designed, according to which BSs implement the maximum ratio transmission beamforming strategy. Applying the designed secure beamforming schemes to the coexistence system established, we formulate the secrecy rate maximization problems subjected to the power and transmission quality constraints. To solve the nonconvex optimization problems, we design an approximation and iteration-based genetic algorithm, through which the original problems can be transformed into a series of convex quadratic problems. Simulation results show the impact of multiple antenna arrays at the mmWave on improving the secure communication. Our results also indicate that through the cooperative and adaptive beamforming, the secrecy rate can be greatly increased. In addition, the convergence and efficiency of the proposed iteration-based approximation algorithm are verified by the simulations.

In this paper, we investigate the downlink cooperative multigroup multicast transmission in the integrated terrestrial-satellite network, in which base stations (BSs) and the satellite provide the multicast service for ground users in a cooperative manner while reusing the entire bandwidth. For both terrestrial BSs and the satellite, multiantennas are equipped and beamforming techniques are utilized for improving the system performance. Based on the architecture, we formulate a weighted max-min fair (MMF) beamforming design problem to jointly optimize the beamforming vectors of BSs and the satellite, which is solved based on the relation between the quality of service problem and the MMF problem. When it comes to the large scale case, where large numbers of BSs are distributed within the coverage of the satellite, we propose a time division cooperative multigroup multicast scheme consisting of two phases for the BSs and the satellite. Then, an iterative algorithm is proposed to solve the weighted MMF problem in the time division case. Finally, numerical results are provided to evaluate the cooperative multicast schemes as well as the proposed algorithms.

Social networks have attracted billions of users and supported a wide range of interests and practices. Users of social networks can be connected with each other by different communities according to professions, living locations, and personal interests. With the development of diverse social network applications, academic researchers, and practicing engineers pay increasing attention to the related technology. As each user on the social network platforms typically stores and shares a large amount of personal data, the privacy of such user-related information raises serious concerns. Most research on privacy protection relies on specific information security techniques such as anonymization or access control. However, the protection of privacy depends heavily on the incentive mechanisms of social networks, like users' psychological decisions on security execution and socio-economic considerations. For example, the desire to influence the behaviors of other people may change a user's choice of security setting. In this paper, a game theoretic framework is established to model users' interactions that influence users' decisions as to whether to undertake privacy protection or not. To model the relationship of user communities, community-structured evolutionary dynamics are introduced, in which interactions of users can only happen among those users who have at least one community in common. Then the dynamics of the users' strategies to take a specific privacy protection or not is analyzed based on the proposed community structured evolutionary game theoretic framework. Experiments show that the proposed framework is effective in modeling the users' relationships and privacy protection behaviors. Moreover, results can also help social network managers to design appropriate security service and payment mechanisms to encourage their users to take the privacy protection, which can promote the spreading of privacy behavior throughout the network.

Advanced informatics technologies facilitate the construction of green smart cities, especially the Wi-Fi implementation and management, for rapidly increasing personal Wi-Fi devices in autonomous environments residing in nonoverlapped channels often result in low energy efficiency and severe cochannel interference. In this paper, a green Wi-Fi management framework is constructed in order to reduce the overall energy consumption through turning off a portion of access points (APs) and aggregating their users to the other active APs. A Tabu-search-assisted active AP selection algorithm is proposed to minimize the power consumption with a seamless wireless converge. For the active APs, based on our defined metric airtime cost that is integrated by the in-range interference and the hidden terminal interference, a reinforcement-learning-aided AP self-management algorithm is proposed to dynamically adjust APs' channels in the partially overlapped channel space. Extensive simulations and field experiments demonstrate that the power consumption can be reduced by about 65%, and the airtime cost of APs can be reduced by 50% compared with the typical least congestion channel search algorithm.

As a promising solution for the next generation mobile networks (5G) deployment, long term evolution-unlicensed (LTE-U) is expected to improve spectrum utilization and channel capacity, which is beneficial for the industrial Internet of Things integrating many heterogeneous networks. However, such technology migration can potentially induce severe interference to the devices and networks originally operating on unlicensed bands, especially to the Wi-Fi network. In this paper, first of all, we emulate the coexistence of LTE-U networks and Wi-Fi networks, and experimentally evaluate their mutual interference relying on deploying time division duplex based OpenWrt wireless routers. Moreover, an LTE-U and Wi-Fi coexistence mechanism is proposed, where users with diverse traffic demands are capable of accessing either the public LTE-U network or its nearby Wi-Fi network. Finally, an access point selection algorithm with the aid of coalition formation game is developed for improving the system's throughput. Sufficient simulations based on Network Simulator 3 demonstrate that the overall throughput of the LTE-U and Wi-Fi hybrid cosystem outperforms that of the single LTE-U network as well as of the single Wi-Fi network.

Mobile communication technology provides more service paradigms to social networks, allowing the development of mobile social networks (MSNs). An important scenario of MSNs is crowdsensing, which takes advantage of simple sensing and computation abilities on the portable devices of ordinary people, and fuses the sensing results to accomplish large-scale tasks. In crowdsensing, the integration of individual sensing data from users is of great significance, yet highly depends on the goal of tasks. In this paper, we propose a high-level distributed cooperative environmental state inference scheme based on non-Bayesian social learning, which can be applied to various crowdsensing tasks, e.g., traffic monitoring, air quality monitoring, and weather forecasting. In the proposed scheme, users exchange information with their neighbors and cooperatively infer the hidden state, which is the goal of the crowdsensing task but cannot be measured directly. We prove theoretically that every user is able to asymptotically learn the hidden state, even when the users locations and relationships keep changing, and when some users cannot observe signals or provide their own inferences. We also optimize the weight matrix in the fusion step by maximizing the learning speed.

With the emergence of big data computing and analysis, cloud computing services become more and more popular, which has recently drawn researchers' great attentions to develop various new applications and mechanisms. In this paper, we consider the on-demand mechanism design in the infrastructure as a service (IaaS), including resource allocation and pricing issues under dynamic scenarios. Most of existing works on mechanism design assumed static and independent individual utility, while the cloud computing services are provided in a dynamic environment. To solve such problems, we start with analyzing the Google cluster-usage dataset to draw the statistical and stochastic characteristics of the IaaS consumers and providers. Based on the characteristics mined from real data, we propose a stochastic matching algorithm with Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency, with its online version using Q-learning method to address the imperfect model estimation problem. We further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism, which is an extension of traditional Vickrey-Clarke-Groves (VCG) mechanism. The proposed mechanism is studied under two application scenario: quality sensitive services, where unilateral MDP-VCG auction is implemented; and quality insensitive services, where MDP-VCG double auction is implemented. To verify the performance of our proposed mechanism, we conduct experiment using the Google dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.

In millimeter wave (mm-wave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mm-wave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that is imposed by the effect of power leakage, angular spread, and cluster duration. We extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems associated with a broad bandwidth.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service and the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems, two of which are linear and the rest can be solved by introducing a Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

We consider auction mechanism design and performance analysis for data transactions in mobile social networks. Existing mobile network plans can result in some users ending a monthly plan with excess data, while others may have to pay a costly fee to buy more data. Thus we suggest data auctions with a single seller, or a multiple-seller networked data auction, that operate in mobile social networks, to deal with the asymmetry between extra unused data resources and urgent data demands. Based on earlier work on the analysis of auctions, we design the data transaction mechanism, and summarise the analysis on state transmission, stationary probabilities of the system, and the expected income for data sellers. To improve the efficiency and performance of the system, socially- aware mobility models are also proposed. The proposed data auction mechanisms and friendship-based mobility model are then simulated as operating on Flickr, a real-world online social network database. Results show that the number of data bidders in different auctions can be balanced through the proposed mobility model, and also increase the income per unit time of sellers in the networked data auction.

This paper presents a latency-efficient mobile video streaming design in the context of metropolis by incorporating caching. It shows that video traffic can be substantially offloaded from backhaul by caching predictable demands in the network edge. Notably, exploiting the spatial and temporal characteristics of video popularity, we focus on two sub problems: how to cache the content and how to associate users. Firstly, we investigate cache deployment strategy based on clients' viewing behavior in both downtown and suburb. The proposed hybrid collaborative filtering (CF)-based scheme guarantees high hit rate utilizing the available storage capacity in small base stations (SBS). Further, we formulate the dynamic user equipment and SBS (UE- SBS) optimal association problem into a convex optimization problem, so as to maximize the sum transmission rate of SBSs under the resource and quality-of-service constraint. Performance evaluation of real trace data demonstrates the significant advantage of our proposed framework.

The proliferation of information technologies results in a complex network evolution of online social networks. Traditional model driven aided description cannot be appropriate for the dynamic evolution of social networks. However, in this paper, relying on the big data collected from a range of real-world online social networks, we try to explore the underlying evolution for online social networks. Firstly, we define a pair of big data driven similarity based utility models (U- models), i.e. the undirected U-model as well as the directed U-model, which can effectively reflect the statistical characteristics of online social networks. Secondly, we analyze the small-world property, scale-free property and high clustering coefficient property of our proposed U-models which consider nodes' similarity, popularity and asymmetry in a network. Finally, relying on three real-world big datasets, i.e. Sina Weibo, Tencent Weibo and Twitter, sufficient experiments show that the U-models outperform the traditional models in portraying the evolution statistical characteristic of online social networks.

With the rapid increase of mission demands for the tracking and data relay satellite system (TDRSS), the technical issue of high-efficient scheduling has attracted more attention in recent years. Most of previous scheduling algorithms are designed based on the assumption of missions' uniform time- space distribution, which have showed unsatisfactory performance in real scenarios with non-uniform distribution of mission demands. In this paper, we first transform the TDRSS scheduling problem into the heterogeneous inter- satellite link antenna (ILA) pointing route problem. Then, a two-stage heuristic algorithm with hierarchical scheduling strategies is proposed with the consideration of non-uniform time-space distribution of missions. Finally, we employ the TDRSS dataset to verify our proposed algorithm by comparing with the improved Rojanasoonthon's greedy randomized adaptive search procedure (GRASP) algorithm. Experimental results show that our proposed two-stage heuristic algorithm can schedule 2.41%, 4.43% and 6.02% more missions and consume 11.84%, 10.38% and 9.54% less setup times of SA antennas than the improved GRASP algorithm for the mission scale of 200, 400 and 600, respectively. In addition, setup times of SA antennas in those instances with non-uniform distribution in space can be more efficiently compressed by our proposed two-stage heuristic algorithm.

Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. With the increasing complexity of application scenarios, the coordination of multiple UAVs has become a hot topic. However, the limited capability of UAVs make it hard to achieve stable and reliable control. Considering this practical problem, we propose a cloud-based UAV system. It extricates the computing and data storage from UAVs and utilizes the cloud to process the sensor data and to maintain the stable operation of multi-UAV systems. Firstly, we analyze the cloud-based system's on-demand service ability and its impact on UAVs' control procedure. Secondly, we propose a UAV cloud control system (CCS) which serves as a network control system. Moreover, the stable condition of the UAV cloud control system is derived. It reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of previous theoretical analysis.

The privacy issue arising in data mining applications has attracted much attention in recent years. In the context of distributed data mining, the participant can employ data perturbation techniques to protect its privacy. Data perturbation generally causes a negative effect on the mining result, which means there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a number of users provide data to a mediator to train a classifier. Interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. The basis idea is that the user gradually reduces the perturbation in data until it is satisfied with the classification accuracy. Experimental results based on real data demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the classification accuracy and the preserved privacy.

Terrestrial-satellite networks (TSNs) play a significant role in achieving 100\% geographic coverage in the next generation of wireless networks. In TSNs, multimedia transmission is an important application scenario, where efficient content delivery solutions are required for effectively alleviating network congestion. In this paper, we study multicast beamforming problems in TSNs by reusing the entire bandwidth for providing efficient solutions for content delivery. In order to mitigate the interference imposed by multicast transmission of the satellite, we formulate the cooperative multicast beamforming problems for the TSN under the quality of service (QoS) constraints. Then, according to different considerations, the semidefinite relaxation (SDR) method is applied for the beamforming design of the satellite, whilst the recently developed feasible point pursuit successive convex approximation (FPP-SCA) approach is adopted for tackling the beamforming design problem of the base station. The system performance is studied by simulation results. Our investigations show that our TSN is capable of attaining a sufficient high rate for supporting multimedia transmission.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service (QoS), the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

Next-generation wireless networks are expected to support exponentially increasing number of users and demands of data, which all rely on the essential media: spectrum. Notice that the 5G networks are featured either by wide bandwidth like mmWave systems, or by large-scale antennas like massive MIMO. We find those two trends, together with waveforming or beamforming, can lead to a common phenomenon: the spatial focusing effect. Based on this focusing effect, we propose a general spatial spectrum sharing framework that enables concurrent multi-users spectrum sharing without the requirement of orthogonal resource allocation. Simulation results show that both TR wideband and massive MIMO system can achieve equivalently high throughput performance with the spatial spectrum sharing scheme.

In data relay satellite (DRS) systems, the performance of tasks scheduling is influenced by the variation of task and resources, which degrades the processing capacity of relay satellites. Considering this problem, we investigate the dynamic scheduling in the application of DRS. To achieve the efficient resource utilization and reliable data transfer, the strategies of task preemptive switching and decomposition are designed. Based on the initial scheme, we construct a dynamic scheduling model with multiple objectives, including maximizing the total weight of scheduled tasks, minimizing the change of scheduling scheme and minimizing the number of decomposed subtasks. Meanwhile, a preemptive dynamic scheduling algorithm (PDSA) is designed to solve the proposed model. Explicitly, our simulation results show that PDSA is superior to the whole rescheduling algorithm (WRA) in quantities of completed tasks, rescheduling rate of scheme and processing time, which can efficiently improve the performance of dynamic scheduling in DRS systems.

How to improve the information diffusion coverage rate in socially-aware mobile networks has drawn great attention. To address this issue, the concept of the tie strength, the partial strength and the value strength were proposed in order to achieve a superior criterion for information diffusion. However, the previous works did not consider the existence of various patterns among the nodes in socially-aware mobile networks. In this paper, we propose the asymmetric normalization forms of the partial strength as well as the value strength. Moreover, we explore the essence of asymmetry and its influence on information diffusion relying on analyzing the characteristics of graph structures as well as information local traps. Simulation results on the real-world social network and on the mobile network verify that our proposed asymmetric normalization forms are beneficial to promoting information diffusion.

Thanks to recent advance in massive social data and increasingly mature big data mining technologies, information diffusion and its control strategies have attracted much attention, which play pivotal roles in public opinion control, virus marketing as well as other social applications. In this paper, relying on social big data, we focus on the analysis and control of information diffusion. Specifically, we commence with analyzing the topological role of the social strengths, i.e., tie strength, partial strength, value strength, and their corresponding symmetric as well as asymmetric forms. Then, we define two critical points for the cascade information diffusion model, i.e., the information coverage critical point (CCP) and the information heat critical point (HCP). Furthermore, based on the two real-world datasets, the proposed two critical points are verified and analyzed. Our work may be beneficial in terms of analyzing and designing the information diffusion algorithms and relevant control strategies.

How to enhance security and stability of the cyber physical systems (CPSs) becomes a critical issue. In this paper, we propose a solution from the perspective of users' information diffusion process in CPCs. Relying on the virus propagation model, we conceive an idle-carrier-idle (ICI) model to characterize the information diffusion. Moreover, `effective diffusion rate' is defined in order to benchmark the efficiency of users' information diffusion. Based on the complex network theory, the threshold of the information diffusion both in homogenous networks and in free-scale networks is derived. Furthermore, a pair of game models are proposed for efficiently controlling the information diffusion and then for enhancing the security of the system. Furthermore, the equilibriums of two proposed games are demonstrated. Finally, essential numerical analysis and simulations show the effectiveness and feasibility of our proposed models.

This paper investigates a multimedia multicast beamforming scheme in the integrated terrestrial-satellite networks, where base stations (BSs) and the satellite work cooperatively provide ubiquitous services for ground users. Due to the contents diversity of multimedia services, users that request the same contents can be served as a group using multicasting. By utilizing multiple transmission antennas, multicast beamforming is performed among groups while reusing the entire bandwidth, which, however, can inevitably cause the co-channel interference among users. Taking both system performance and user fairness into account, we optimize the total system capacity performance under the satellite capacity constraint and derive the optimal power allocation schemes. Numerical results are presented in the end to evaluate the effectiveness of the proposed scheme compared with the greedy and suboptimal searching strategies.

The increasing traffic demand in both ground and satellite communication systems will lead to increasing spectrum demand. Spectrum sharing would become a challenging issue in future between terrestrial and satellite systems with frequency reusing, as well as the interference management. Upon this, we propose the concept of the Cloud Based Integrated Terrestrial-Satellite Network (CTSN), where both base stations of the cellular networks and the satellite are connected to a cloud central unit and the signal processing procedures are executed centrally at the cloud. By utilizing the channel state information (CSI), the interference from the mixed signal can be mitigated. When it comes to the case of imperfect CSI, we propose a resource allocation scheme in respect to subchannel and power to maximize the total capacity of the terrestrial system while limiting the total interference to the satellite. The optimization problem is solved by means of the dual decomposition method. Simulation results are provided to evaluate the effectiveness of the algorithm.

In the fifth-generation (5G) networks, millimeter- wave (mmWave) bands have drawn great attention for the large amount of possible bandwidth. Meanwhile, satellite communications have also shown great interest in the mmWave bands, especially the Ka band. Under such a circumstance, the spectrum sharing between the satellite and terrestrial communication systems becomes prominent. In this paper, we analyze the interference caused by terrestrial cellular systems to the geostationary (GEO) system in two transmission modes. In order to protect the GEO system, we construct a protection radius where the terrestrial transmitters must locate outside. Simulations are conducted to verify the effectiveness of the proposed scheme.

Device-to-device (D2D) communication is a promising technology to improve spectrum efficiency, energy efficiency, and transmission delay due to proximity of user equipments (UEs). With cooperative D2D communications, a UE needs to select an optimal peer to act as a relay to forward data to the base station (BS). Therefore, it is essential to take into account the social relationships among UEs during peer selection so as to enhance data privacy. In this paper, we investigate a dynamic social-aware peer selection problem by formulating it as a dynamic optimization problem and proposing the drift-plus-penalty ratio algorithm to solve it. Simulation results show that the proposed peer selection scheme outperforms other existing schemes while keeping the handover frequency lower than others.

Earth satellite networks are playing an increasingly important role in observation, surveillance and reconnaissance of specific targets or areas with dramatic growing of the demand for such services. The harsh and vulnerable space environment makes satellite nodes susceptible to a variety of attacks and prompts us to protect satellite networks by securing them with efficient defending strategy. In this paper, we propose a satellite scheduling problem in a vulnerable space environment. A set of complex operational constraints is imposed to make the best defending strategy between task requests and satellites under protection. To reduce the complexity, we decompose the original problem into two subproblems of satellite scheduling and satellite protection. A joint optimization algorithm is adopted to find near optimal solution. The results of extensive computational experiments show its effective and superior scheduling performance.

Relying on clustering and the cluster head selection algorithms, vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) based vehicular ad hoc networks (VANETs) play a critical role in intelligent transport system (ITS). However, the existing clustering and cluster head selection algorithms did not consider the influence of the vehicles' communication contents and their correlations. Specifically, the power-law characteristics of vehicle content demands are beneficial in terms both of achieving efficient clustering algorithm and selecting optimal cluster heads. In order to simulate the real vehicular communication scenarios, we commence with the mobility model design in this paper. Moreover, a novel clustering algorithm relying on content demands is proposed, which attracts vehicles to adopt V2V network through price advantage. Furthermore, based on the Fermi rule, i.e., one of the stochastic evolutionary strategies in complex networks, and evolution game, our cluster head selection algorithm is capable of representing more realistic vehicles' features, including selfishness, fairness and bounded rationality. Finally, the effectiveness and feasibility of our proposed algorithms are verified.

In heterogeneous ultra-dense networks (HetUDNs), the software-defined wireless network (SDWN) separates resource management from geo-distributed resources belonging to different service providers. A centralized SDWN controller can manage the entire network globally. In this paper, we focus on mobile traffic offloading and resource allocation in SDWN-based HetUDNs, constituted of different macro base stations and small-cell base stations (SBSs). We explore a scenario where SBSs' capacities are available, but their offloading performance is unknown to the SDWN controller: this is the information asymmetric case. To address this asymmetry, incentivized traffic offloading contracts are designed to encourage each SBS to select the contract that achieves its own maximum utility. The characteristics of large numbers of SBSs in HetUDNs are aggregated in an analytical model, allowing us to select the SBS types that provide the off-loading, based on different contracts which offer rationality and incentive compatibility to different SBS types. This leads to a closed-form expression for selecting the SBS types involved, and we prove the monotonicity and incentive compatibility of the resulting contracts. The effectiveness and efficiency of the proposed contract-based traffic offloading mechanism, and its overall system performance, are validated using simulations.

In a cooperative network, the user equipment (UE) shares information for cooperatively achieving a common goal. However, owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is a paucity of information on modeling the UEs' decision making behavior, namely as to whether they are willing/able to share genuine information, even though this directly affects the information credibility across the network. Hence, we propose a game theoretic framework for the associated information credibility modeling by taking into account the users' information sharing strategies and utilities. This framework is investigated under both a homogeneous model and a heterogeneous model. The spontaneous information credibility equilibria of both models are derived and analyzed, including the closed-form analysis of the homogeneous model based on a sophisticated evolutionary game model and on the reinforcement learning-based analysis of the heterogeneous model. Moreover, a credit mechanism is designed for encouraging the UEs to share genuine information. Experimental results relying on real-world data traces support our utility function formulation, while our simulation results verify the theoretical analysis and show that all the UEs are encouraged by the proposed algorithm to share genuine information with a probability of one, when a credit mechanism is invoked. The proposed modeling techniques may be applied in diverse cooperative networks, including classic wireless networks, vehicular networks, as well as social networks.

Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology are expected to increase both energy efficiency and spectral efficiency. In this paper, user association and power allocation in mmWave-based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. The joint user association and power optimization problem are modeled as a mixed-integer programming problem, which is then transformed into a convex optimization problem by relaxing the user association indicator and solved by Lagrangian dual decomposition. An iterative gradient user association and power allocation algorithm is proposed and shown to converge rapidly to an optimal point. The complexity of the proposed algorithm is analyzed and its effectiveness compared with existing methods is verified by simulations.

With the wide adoption of smart mobile devices, there is a rapid development of location-based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivizes a large group of mobile users to participate. In this paper, we investigate the problem of designing a crowdsourcing tournament to maximize the principal's utility in crowdsourcing and provide continuous incentives for users by rewarding them based on the rank achieved. First, we model the user's utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Finally, we present numerical results to show the parameters' impact on the tournament design and compare the system performance under the different proposed incentive mechanisms. We show that by using the tournament, the principal successfully maximizes the utilities, and users obtain the continuous incentives to participate in the crowdsourcing activity.

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max-min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.

Recently, the conflict between exploiting the value of personal data and protecting individuals' privacy has attracted much attention. Personal data market provides a promising solution to this conflict, while determining the price of privacy is a tough issue. In this paper, we study the pricing problem in a setting where a data collector sequentially buys data from multiple data owners whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to owners. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data owners. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff which is close to that he can get by setting a fixed price, which is the best in hindsight, for all data owners.

LTE-Unlicensed has recently captured intense attention from both academic and industrial fields. By integrating the unlicensed spectrum with the licensed spectrum, using carrier aggregation, LTE-Unlicensed users can experience enhanced transmission while maintaining the seamless mobility management and predictable performance. However, due to different transmission regulations, the coordination between LTE and Wi-Fi systems requires careful design. It is especially important to understand how to guarantee the transmission quality for LTE users and reduce Wi-Fi users' performance degradation, under the impact of the co-channel interference. In other words, how can we solve the unlicensed resource allocation problem under both LTE and Wi-Fi transmission requirements? In this paper, we propose a matching theory framework to tackle this problem. Specifically, the coexistence between LTE and Wi-Fi systems, i.e., the interaction between LTE and Wi-Fi users, is modeled as a stable marriage game. The coexistence constraints are interpreted as the preference lists. Two semi-distributed solutions, namely, the Gale-Shapley and the random path to stability algorithms are proposed. In addition, to address the external effect in matching, the inter-channel cooperation algorithm is introduced. Last but not least, the resource allocation problem is studied with network dynamics and the proposed mechanisms are evaluated under two typical user mobility models.

Next-generation wireless networks are expected to support an exponentially increasing number of users and demands of data, which all rely on the essential media: spectrum. Spectrum sharing among heterogeneous networks is a fundamental issue that determines network performance. Previous works have focused on a “dynamic spectrum access” mode associated with the cognitive radio technology. These works all focused on the discovery of available spectrum resource either in the time domain or in the frequency domain, i.e., by separating different users' transmission. To initiate a new paradigm of spectrum sharing, the unique characteristics of the technologies should be utilized in the next-generation networks. The 5G networks are featured either by wide bandwidth like mm-wave systems, or by large-scale antennas like massive MIMO. Those two trends can lead to a common phenomenon: the spatial focusing effect. Based on this focusing effect, we propose a general spatial spectrum sharing framework that enables concurrent multi-users spectrum sharing without the requirement of orthogonal resource allocation. Moreover, we design two general network association protocols either in a centralized manner or in a distributed manner. Simulation results show that both the time reversal wideband and the massive MIMO system can achieve high throughput performance with the spatial spectrum sharing scheme.

Driven by the demand for supporting the rapidly increasing wireless traffic, the next-generation communication system, i.e., the 5G system, needs to accommodate a massive number of users and judiciously manage the interference. One promising candidate, the time reversal (TR) system, uses a large bandwidth and designs transmitting waveforms, such that the environment acts as a matched filter and the transmitted signal adds up coherently at the intended users. Therefore, the energy is focused only at the intended users with reduced interference to others. The other candidate, massive MIMO system, utilizes a large number of antennas to focus on the energy to the users and reduce the mutual interference. However, the massive number of users poses a limit on system performance due to increasing interuser interference, and the system has to make a judicial selection of transmitting users. In this paper, we propose a scheduler that maximizes the system weighted sum rate while satisfying the minimum rate requirements of the transmitting users. The optimization problem is transformed into a mixed integer quadratically constrained quadratic programming with linear time complexity. We also investigate the impact of imperfect channel information on the proposed scheduler algorithm and reveal similar channel estimation error distribution between the TR and massive MIMO system. We evaluate the performance of the proposed scheduler in different scenarios and the results show that the proposed scheduler has several desirable characteristics, including low time complexity, suitable on versatile system structure, and robustness against imperfect channel information.

Wireless network virtualization has emerged as a promising technology to provide a variety of services and applications for future wireless network as by enabling a more effective exploitation of network resources. In a mobile virtual network (MVN), both infrastructure provider (InP) and service provider (SP) must have a complementary relationship, as their revenues are mutually dependent. The trading of resources and services between the InP and SP is usually a long-term supply contract, and details of trades are left to be specified in the future. Thus, the returns of the InP and SP depend on their bargaining positions, ex post, and investments, ex ante. As a result, the InP and SP may hesitate to have specific investment, since it may put them at a risk of no return. In this paper, the problems of determining how the ownership of the resources affect the InP and SP's incentives to invest and how to choose the most efficient investments in an MVN are studied. First, a general system model is developed in multiple InPs and SPs engaged in a complementary relationship to exchange multiple physical and virtual resources. Subsequently, for this formulated problem, the optimal investments are derived. Furthermore, we give detailed analysis of a special case and shed light on the problem of ownership and investment efficiency by answering the question on whether the ownership of resources should be integrated or operated separately by the SP and InP. Simulation results assess the parameters that affect the efficiency of investment through simulations.

In this paper, we considers the resource allocation problems for video transmission in space based information networks. The queueing system analyzed in this work is composed of multiple users and a single server. To minimize both of the time average cost and delay of the system, and subject to the constraint that the queues in the system must be stable, we introduce a predictive backpressure algorithm into the consideration of resource allocation to make decision on which packets to be served first. Meanwhile, a multi-resolution wavelet decomposition based backpropagation neural network for the prediction of video traffic is designed in this paper. Performances of the proposed video traffic prediction system and resource allocation scheme are analyzed in the simulations. Results indicate that the prediction accuracy for the video traffic is improved according to the proposed prediction system, and the delay of the queueing system can be reduced through this prediction based resource allocation.

In this paper, the notion of the cumulative time varying graph (C-TVG) is proposed to model the high dynamics and relationships between ordered static graph sequences for space-based information networks (SBINs). In order to improve the performance of management and control of the SBIN, the complexity and social properties of the SBIN's high dynamic topology during a period of time is investigated based on the proposed C-TVG. Moreover, a cumulative topology generation algorithm is designed to establish the topology evolution of the SBIN, which supports the C-TVG based complexity analysis and reduces network congestions and collisions resulting from traditional link establishment mechanisms between satellites. Simulations test the social properties of the SBIN cumulative topology generated through the proposed C-TVG algorithm. Results indicate that through the C-TVG based analysis, more complexity properties of the SBIN can be revealed than the topology analysis without time cumulation. In addition, the application of attack on the SBIN is simulated, and results indicate the validity and effectiveness of the proposed C-TVG and C-TVG based complexity analysis for the SBIN.

In a cooperative network, users share information with each other to achieve a common target. Due to the concerns of privacy and cost, users may be reluctant to share genuine information with each other, which incurs the information trustworthiness problem. Most of the existing research attempts have proposed various mechanisms targeting to enhance the information credibility in various scenarios. However, the users' information sharing inclinations have not been well considered and modeled, which closely determine the information credibility in the network. In this paper, we study a trustworthy situation in cooperative networks and utilize the concept of reputation to model users' behaviors. Specifically, we propose two reputation learning methods based on both the peer-to-peer Bayesian learning and the social non-Bayesian learning models, and also propose a trustworthiness learning method based on the posterior estimation. Finally, simulations are conducted to verify the correctness and effectiveness of our theoretical analysis.

The last decade has witnessed a booming era of wireless vehicular networks, supporting diverse road traffic services and applications. Information dissemination/sharing among vehicles is the fundamental goal of vehicular networks. Although diverse information dissemination/sharing mechanisms have been proposed in the existing literature, the physical layer outage performance of information sharing has not been analyzed. Against this background, in this paper, we study the outage probability of road traffic information sharing in underlay cognitive vehicular networks under both a general scenario and a specific highway scenario. The general scenario relies on the Nakagami-m channel, while the highway scenario is its special case associated with the Rayleigh fading channel. Moreover, we also invoke a real-world dataset containing the locations of Beijing taxis to conduct simulations, the results of which verify the accuracy of our theoretical analysis.

How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.

Traffic density estimation with vehicular ad hoc networks (VANETs) can facilitate many applications. Traditional density estimation is achieved by counting the number of vehicles occupied in a certain area with inductive loop detectors and cameras, which is applied over limited coverage and brings high cost. In this paper, we propose to fuse vehicle spacing information and and compute average spacing in a specific area during a short period of time for density estimation. The robustness of the proposed scheme against Byzantine attack is then analyzed. Finally, we carry our experiments with U.S. Highway 101 data and the results show that the average spacing estimation is consistent with the real value.

The recently announced Super Wi-Fi Network proposal in United States is aiming to enable Internet access in a nation-wide area. As traditional cable-connected power supply system becomes impractical or costly for a wide range wireless network, new infrastructure deployment for Super Wi-Fi is required. The fast developing Energy Harvesting (EH) techniques receive global attentions for their potential of solving the above power supply problem. It is a critical issue, from the user's perspective, how to make efficient network selection and access strategies. Unlike traditional wireless networks, the battery charge state and tendency in EH based networks have to be taken into account when making network selection and access, which has not been well investigated. In this paper, we propose a practical and efficient framework for multiple base stations access strategy in an EH powered Super Wi-Fi network. We consider the access strategy from the user's perspective, who exploits downlink transmission opportunities from one base station. To formulate the problem, we used Partially Observable Markov Decision Process (POMDP) to model users' observations on the base stations' battery situation and decisions on the base station selection and access. Simulation results show that our methods are efficacious and significantly outperform the traditional widely used CSMA method.

In a cooperative network the user equipment (UE) share information with each other for cooperatively achieving a common goal. While owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Hence diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is a paucity of information on the UEs' information sharing inclination, even though this directly affects the information credibility across the network. In this paper, we propose a general framework for the information credibility modelling of cooperative networks by taking into account the users' information sharing inclinations. Specifically, the utility functions of sharing both genuine and false information are defined. Based on this utility formulation and its closed-form analysis, the spontaneous information credibility equilibrium is derived. Our simulation results verify the accuracy of our theoretical analysis.

Self-managed smart devices with growing intelligence can optimize their own performances but pose potential negative impacts on others. The concept Cyber Physical Social System (CPSS), originated from Cyber Physical System (CPS), takes the social dimension into considerations and provides a new paradigm for social interactions between self-managed smart devices. In this paper, we focus on modeling the social interaction among smart devices, and conduct a case study in the autonomous WiFi scenario. Specifically, we propose an active interference measurement methodology reflecting both in-range interference and hidden terminal interference, and a coordinated power control based on the Nash bargaining is further formulated for interference reductions. A four-AP SWP testbed is implemented and deployed in a real office environment, where the social power control between VAs can bring great interference reductions for the APs with hidden terminal interference than the non-cooperative power control scenario.

Super Wi-Fi is expected to enable the Internet access everywhere in a country. Considering the infrastructure deployment issues, energy harvesting technology is a promising solution for power supply. Most of existing works focused on the energy scheduling from the network operator's point of view. In this paper, we study the access point selection strategy from users' perspectives and consider Super WiFi networks powered by solar energy harvesting. Although the network selection problem has been studied, the energy harvesting scenario has not been well investigated and the influence of battery condition has not been taken into account. In our work, we consider the utility of the access users is affected not only by the total number of accessed users, but also the battery condition. In order to formulate the battery states and user states, we incorporate the physical characteristics of solar cell, as well as the dynamic access behaviors of users through Markov Decision Process (MDP)model. By using the value iteration method, the set of optimal network selection strategies is obtained. Simulation results validate that our method has a remarkable performance improvement of utility over the myopic and random access strategies.

Wireless network's traffic capacity is one of the most important measurements of network performance. In a vulnerable network environment, attacks for network components can cause degradation of traffic capacity and lead to increasing network congestion and overall performance degradation. In this paper, we propose an effective protection resources allocation scheme which allocates limited protection resources of nodes based on assessment of vulnerability of network components. This scheme proves to be the best allocation strategy in respect to enhancing the quantity of network traffic capacity. The simulation results show that the proposed allocation scheme outperforms other traditional allocation schemes based on critical node analysis. A thorough analysis of traffic capacity and network vulnerability has also been provided.

Topology control (TC) is one of the most important techniques for connection establishments between entities in Internet of Things, especially in the scenario where there are no fixed infrastructures such as prompt sensor deployments, emergency communications, and vehicle-vehicle communications. The goal of TC is to assign per-node transmission power such that the resulting topology is energy efficient and also satisfies some global graph property such as connectivity. Among the literatures, however, few efforts focused on the issue of TC with interest-independent nodes. Besides, in a distributed mobile networks, nodes' mobility can cause the dynamic network topology. As a result, nodes need to execute the TC algorithm periodically. To jointly consider nodes' selfishness and mobility, we propose a potential game and mobility aware TC (PGMATC) framework based on mobility traces and potential game, where a practical utility function is designed by considering the expected utility for a TC period. Extensive simulations driven by actual data demonstrate that PGMATC can improve the network connectivity with quite a limited increase in energy consumption, compared with the traditional TC algorithms.

The super-WiFi network concept has been proposed for nationwide Internet access in the United States. However, the traditional mains power supply is not necessarily ubiquitous in this large-scale wireless network. Furthermore, the non-uniform geographic distribution of both the based-stations and the tele-traffic requires carefully considered user association. Relying on the rapidly developing energy harvesting techniques, we focus our attention on the sophisticated access point (AP) selection strategies conceived for the energy harvesting aided super-WiFi network. Explicitly, we propose a solar radiation model relying on the historical solar activity observation data provided by the University of Queensland, followed by a beneficial radiation parameter estimation method. Furthermore, we formulate both a Markov decision process (MDP) as well as a partially observable MDP (POMDP) for supporting the users' decisions on beneficially selecting APs. Moreover, we conceive iterative algorithms for implementing our MDP and POMDP-based AP-selection, respectively. Finally, our performance results are benchmarked against a range of traditional decision-making algorithms.

Collaborative spectrum sensing is an effective method to improve detection rates in cognitive radio networks. However, it is vulnerable to spectrum sensing data falsification (SSDF) attacks when malicious secondary users (SUs) report fraudulent sensing data. In order to improve the robustness, numerous attack prevention schemes have been proposed to identify malicious SUs. Nevertheless, most of them neglect to incentivize SUs to send truthful reports. An incentive method based on peer-prediction is proposed to identify malicious suspects, punish attackers, and incentivize SUs to send truthful reports simultaneously for decision fusion. Moreover, continuous peer-prediction derived from the binary case is introduced, which is capable of preventing attacks in the continuous domain. Theoretical analysis and simulation results demonstrate that honest SUs are rewarded for accurate and truthful sensing results, while malicious SUs incur penalty for making falsified sensing reports. A significant improvement of detection rates is obtained by the proposed scheme when there are no more than half of malicious SUs conducting SSDF attacks.

Exploring potentially useful information from huge amount of textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristics of microblog texts, using term frequency vectors to represent microblog texts will cause “sparse data” problem. Finding proper representations of microblog texts is a challenging issue. In this paper, we apply deep networks to map the high-dimensional representations of microblog texts to low-dimensional representations. To improve the result of dimensionality reduction, we take advantage of the semantic similarity derived from two types of microblogspecific information, namely the retweet relationship and hashtags. Two types of approaches, including modifying training data and modifying the training objective of deep networks, are proposed to make use of microblog-specific information. Experiment results show that the deep models perform better than traditional dimensionality reduction methods such as latent semantic analysis and latent Dirichlet allocation topic model, and the use of microblog-specific information can help to learn better representations.

This paper considers the resource allocation problems for video transmission in space-based information networks. The queueing system analyzed in this study is constituted by multiple users and a single server. The server is operated as a cloud that can sense the traffic arrivals to each user's queue and then allocates the transmission resource and service rate for users. The objectives are to make configurations over time to minimize the time average cost of the system, and to minimize the waiting time of packets after they enter the queue. Meanwhile, the constraints on the queue stability of the system must be satisfied. In this paper, we introduce a predictive backpressure algorithm, which considers the future arrivals with a certain prediction window size into the consideration of resource allocation to make decisions on which packets to be served first. In addition, this paper designs a multiresolution wavelet decomposition-based backpropagation network for the prediction of video traffic, which exhibits the long-range dependence property. Simulation results indicate that the delay of the queueing system can be reduced through this prediction-based resource allocation, and the prediction accuracy for the video traffic is improved according to the proposed prediction system.

Cellular network is widely used and device-to-device (D2D)-assisted approaches have been proposed for improving performance on spectrum efficiency, overall throughput and energy efficiency. However, few of them have considered the downlink transmission for multiple concurrent devices from an energy efficiency perspective. In this paper, we focus on a D2D-assisted cellular communication in video stream sharing scenario. Two energy saving solutions for downlink transmission are proposed with constraint on D2D cluster's energy consumption. We take peak signal-to-noise ratio (PSNR) as the measurement for video quality and consider both the downlink transmission energy and reception energy. In particular, we propose the D2D cluster formation approach and the D2D caching performance both for the purpose of energy saving, with distributed merge-and-split algorithm adopted from the perspective of coalition game theory and a relaxation factor defined to give constraints on total energy consumption for each cluster. Both D2D cluster and D2D caching approaches are effective for energy saving for the BS combined with all user devices; however, D2D cluster brings an unfairness problem between the cluster head and other cluster nodes. Therefore, we compare the two approaches on energy saving performance as well as fairness measurement. Moreover, a centralized algorithm for D2D cluster is also proposed as a benchmark for the distributed D2D cluster algorithm. Simulation result shows considerable amount of energy saving in the proposed D2D cluster and caching assisted cellular network for video stream sharing problem.

The energy consumption of a base station (BS) has attracted much attention in the study of wireless communication. Device-to-device communication, which can be utilized to offload the traffic from the BS, provides an effective way to increase network energy efficiency. How to optimally coordinate users to redistribute the traffic so as to minimize the energy consumption is an important issue. In this paper, we study two problems that are critical to this issue. First, considering that relaying data to others incurs costs to the users and different users have different costs, we propose a contract theoretical approach to design the mechanism for pricing the contributions of users. The second problem is to make a proper matching between users who demand data and users who are willing to relay data. Matching theory is exploited to deal with this problem. Specifically, we consider both interference-free and interference scenarios and develop matching algorithms, which can achieve stable matching and weak stable matching, respectively. Simulation results demonstrate the effectiveness of the proposed algorithms.

The explosion in demand for wireless data services in recent years has triggered pervasive deployment of wireless networks. How to associate to one of the wireless networks in the best interest of a user is an essential problem to mobile computing. In this paper, we analyze a data set of wireless LAN traces collected from campus networks, from which we observe that the user arrival distribution is approximately Poisson distributed; the session time and the waiting time to switch network can be approximated by exponential distributions. Based on the data analysis, we formulate a wireless access network association game as a multidimensional Markov decision process with negative network externality, where the best response strategy is an approximate Nash equilibrium. A modified value iteration algorithm is proposed to search the best response strategy profile. Applying the proposed algorithm to the data-driven stochastic model, the best response strategy is shown to achieve a better individual expected utility while satisfying the individual rationality, and attain a near-optimal social welfare performance compared to other strategies such as the centralized method and the greedy algorithm.

In space-based networks, the data relay satellites can assist low-earth-orbit satellites in relaying data to other satellites or the ground station and improve the real time system throughput. To take full advantage of transmission resource of the cooperative relays, this paper proposes a multiple access and resource allocation strategy, in which relays can receive and transmit simultaneously according to channel characteristics of space-based systems. Based on the queueing theoretic formulation, the stability of the proposed protocol is analyzed and the maximum stable throughput region is derived, which would provide the appropriate guidance for the design of the system optimal control. Simulation results exhibit multiple factors that affect the stable throughput and verify the theoretical analysis.

Collaborative spectrum sensing is an effective method to improve the detection rate in cognitive radio. However, it is vulnerable to spectrum sensing data falsification attacks. In order to improve the robustness, numerous attack prevention schemes have been proposed to identify malicious secondary users (SUs). Nevertheless, most of them neglect to incentivize SUs to send truthful reports. Therefore, an incentive method based on Private-Prior Peer-Prediction with approximate subjective priors is proposed to identify malicious suspects and punish attackers when falsifying the sensing data simultaneously. The theoretical analysis and simulation results demonstrate that honest SUs are rewarded by accurate and truthful sensing results while malicious SUs receive heavy loss for making falsified sensing results. Moreover, a significant improvement of detection rates is demonstrated when there are a large number of malicious SUs conducting cooperative attacks compared to the pure majority rule scheme.

With the emergence of large scale data processing systems and big data analysis, cloud computing has become more and more popular. In this paper, we focus on the mechanism design in the infrastructure as a service (IaaS) cloud computing service market. Most of existing works on mechanism design assume static and independent individual utility, while in practice the cloud service is provided in a dynamic environment. To solve such problems, we propose a stochastic matching algorithm based on Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency by considering the opportunity cost in the future. Based on the MDP formulation, we further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism. Finally, we conduct experiment using Google cluster-usage traces dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.

In small cell networks, due to the small coverage of small cell access points (SAPs), handoffs may be executed frequently. Therefore, evaluating the utility that a user equipment (UE) can acquire from an SAP is of great significance. In this paper, different from traditional evaluation schemes, we propose a social interactive evaluation scheme. The UEs are allowed to share their local believes and fuse them in a non-Bayesian manner. One advantage of the scheme is that it allows UEs to evaluate an SAP that they do not connect to, based on which UEs can get prepared for handoff in advance. Both the theoretical analysis and simulation illustrate that UEs not connecting to an SAP is able to learn the real utility iteratively and accurately. Additionally, compared to UEs performing individual Bayesian estimation, UEs with the non-Bayesian scheme can learn the real utility faster if the signal cannot be observed in every iteration.

Residential wireless networks have grown rapidly in the past decade. Meanwhile, dense deployments and autonomous managements of home Access Points (APs) greatly increase channel congestion levels and degrade the user experience. To address such problems, this paper proposes an architecture in residential environments called Wi-Fi Union (WU), where home APs can voluntarily join WU and become "member APs". WU helps member APs decrease their congestion levels by assigning channels in a coordinated manner with incentive considerations. First, we propose congestion level metric normalized airtime which can be passively and independently measured by member APs. Normalized airtime is further used to classify APs into heavily congested APs and lightly congested APs. A tabu search based channel assignment algorithm is presented which can decrease the congestion level of heavily congested member APs, and guarantee lightly congested member APs to be still lightly congested. Extensive NS-3 simulations driven by actual Wi-Fi data show that the united channel assignments has a 1.5 times throughput than that in the default setting on average.

The issue of data pricing is becoming more important than before in order to build an internet ecosystem. In this paper, we consider a data redistribution market where users with extra data quota are able to sell data to users that have used up their data quota. We consider this market problem with multiple users on both sides, and analyze on two possible market environment to achieve market equilibrium: the exogenous pricing scenario and endogenous pricing scenario. The stable matching algorithm and message-passing algorithm are used respectively. Simulation results show that while exogenous pricing market achieves equilibrium at a certain market price, the endogenous pricing market achieves equilibrium for each pair with better overall performance although without stability.

Privacy issues arising in the process of collecting, publishing and mining individuals' personal data have attracted much attention in recent years. In this paper, we consider a scenario where a data collector collects data from data providers and then publish the data to a data user. To protect data providers' privacy, the data collector performs anonymization on the data. Anonymization usually causes a decline of data utility on which the data user's profit depends, meanwhile, data providers' would provide more data if anonymity is strongly guaranteed. How to make a trade-off between privacy protection and data utility is an important question for data collector. In this paper we model the interactions among data providers/collector/user as a game, and propose a general approach to find the Nash equilibriums of the game. To elaborate the analysis, we also present a specific game formulation which takes k-anonymity as the anonymization method. Simulation results show that the game theoretical analysis can help the data collector to deal with the privacy-utility trade-off.

The Chinese Spring Festival travel season (Chunyun) has been called the largest annual human migration in the world with approximately 3.6 billion trips in 2014. Understandably, all forms of transportation are stressed at or near saturation during this period and many people, especially lower-income individuals fail to get to their destinations. We present CountryRoads, a ridesharing system to address the transportation shortage during Chunyun. The CountryRoads system collects users' route information, and matches drivers and passengers as an online bipartite matching problem based on the proximity of the passenger's origin and destination and the route of the driver. The system is evaluated during four Chunyun periods from 2012 to 2015 with up to 17272 users and forms 4777 ridesharing tuples in 2015.

How to enhance the network capacity is one of the most important issues. To achieve this, the existing works have focused on improving either the network structure or routing strategies with a common assumption of uniformly distributing the replicas of information among the nodes in the network. The nodes associated with information replicas are considered as source nodes (or server). However, for many networks such as the Internet, some nodes have much more traffics than the others, exhibiting an asymmetric phenomenon. In this letter, we study the optimal source selection strategy to enhance the network capacity, where an optimization model is proposed to find the optimal source selection probability distribution. Simulation results show that in homogeneous networks, most of the nodes can be the sources. While in heterogeneous networks such as the scale-free networks, only a small number of the nodes can be the sources. Moreover, an interesting phenomenon is observed that the optimal proportion of source nodes in Erdös-Rényi random network and Barabási-Albert scale-free network exhibits a power law relationship with the network size.

It is well known that avalanche effect errors in received noisy ciphertexts will cause severe error propagation in block-ciphered encryption systems, thus resulting in a large reduction in the achievable throughput. However, little is known about the statistical properties of the underlying error sequences in decrypted plaintexts in block-ciphered systems when channel errors are present. A rigorous study of the statistical properties of the errors in block-ciphered crypto-systems operating in cipher block chaining (CBC) mode is provided. The equivalent channel transition probability is obtained and then used to derive error statistics including both error weight probabilities and gap distributions. The validity of the theoretical analyses is confirmed by the excellent match with results obtained by simulated data encryption standard (DES)-based and advanced encryption standard (AES)-based crypto-systems operating in CBC mode. The error statistics will be valuable in the design and performance evaluation of communication protocols, as well as in error-control schemes for block-ciphered crypto-systems in the presence of erroneous ciphertexts, where errors are intentionally left to enhance security against passive eavesdroppers.

Current researches on topology control with cooperative communication (CC) in wireless ad hoc networks have focused on network connectivity, path energy-efficiency and node transmission power reduction. However, fault-tolerance related issues have not been adequately addressed. In this paper, we propose a CC-based scheme to achieve more efficient fault-tolerant topology control. We first definek-connectivity under the CC model and then design a distributed scheme for building at-spanner withk-connectivity of an arbitrary communication network. Simulation results confirm that the proposed scheme can tolerate node failures as well as exploit the advantage of CC to achieve path energy-efficiency and lower power consumption of the network.

In a dynamic system, how to perform learning and make decisions are becoming more and more important for users. Although there are some works in social learning-related literature regarding how to construct belief for an uncertain system state, few studies have been conducted on incorporating social learning with decision making. Moreover, users may have multiple concurrent options on different objects/resources and their decisions usually negatively influence each other's utility, which makes the problem even more challenging. In this paper, we propose an Indian Buffet Game to study how users in a dynamic system learn about the uncertain system state and make multiple concurrent decisions by not only considering the current myopic utility, but also the influence of subsequent users' decisions. We analyze the proposed Indian Buffet Game under two different scenarios: 1) on customers requesting multiple dishes without budget constraint and 2) with budget constraint. For both cases, we design recursive best response algorithms to find the subgame perfect Nash equilibrium (NE) for customers and characterize special properties of the NE profile under homogeneous setting. Moreover, we introduce a non-Bayesian social learning algorithm for customers to learn the system state, and theoretically prove its convergence. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms.

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.

In heterogenous networks, intercell interference coordination (ICIC) is a big challenge. This paper presents an analytical framework to evaluate power control and time mute schemes in closed access femto and macro coexistent networks. We use stochastic geometry to model the downlink scenario and derive the coverage probability of indoor macro users and femto users. The optimal operating parameters for altruistic power control and time mute schemes are achieved. Considering the selfishness of the owners of femtos, we formulate the two-tier interference coordination as pricing games and obtain the closed-form of Nash equilibria. Simulation results demonstrate the influence of different parameters on the performance of ICIC schemes and show that when target SINR ≥ 3 dB, the time mute scheme outperforms the power control scheme in handling the indoor macro user coverage problem.

Nowadays, social networks are extremely large-scale with tremendous information flows, where understanding how the information diffuse over social networks becomes an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. To verify our theoretical analysis, we conduct experiments by using Facebook network and real-world information spreading dataset of Memetracker. Experiment results show that the proposed game theoretic framework is effective and practical in modeling the social network users' information forwarding behaviors.

Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of it have been studied, e.g., spectrum sharing, interference mitigation, etc, the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in cognitive femtocell network, and propose a two-tier pricing game theoretic framework with a dynamic pricing model. We first model the cognitive users' network access behavior as a 2-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm can quickly converge to the Nash equilibrium prices.

Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, in this paper, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and Lambert-W function. Moreover, we theoretically prove the existence, uniqueness, and fairness of the solution to this game model. Accordingly, a cooperative Nash bargaining resource allocation algorithm is developed, which is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithms for efficient and fair resource allocation in cognitive small cell networks.

Recently, cognitive radio enabled femtocell is regarded as a promising technique in wireless communications, where the issues of resource allocation and interference management have been investigated intensively. However, spectrum sensing errors are neglected in most of the existing works. In this paper, we propose a resource allocation scheme for orthogonal frequency division multiple access (OFDMA) based cognitive femtocells. The target is to maximize the sum rate of all femtocell users (FUs) under QoS constraints and co-tier/cross-tier interference constraints under imperfect channel sensing. The subchannel and power allocation problem is first modeled as a mixed integer programming problem, and then transformed into a convex optimization problem by relaxing subchannel sharing and imposing co-tier interference constraints, which is finally solved using the dual decomposition method. Based on the obtained solution, an iterative subchannel and power allocation algorithm is proposed. The effectiveness in terms of instantaneous maximum achievable rate of the proposed algorithm as compared with perfect spectrum sensing schemes is verified by simulations.

Information diffusion over social networks becomes a hot topic recently. Most of the existing works are based on the machine learning method with social network structure analysis and empirical data mining. However, the results learned from some specific dataset may not apply to the future networks, since the social network structure is in a highly dynamic environment. Moreover, the dynamics of information diffusion are also heavily influenced by network users' decisions, actions and their socio-economic interactions, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks, which focuses on the users' behavior analysis from a microeconomics points of view. We also conduct experiments by using real-world Twitter information diffusion dataset, which shows that the proposed evolutionary game theoretic model is effective and practical in modeling the social network users' information diffusion dynamics.

As the nanotechnology becomes more and more mature, the concept of nano communication emerged and attracted lots of researchers' attention. To implement the nano communication system, the diffusion-based molecular communication is considered as a promising bio-inspired approach, where the nano transmitter emits molecules into the medium to transmit data and the nano receiver absorbs molecules from the medium to receive data. Since all the nano machines share the same propagation medium and the molecules are identical, the interference among nano transmitters are unavoidable. In this paper, we analyze the inter-symbol and inter-user interference in the diffusion-based molecular communication systems. Based on the interference analysis, we further study the bit error rate performance and derive the optimal decision threshold for the nano receiver. Simulation results are shown to verify our analysis.

The limited sensor node energy and the large number of nodes with dynamic network topology information have always been the important design concerns in Wireless Sensor Networks (WSN). Node clustering is an effective way to tackle with the two issues by grouping the nodes into hierarchies in order to reduce communication distance and the amount of message. This paper mainly focuses on the unification of the node energy consumption in WSN. The distributions of the energy consumption for various scenarios in the hierarchical network are analyzed for the first time and two main reasons are found leading to the asymmetry of the energy consumption among nodes. One is the energy consumption from the communications between nodes and base station, and the other is that from the cluster head for receiving data from other nodes. It is concluded that the probability of the node acting as cluster head should depend on the distribution of the head's energy consumption, and a variable sampling space oriented to the potential number of cluster heads is established thereafter. Furthermore, a new clustering algorithm, the Segment Equalization Clustering based on Cluster Head Energy Consumption (SECHEC) algorithm is proposed, which can effectively improve the network lifetime and ensure the availability of the system within its entire lifespan.

In this paper, we present an overview of the time-reversal (TR) wireless paradigm for green Internet of Things (IoT). It is shown that the TR technique is a promising technique that focuses signal waves in both time and space domains. The unique asymmetric architecture significantly reduces the cost of the terminal devices, the total number of which is expected to be very large for IoT. The focusing effect of the TR technique can harvest the energy of all the multi-paths at the receiver, which improves the energy efficiency of the wireless transmission and thus the battery life of terminal devices in IoT. Facilitated by the high-resolution spatial focusing, the TR division multiple access scheme leverages the uniqueness of the multi-path profiles in the rich-scattering environment and maps them into location-specific signatures, so that spatial multiplexing can be achieved for multiple users operating on the same spectrum. In addition, the TR system can easily support heterogeneous terminal devices by providing various quality-of-service (QoS) options through adjusting the waveform and rate backoff factor. Finally, the unique location-specific signature in TR system can provide additional physical-layer security and thus can enhance the privacy and security of customers in IoT. All the advantages show that the TR technique is a promising paradigm for IoT.

To meet the dramatically increasing demands for vehicular communications, cognitive vehicular networks have been proposed to broaden the vehicular communication bandwidth by using cognitive radio technology. Meanwhile, the nationwide Super Wi-Fi project that allows the TV white space frequencies to be used for free, makes the concept of cognitive vehicular networks realistic. Recently, lots of technical issues of cognitive vehicular networks have been studied from the network designers' perspective, e.g., vehicular spectrum sensing and access, applications with different vehicular QoS, etc. Different from the existing works, in this paper, we consider from the vehicular users' perspective by optimizing throughput via route selection in cognitive vehicular networks using TV white space. By employing the attainable data rate as route selection metric, we propose two schemes: instantaneous route selection and long-term route selection. To evaluate the expected data rate on the route, we analyze the cognitive vehicular network throughput under two spectrum sharing models: spectrum overlay and spectrum underlay. In the experiments, we use Google spectrum dataset to estimate the intensity of TV base stations in the United States and evaluate the cognitive vehicular network throughput performance, which shows that the spectrum overlay model is more suitable for most of states in current United States, except New Jersey, Delaware and Utah. Moreover, we conduct a case study regarding the route I-88E and I-90E selection between Cortland and Schenectady in New York State. The traffic intensities and traffic intensity transition probabilities of these two routes are estimated using the real-world traffic volume dataset of New York State. Based on the estimated traffic information, we calculate the attainable instantaneous and long-term data rates of each vehicular user, which shows that route I-88E is preferable to route I-90E in most cases.

Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.

In a distributed cognitive radio network, due to negative network externality, rational secondary users tend to avoid accessing the same vacant primary channels with others. Moreover, they usually need to make their channel access decisions in a sequential manner to avoid collisions. The characteristic of negative network externality and the structure of sequential decision making make the multi-channel sensing and access problem challenging, which has not been well studied by the existing literatures. To solve these problems, in this paper, we propose a multi-channel sensing and access game, which not only considers the negative network externality in secondary users' decision making, but also takes into account their sequential decision making structure. We solve the multi-channel sensing problem using Bayesian learning method and design a cooperative learning rule for secondary users to accurately estimate the channel state. We study the multi-channel access problem under two scenarios: with and without resource constraint, respectively. For both scenarios, we design recursive best response algorithms for secondary users to find the subgame perfect Nash equilibria. Specifically, we analyze the homogenous case of the scenario without resource constraint and find that the Nash equilibrium profile exhibits a threshold structure. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed methods.

Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of cognitive femtocell network have been studied, e.g., spectrum sharing, interference mitigation, etc., the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in the cognitive femtocell network and propose a two-tier pricing game theoretic framework with two models: static and dynamic pricing models. In the static pricing model, we derive the closed-form expressions for pricing and demand functions, as well as the Nash equilibrium pricing strategies for both macrocell and femtocell operators. In the dynamic pricing model, we first model the cognitive users' network access behavior as a two-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm in the dynamic pricing model can quickly converge to the Nash equilibrium prices.

Users in a social network are usually confronted with decision making under uncertain network state. While there are some works in the social learning literature on how to construct belief on an uncertain network state, few study has been made on integrating learning with decision making for the scenario where users are uncertain about the network state and their decisions influence with each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this paper, we propose a Dynamic Chinese Restaurant Game to study how a user in a dynamic social network learns the uncertain network state and make optimal decision by taking into account not only the immediate utility but also subsequent users' negative influence. We introduce a Bayesian learning based method for users to learn the network state, and propose a Multi-dimensional Markov Decision Process based approach for users to achieve the optimal decisions. Finally, we apply the Dynamic Chinese Restaurant Game to cognitive radio networks and demonstrate from simulations to verify the effectiveness and efficiency of the proposed scheme.

Many spectrum sensing and access algorithms have been proposed to improve secondary users' (SUs') opportunities of utilizing primary spectrums. However, most of them have separated the analysis of spectrum sensing and access. In this paper, we propose to integrate the design of spectrum sensing and access algorithms by taking into account the mutual influence of them. Due to selfish natures, SUs tend to access the primary channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among SUs, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for SUs to converge to the ESS. With the proposed algorithm, each SU senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of selfish SUs.

In order to mitigate the problem of crowded radio spectrum, dynamic spectrum access (DSA) has become the solution and a research focal point. The number of works in wideband spectrum sensing is dramatically increasing recently due to its importance in cognitive radio (CR) networks. Most works assume that secondary users (SUs) are synchronous with the primary users' (PUs) network. However, in many practical systems the SUs will have no information about the PUs' communication protocols. Furthermore, it is also possible that communications among PUs are not based on synchronous operation. In order to address such problems, a contention-based wideband DSA algorithm with asynchronous cooperative spectrum sensing (CSS), called the DySac, is proposed. In this scheme, the SUs need not maintain synchronism with the PUs, and the SUs who intend to access the primary network compete among each other for available channels. Performance analysis and simulations are conducted to evaluate the proposed DySac.

Cooperative spectrum sensing is a promising technology for spectrum sensing with superior performance. Previous work has assumed that secondary users (SUs) are synchronous with the primary users' (PUs) network. However, on one hand, the SUs may have no information about the PUs' communication protocols. On the other hand, in some cases communications among PUs are not based on synchronous operation. In order to address such problems, an asynchronous cooperative sensing scheme is proposed. In this scheme, the SUs need not synchronize with the PUs when dynamically sensing and accessing primary channels. The channel-usage pattern is modeled as an ON-OFF process and the SUs' average throughput of each asynchronous case is analyzed based on the detection and false alarm probability. The optimal sensing period for the sensing scheme is achieved by maximizing the SUs' average throughput. Simulation results evaluate the performance of the scheme.

In a cognitive radio network with mobility, secondary users can arrive at and leave the primary users' licensed networks at any time. After arrival, secondary users are confronted with channel access under the uncertain primary channel state. On one hand, they have to estimate the channel state, i.e., the primary users' activities, through performing spectrum sensing and learning from other secondary users' sensing results. On the other hand, they need to predict subsequent secondary users' access decisions to avoid competition when accessing the ”spectrum hole”. In this paper, we propose a Dynamic Chinese Restaurant Game to study such a learning and decision making problem in cognitive radio networks. We introduce a Bayesian learning based method for secondary users to learn the channel state and propose a Multi-dimensional Markov Decision Process based approach for secondary users to make optimal channel access decisions. Finally, we conduct simulations to verify the effectiveness and efficiency of the proposed scheme.

Dynamic spectrum access in cognitive radio networks can greatly improve the spectrum utilization efficiency. Nevertheless, interference may be introduced to the Primary User (PU) when the Secondary Users (SUs) dynamically utilize the PU's licensed channels. If the SUs can be synchronous with the PU's time slots, the interference is mainly due to their imperfect spectrum sensing of the primary channel. However, if the SUs have no knowledge about the PU's exact communication mechanism, additional interference may occur. In this paper, we propose a dynamic spectrum access protocol for the SUs confronting with unknown primary behavior and study the interference caused by their dynamic access. Through analyzing the SUs' dynamic behavior in the primary channel which is modeled as an ON-OFF process, we prove that the SUs' communication behavior is a renewal process. Based on the Renewal Theory, we quantify the interference caused by the SUs and derive the corresponding closed-form expressions. With the interference analysis, we study how to optimize the SUs' performance under the constraints of the PU's communication quality of service (QoS) and the secondary network's stability. Finally, simulation results are shown to verify the effectiveness of our analysis.

Many spectrum sensing methods and dynamic access algorithms have been proposed to improve the secondary users' opportunities of utilizing the primary users' spectrum resources. However, few of them have considered to integrate the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this paper, we propose to jointly analyze the spectrum sensing and access problem by studying two scenarios: synchronous scenario where the primary network is slotted and non-slotted asynchronous scenario. Due to selfish nature, secondary users tend to act selfishly to access the channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among secondary users, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for the secondary users to converge to the ESS. With the proposed algorithm, each secondary user senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.

Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is to study the rational strategy considering the negative network externality, i.e, the influence of subsequent users' decisions on an individual's throughput due to the limited available resources. In this work, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modelled as a multi-dimensional Markov Decision Process (MDP). A modified value iteration algorithm is proposed to efficiently obtain the optimal decision rule with a simple threshold structure, which enables us to reduce the storage space of the strategy profile. We further investigate the mechanism design problem with incentive compatibility constraints, which enforce the networks to reveal the truthful state information. The formulated problem is a mixed integer programming problem which in general lacks an efficient solution. Exploiting the optimality of substructures, we propose a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multi-network scenario, the proposed algorithm can outperform the heuristic greedy approach in a polynomial-time complexity. Finally, simulation results are shown to validate the analysis and demonstrate the effectiveness of the proposed algorithms.

Dynamic spectrum access in cognitive radio networks can greatly improve the spectrum utilization efficiency. Nevertheless, interference may be introduced to the Primary User (PU) when the Secondary Users (SUs) dynamically utilize the licensed channel. If the SUs can be synchronous with the PUs, the interference is mainly due to their imperfect spectrum sensing of the primary channel. However, if the SUs have no knowledge about the PU's communication mechanism, additional interference may occur. In this paper, we propose a renewal theoretical framework to study the situation when SUs confronting with unknown primary behavior. We quantify the interference caused by the SUs and derive the corresponding close-form expressions. With the interference analysis, we study how to optimize the SUs' performance under the constraints of the PU's communication quality of service (QoS). Finally, simulation results are shown to verify the effectiveness of our analysis.

One critical issue in dynamic spectrum access of cognitive radio networks is the analysis of interference caused by Secondary Users (SUs). Most of the current works focus on mitigating the aggregated interference effects of SUs at Primary Users (PUs) in the physical layer. However, the interference is also dynamically related to the communication behaviors between PUs and SUs. In this paper, we analyze the interference caused by SUs in the MAC layer by taking into account the dynamic behaviors between PUs and SUs. Based on the ON-OFF primary channel state model, we derive the close-form expressions for the probability of interference caused by SUs and quantify the interference effect in two scenarios: slotted secondary network and non-slotted secondary network. We also discuss how to control SUs' access behavior such that the normal communication of PUs can be guaranteed. Finally, simulation results are shown to verify the effectiveness of our analysis.

One critical challenge for accurate localization with Received Signal Strength Indicator (RSSI) is the anisotropic environment, which causes the RSS-Distance Relationship (RDR) to vary spatially. To alleviate localization error caused by RDR anisotropy, most of existing works adopt multiple RDR algorithms. However, we have found that the arbitrary RDR selection in these algorithms can lead to large localization error. Moreover, localization accuracy can be further enhanced by utilizing information provided by more Access Points (APs). To address these problems, we propose a Probabilistic Neural Network based localization algorithm in this paper. The algorithm features two steps: Global Optimization and Regional Compensation, during which all APs exchange information about the Blind Node (BN) to locate it collaboratively. Simulation result shows that the proposed algorithm can achieve a localization accuracy 35% higher than that of multiple RDR algorithms.

In this paper, a platform "uSensing" is proposed to support mobile smartphones accessing into wireless sensor networks(WSNs). Since phones have different platforms, e.g. operating systems and CPU processors, it is a challenge to provide a universal platform for smartphones. To solve it, we design novel hardware and software: 1) "uSD": a SD card with zigbee radio; 2) "uSinkWare": a software running on smartphones. The uSD card and uSinkWare consists of uSensing platform. With uSensing, any phones can communicate with WSNs and parse sensor messages. We demonstrate the proposed uSensing in Nokia 5800XM to connect with our WSNs testbed, and evaluate the performance of uSensing through measuring the phone CPU and RAM. The experiment results show that the uSensing can support the smartphones very well to access into the WSNs as the data sink.

Femtocell is a small access point using the wire broadband connections or wireless technologies to access the mobile operator's network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for wireless communication system.Femtocell related handover cost reduction is one of the important targets in LTE-Advanced SON (Self-Organising Networks). In this paper, a handover optimization algorithm based on the UE's mobility state is proposed. An analytical model was presented for the handover signalling cost analysis. Numerical results are provided to compare the signalling cost of different handover management schemes. The comparison between the proposed algorithm and the traditional handover control algorithm shows that the algorithms proposed in this paper have a significant reduction in the signalling overhead.

Home eNodeB (HeNB) is a low-power access point using the local broadband connections or a separate RF backhaul to access the mobile operator's network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for users. Considering the potential frequent mobility between HeNB-HeNB and HeNBeNB, HeNB mobility enhancement is proposed as one of the most important work items in 3GPP LTE-Advanced. In this paper, four X2 interface based HeNB mobility enhanced architectures are explicitly discussed in terms of signalling overhead evaluation. The numerical results show that the direct-X2 based option 2 in HeNB-HeNB scenario and the X2-GW based option 3 in eNB-HeNB scenario have the best trade-off in signalling overhead and complexity respectively.

Dynamic spectrum access has become a focal issue recently. Lots of works have been done concerning secondary users (SU) synchronously accessing primary users' (PU) network. However, on one hand, SU have to periodically synchronize with PU's time tables, which will bring lots of unnecessary overhead. On the other hand, it is possible that SU have no idea about PU's communication scheme at all or even communications among PU are not based on synchronous scheme. In order to address such problems, this paper advances an asynchronous algorithm, called A-DSA, for SU to asynchronously access CR-based Ad-Hoc network. We focus on three questions in this paper: 1) how to choose channels to sense; 2) how to sense the chosen channels; 3) how to determine the final access channel after sense. Three strategies are proposed towards each question. Our simulations show that choosing sense channels by A-DSA can attain 20% more success rate than randomly choosing. Moreover, sensing by A-DSA can not only achieve nearly 50% less interference probability than equal allocation of the overall sense time, but also well adapt to time-varying channels.