Graph pooling has gained significant progress in recent years as an effective solution for graph-level property classification tasks. With the emergence of research on Heterogeneous Information Networks (HINs), this paper argues that graph-level datasets for graph classification should be treated as HINs rather than homogeneous graphs to enhance information aggregation. We propose HINPool, a novel and general graph pooling framework for graph-level property classification with HINs. First, we devise a systematic HIN construction procedure from the original data to capture complex interactions. Next, we introduce a type-aware heterogeneous graph pooling method featuring a Type-Aware Selector (TAS) to select essential nodes and a Readout Aggregator (RA) to fuse critical information into a graph-level representation. Finally, a cross-layer fusion function is applied to combine the output embeddings from each graph pooling layer, creating a final graph representation for downstream classification tasks. Our approach achieves near state-of-the-art performance on widely used graph classification benchmark datasets, demonstrating significant improvements in four out of five datasets. This work redefines the strategy for graph-level property classification with HGNNs and heterogeneous graph pooling to model intricate relationships, enhancing performance without requiring extensive domain-specific knowledge.

Kinship face synthesis is a challenging problem due to the scarcity and low quality of the available kinship data. Existing methods often struggle to generate descendants with both high diversity and fidelity while precisely controlling facial attributes such as age and gender. To address these issues, we propose the Style Latent Diffusion Transformer (StyleDiT), a novel framework that integrates the strengths of StyleGAN with the diffusion model to generate high-quality and diverse kinship faces. In this framework, the rich facial priors of StyleGAN enable fine-grained attribute control, while our conditional diffusion model is used to sample a StyleGAN latent aligned with the kinship relationship of conditioning images by utilizing the advantage of modeling complex kinship relationship distribution. StyleGAN then handles latent decoding for final face generation. Additionally, we introduce the Relational Trait Guidance (RTG) mechanism, enabling independent control of influencing conditions, such as each parent's facial image. RTG also enables a fine-grained adjustment between the diversity and fidelity in synthesized faces. Furthermore, we extend the application to an unexplored domain: predicting a partner's facial images using a child's image and one parent's image within the same framework. Extensive experiments demonstrate that our StyleDiT outperforms existing methods by striking an excellent balance between generating diverse and high-fidelity kinship faces.

Extremely large-scale reconfigurable intelligent surfaces (XL-RISs) have been recognized as a promising technology to enhance the capabilities of communication systems and mitigate severe path loss. However, due to the large size of XL-RISs, simply adopting the deployment strategies of RIS may result in coverage areas with relatively low performance improvement. To investigate the potential contribution of each position on the designated surface, we propose a Shapley value sampling method to evaluate their Shapley values. Based on the approximated Shapley values, we propose a linear-time algorithm to determine the near-optimal XL-RIS deployment in terms of expected total transmission rate. Finally, simulation results show that the proposed Shapley sampling method effectively predicts the contribution of each XL-RIS module in total capacity, and we identify the scenarios where our deployment algorithm outperforms the conventional rectangular XL-RIS with the same number of RIS elements.

Quantum key distribution (QKD) is currently the only visible technology for secure symmetric key exchange between communicating parties. However, existing quantum photon sources (PSs) in QKD networks for generating keys between nodes are costly but offer limited achievable key rates. Moreover, achievable key rates across links diminish drastically with link distance, underscoring the importance of effectively identifying relay paths and strategically allocating PS resources. To optimize network costs, it is essential to jointly deploy PSs, allocate key rates across links, and determine relay paths based on anticipated traffic. To this end, we formulate a novel optimization problem, termed DAP, which simultaneously considers PS placement, key rate allocation, and relay path identification. Our proposed$O(\log\vert V\vert)$approximation algorithm, ADAP, leverages an advanced linear programming (LP) conversion with tailored rounding techniques. Simulation results manifest that ADAP achieves at least an 83 % reduction in the number of PSs.

Most quantum network schemes delay entanglement generation until a request arrives, causing slower processing. To this end, an approach of pre-establishing an initial shared state has emerged. However, the initial shared state must be versatile enough to accommodate all possible requests and may consume considerable qubits. It is crucial to minimize the number of qubits used while satisfying every possible request. We first introduce a 2 -approximation algorithm for the special case where each request consists of only one Bell or GHZ state requirement. Afterward, the 2 -approximation algorithm is extended to handle any possible request that may contain one or more requirements. Finally, via extensive simulation results, we show that our algorithm can outperform existing approaches by up to 29% in used qubits.

Robustness of reconfigurable intelligent surface (RIS) has been a concern due to potential hardware impairments (HWI) and imperfect channel state information (CSI) measurements caused by the numerous passive elements on board. Recent studies observe that the impairments not only introduce mis-alignment in phase adjustments but also affect the amplitude of reflected signals, which further complicates the issue. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based discrete optimization framework aimed at mitigating various HWI and CSI imperfections in RIS-assisted millimeter-wave (mmWave) multi-input-single-output (MU-MISO) systems. Employing proximal policy optimization (PPO), our method discretely addresses HWI and CSI challenges without continuous relaxation. Simulation results demonstrate the superiority of our approach over the traditional optimal beamforming baseline in minimizing the worst-case mean squared error (MSE) of the signal received by the users. The code has been made open-source on GitHub, serving as a valuable reference for further research and application in RIS-assisted communication systems.

Entangled pairs serve as the cornerstone for secure data transmission. All-optical-switching technology on nodes enables the entangling signals to bypass nodes and build ultra-long entangled pairs. Nevertheless, entangled pairs suffer from decoherence over distance, causing inadequate fidelity and potentially compromising transmission quality. To address the challenges, we employ entanglement purification to enhance fidelity to meet the threshold. However, the purification process consumes additional entangled pairs and may fail. Besides, the purification efficiency would be poor if the input pairs have low fidelity. Thus, it is unavoidable to divide the path into sub-paths with appropriate lengths for better purification efficiency and then merge them into a longer entangled pair by swapping. The novel optimization problem DOSP then emerges: maximizing the probability while adhering to fidelity constraints. To tackle the DOSP efficiently, we propose a (1–δ)-approximation algorithm NSPS to consider the probability and fidelity jointly, where is a positive user-defined constant. Finally, the simulation results manifest that the NSPS can outperform the existing methods by at least 70%.

Traditional end-to-end entanglement typically operates between two parties. However, such a setup may fall short when three parties are involved. GHZ states, a multi-qubit entangled state, provide an approach to such a problem. A fusion node is first chosen, and then the paths from all end nodes to the fusion node are fused to create an entangled state between all parties. The selection of the fusion node becomes critical since it highly affects the success probability of the whole process. Thus, in this paper, we explore the promising scenario for multiple requests of authorizable tripartite teleportation and introduce a novel optimization problem to maximize the (expected) total profit for 3-GHZ requests. Via extensive simulation results, we show that our algorithm can outperform existing approaches significantly.

Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imbalanced data can lead to sub-optimal performance. Traditional oversampling methods, while effective, risk overfitting and face difficulties in reintegrating synthetic samples into the original graph. In this study, we introduce Framework of Imbalanced Node Classification on heterogeneous graph neural network with GAN (FincGAN), a new framework that utilizes oversampling techniques to address class imbalance in heterogeneous graphs. Instead of duplicating existing samples, FincGAN employs a Generative Adversarial Network (GAN) to create synthetic samples and uses deep learning-based edge generators to connect them back to the original graph. Our evaluations on spam user detection in the Amazon and Yelp Review datasets show that FincGAN outperforms baseline models in all essential metrics, including F-score and AUC-PRC score, showing its effectiveness in addressing class imbalance.

Face editing has recently blossomed into a highly active and significant domain, impacting numerous applications from entertainment to security. Despite its rapid growth, the field still faces challenges in establishing a universally accepted and robust evaluation mechanism that can comprehensively assess the performances of face editing techniques and their underlying generative models. Our paper introduces a well-defined evaluation protocol that seamlessly combines systematic experimental methodologies with thorough subjective evaluations. This collaborative approach ensures a more in-depth and unbiased examination of face editing techniques. Based on our extensive studies, we observe that traditional metrics, notably the Fréchet Inception Distance (FID), serve well in measuring perceptual attributes of edited images. However, they might fall short in covering all aspects of a face editing method’s capabilities. To bridge this gap, we have incorporated additional metrics that assess disentanglement and editing effectiveness, leading to the creation of a holistic assessment framework that promises a more comprehensive evaluation upon face editing capability of different deep generative models.

Split learning (SL), a machine learning (ML) technique for collaborative training across devices and servers, partitions the model across entities to leverage computing power while preserving raw data privacy. However, device heterogeneity in terms of computation or communication capabilities causes the presence of stragglers, resulting in significant delays in the training process. This paper addresses the straggler problem in a wireless scenario with one edge server and multiple devices collaborating to train ML models using SL. We introduce a novel, low-complexity Coalition Formation Game (CFG) algorithm for SL in wireless networks. The CFG algorithm clusters work-ers based on their training times, effectively mitigating delays caused by stragglers. Specifically, this solution involves training devices negotiating to form or leave clusters to achieve better accuracy and/or shorter training delays, as well as selecting SL cut layers that best align with their communication and computing resources. Theoretical proof is provided, guaranteeing the termination of the CFG algorithm and the stability of the final clustering, where no devices have the incentive to leave the collaboration. We validate the proposed method on various datasets, and the results show that it strikes a good balance between lower training delay and high accuracy, consistently achieving top accuracy, and converging at the fastest speed.

Precise vehicle positioning is the key foundation for advancing vehicle automation technology beyond level three. However, the conventional global positioning system (GPS) is susceptible to inaccuracies caused by environmental interference. Existing works for improving positioning accuracy either require fundamental infrastructure modification to replace GPS or utilize prior knowledge of environmental information to reduce interfer-ence, where both are impractical in the real world. To improve the GPS-based vehicle positioning system to provide more accurate coordinate estimates without prior knowledge of environmental information, we propose a self-supervised learning architecture composed of four learning methods: hierarchical density-based spatial clustering (HDBSCAN), graph convolution network (GCN), domain-adversarial neural network (DANN), and long short-term memory (LSTM). The proposed framework utilizes both spatial and temporal information in vehicle positioning. The simulation results within our proposed comprehensive framework demonstrate a significant improvement in the accuracy of vehicle coordinate estimates, with the estimation error mean decreasing by 46 % and the error standard deviation decreasing by 34 % compared to the baseline.

Reconfigurable intelligent surfaces (RIS) is a promising technique to improve communication quality by adjusting the phase shift value of the passive reflected elements equipped on RIS. However, the phase shift value controlled by RIS may suffer from inevitable errors brought by hardware impairment and interference during transmission, which might lead to performance degradation. On the other hand, the assumption of uniform amplitude response made by most literature is not practical due to the imperfect reflection efficiency of the material. To jointly address these two issues, we consider a practical amplitude model that is a function of phase shift value and the phase shift value in our system is imposed an additional error following the Von-Mises distribution. To find the optimal solution that minimizes the average mean square error (MSE) of the received signal, we proposed a gradient descent method (GDM)-based phase shift algorithm that iteratively follows the gradient flow until converges. The numerical result is presented to show the superiority of our proposed algorithm over other existing algorithms.

Reconfigurable intelligent surface (RIS) is a revolutionary passive radio technique to facilitate capacity enhancement beyond the current massive multiple-input multiple-output (MIMO) transmission. However, the potential hardware impairment (HWI) of the RIS usually causes inevitable performance degradation and the amplification of imperfect CSI. These impacts still lack full investigation in the RIS-assisted wireless network. This paper developed a robust joint RIS and transceiver design algorithm to minimize the worst-case mean square error (MSE) of the received signal under the HWI effect and imperfect channel state information (CSI) in the RIS-assisted multi-user MIMO (MU-MIMO) wireless network. Specifically, since the proposed robust joint RIS and transceiver design problem yields non-convex characteristics under severe HWI, an iterative three-step convex algorithm is developed to approach the optimality by relaxation and convex transformation. Compared with the state-of-the-art baselines that ignore the HWI, the proposed robust algorithm inhibits the destruction of HWI while raising the worst-case MSE effectively in several numerical simulations. Moreover, due to the properties of the HWI, the performance loss is notable under the magnification of the number of reflected elements in the RIS-assisted MU-MIMO wireless network.

Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as “Least Delete (LD)” and “Largest-First Rounding with Capacity Constraints (LFRCC).” The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86$\%$and 89$\%$in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90$\%$. In addition, the solution of LFRCC is larger than that of the optimal solution within 7$\%$on average.

Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading decision and resource allocation need to be jointly handled to optimize the service provision efficiency within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision in the MEC system. M-DRL is composed of two parts:glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a “glimpse” of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements.

Distributed Denial-of-Service (DDoS) attack is critical to latency-critical systems such as Multi-Access Edge Computing (MEC) as it significantly increases the response delay of the victim service. An intrusion prevention system (IPS) is a promising solution to defend against such attacks. Still, there will be a trade-off between IPS deployment and application resource reservation as IPS deployment will reduce the computational resources for MEC applications. In this work, we propose a game-theoretic framework to study the joint computational resource allocation and IPS deployment in the MEC architecture. Given the expected attack strength and end-user demands, we study the pricing strategy of the MEC platform operator (MPO) and purchase strategy of the application service providers (ASPs). The best responses of both MPO and ASPs are derived theoretically. Based on the best responses, we propose an efficient algorithm to derive the Stackelberg equilibrium. The properties and optimality in the efficiency of the equilibrium are analyzed through simulations. The results confirm that the proposed solutions significantly increase the social welfare of the system.

Graph neural networks are a promising deep learning method that can apply graph structures to various tasks. In real-world scenarios, we often have heterogeneous graphs, wherein different node and edge types capture complex interactions between nodes. High-dimensional node features provide rich information about the target nodes. Conventional heterogeneous graph neural networks (HGNN) focus more on graph structures than node features and may have difficulties extracting knowledge from complex node features. In this study, we propose a novel framework, the tree-boosted heterogeneous graph neural network abbreviated as TreeXGNN, which could efficiently and automatically extract target node features via gradient-boosted decision trees (GBDT). It integrates community structure information with proper fusion modules and a shared feature space design on HGNN. We achieved state-of-the-art performance on the three well-known heterogeneous graph benchmark datasets, IMDB, DBLP, and ACM, and significantly improved performance compared to previous studies. Our work paves the foundation for integrating tree-based models to boost HGNNs for general community analysis.

Mobile live video streaming is expected to become mainstream in the fifth generation (5G) mobile networks. To boost the Quality of Experience (QoE) of streaming services, the integration of Scalable Video Coding (SVC) with Mobile Edge Computing (MEC) becomes a natural candidate due to its scalability and the reliable transmission supports for real-time interactions. However, it still takes efforts to integrate MEC into video streaming services to exploit its full potentials. We find that the efficiency of the MEC-enabled cellular system can be significantly improved when the requests of users can be redirected to proper MEC servers through optimal user associations. In light of this observation, we jointly address the caching placement, video quality decision, and user association problem in the live video streaming service. Since the proposed nonlinear integer optimization problem is NP-hard, we first develop a two-step approach from a Lagrangian optimization under the dual pricing specification. Further, to have a computation-efficient solution and less performance loss, we provide a one-step Lagrangian dual pricing algorithm by the convex transformation of non-convex constraints. The simulations show that the service quality of live video streaming can be remarkably enhanced by the proposed algorithms in the MEC-enabled cellular system.

Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-based services and applications, and personalized federated learning (PFL) evolves to alleviate the issue of data heterogeneity. However, FL and PFL usually rest on two assumptions: the users' data is well-labeled, or the personalized goals align with sufficient local data. Unfortunately, the two assumptions may not hold in most cases, where data labeling is costly, or most users have no sufficient local data to satisfy their personalized needs. To this end, we first formulate the problem, DoLP, that studies the issue of insufficient and partially-labeled data on FL-based services. DoLP aims to maximize two service objectives: 1) personalized classification objective and 2) the personalized labeling objective for each user within the constraint of training time over wireless networks. Then, we propose a PFL-based service system DoFed-SPP to solve DoLP. The DoFed-SPP's novelty is two-fold. First, we devise an inference-based first-order approximation metric, similarity ratio, to identify the similarity between users' local data. Second, we design an approximation algorithm to determine the appropriate size and set of users for uploading in each round. Extensive experiments show DoFed-SPP outperforms the state-of-the-art in final accuracy and time-to-accuracy performance on CIFAR10/100 and DBPedia.

Network traffic data are critical for network research. With the help of synthetic traffic, researchers can readily generate data for network simulation and performance evaluation. However, the state-of-the-art traffic generators are either too simple to generate realistic traffic or require the implementation of original applications and user operations. We propose Synthetic PAcket Traffic Generative Adversarial Networks (SPATGAN) that are capable of generating synthetic traffic. The framework includes a server agent and a client agent, which transmit synthetic packets to each other and take the opponent's synthetic packets as conditional labels for the built-in Timing Synthesis Generative Adversarial Networks (TSynGAN) and a Packet Synthesis Generative Adversarial Networks (PSynGAN) to generate synthetic traffic. The evaluations demonstrate that the proposed framework can generate traffic whose distribution resembles real traffic distribution.

Vehicle positioning is a key component of autonomous driving. The global positioning system (GPS) is the most commonly used vehicle positioning system currently. However, its accuracy will be affected by environmental differences and thus fails to meet the requirements of meter-level accuracy. We consider a coordinate neighboring vehicle positioning sys-tem (CNVPS) based on GPS, omnidirectional radar, and V2V communication ability to obtain additional information from neighboring vehicles to improve the GPS positioning accuracy of vehicles in various environments. We further use the concept of transfer learning (TL) wherein an adversarial mechanism is designed to eliminate the deviation of multiple environments to optimize vehicle positioning accuracy in multiple environments using one model. The simulation results show that, compared with the existing methods, the proposed system architecture not only improves the performance but also effectively reduces the amount of data required for training.

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly$\mathcal {NP}$-hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time$\left({1 - \frac {1}{e}}\right)$-approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless$\mathcal {P} = \mathcal {NP}$, and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks.

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we formulate the problem and derive the closed-form solution in the form of Lagrangian multipliers; the searching of the optimal variables is formulated as a Multi-Arm Bandit (MAB) and we propose a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement, especially in the low wireless resource and high user number scenario compared to other baselines.

Competitive Influence Maximization (CIM) aims to maximize the influence of a party given the competition from other parties in the same social network, like companies find key users to promote their competitive products on the social network to achieve maximum profit. Recently, learning-based solutions are introduced to tackle the competitive influence maximization problem. However, such studies focus on the static nature of social networks. This paper proposes a deep reinforcement learning-based framework employing network embedding, termed as DRL-EMB, to tackle the CIM problem on evolving social networks. The DRL-EMB key objective is to find the best strategy to maximize the party's reward, considering budget and competition with information propagation and network evolving being run in parallel. We validate our proposed framework with the DRL-based model using hand-crafted state features (DRL-HCF) and heuristic-based methods. Experimental results show that our proposed framework, DRL-EMB, achieves better results than heuristic-based and DRL-HCF models while significantly outperforming the DRL-HCF model in terms of time efficiency.

High-fidelity kinship face synthesis receives increasing interest in this technology for visual kinship applications, including law enforcement, social media analysis, finding lost children, etc. However, it is a challenging task because of the unresolved ambiguities by the limited amount of available kinship data and severe data noise. To address these issues, we leverage the pretrained state-of-the-art face synthesis model, StyleGAN2, to assist the synthesis. With StyleGAN2, we develop three different kinship face synthesis strategies: (1) synthesis based on the kinship statistics, (2) synthesis using the latent code interpolation of the parents, and (3) synthesis based on the latent code interpolation from the disentangled age and gender independent latent space. The first two methods synthesize kinship faces through the direct manipulation of the original StyleGAN2 latent codes. The third one, on the other hand, is a two-stage synthesis method which first learns an age and gender invariant latent representation upon the one of StyleGAN2 to represent the genes. Combining with the maximal selection process to fuse the corresponding representation of parents, we form the genes of the child followed by them feeding back to StyleGAN2 for the final synthesis. With extensive ablation studies and experiments, we observe that all three methods can generate more photo-realistic and clearer faces than the previous state-of-the-art method. In addition, the third method achieves the best kinship verification results on the FIW dataset. Surprisingly, the subjective evaluation results of the three proposed methods are very close because typical humans are not good at recognizing unfamiliar kinship faces.

The network attack such as Distributed Denial-of-Service (DDoS) attack could be critical to latency-critical systems such as Mobile Edge Computing (MEC) as such attacks significantly increase the response delay of the victim service. Intrusion prevention system (IPS) is a promising solution to defend against such attacks, but there will be a trade-off between IPS deployment and application resource reservation as the deployment of IPS will reduce the number of computation resources for MEC applications. In this paper, we proposed a game-theoretic framework to study the joint computation resource allocation and IPS deployment in the MEC architecture. We study the pricing strategy of the MEC platform operator and purchase strategy of the application service provider, given the expected attack strength and end user demands. The best responses of both MPO and ASPs are derived theoretically to identify the Stackelberg equilibrium. The simulation results confirm that the proposed solutions significantly increase the social welfare of the system.

Intermittent computing enables battery-less systems to support complex tasks such as face recognition through energy harvesting, but without an installed battery. Nevertheless, the latency may not be satisfied due to the limited computing power. Integrating mobile edge computing (MEC) with intermittent computing would be the desired solution to reduce latency and increase computation efficiency. In this work, we investigate the joint optimization problem of bandwidth allocation and the computation offloading with multiple battery-less intermittent devices in a wireless MEC network. We provide a comprehensive analysis of the expected offloading efficiency, and then propose Greedy Adaptive Balanced Allocation and Offloading (GABAO) algorithm considering the energy arrival distributions, remaining task load, and available computing/communication resources. Simulation results show that the proposed system can significantly reduce the latency in a multi-user MEC network with battery-less devices.

Network anomaly detection (NAD) aims to capture potential abnormal behaviors by observing traffic data over a period of time. In this work, we propose a machine learning framework based on XGBoost and deep neural networks to classify normal traffic and anomalous traffic. Data-driven feature engineering and post-processing are further proposed to improve the performance of the models. The experiment results suggest the proposed model can achieve 94% for F1 measure in the macro average of five labels on real-world traffic data.

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24 and 14.16 percent respectively.

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of Internet-of-Things (IoT) devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from IoT devices, the queueing delay at the edge nodes not only poses a colossal impediment to achieving satisfactory quality of experience (QoE) for the IoT devices but also to the benefits of the edge nodes owing to escalating energy expenditure. Moreover, since the service providers may differ, computationally competent entities' computing services should entail economic compensation for the incurred energy expenditure and the capital investment. Therefore, the workload allocation mechanism, where we consider flat-rate and dynamic pricing schemes in the multi-layer edge computing structure, is much-needed. We use Stackelberg game to capture the inherent hierarchy and interdependence between the second-layer edge node (SLEN) and first-layer edge nodes (FLENs). A truthful admission control mechanism grounded on the optimal workload allocation is designed for FLENs without violating end-to-end (E2E) latency requirements. We prove that a Stackelberg equilibrium with the E2E latency guarantee and truthfulness exists and can be reached through proposed algorithm. Simulation results confirm the effectiveness of our scheme and illustrate several insights.

Recent studies have considered the reinforcement and deep reinforcement learning models to address the competitive influence maximization (CIM) problem. However, these models assume complete network topology information is available to address the CIM problem. This assumption is unrealistic as it is difficult to obtain complete social network data and requires exhaustive efforts to obtain it. In this work, we propose a deep reinforcement learning-based (DRL) model to tackle the competitive influence maximization on unknown social networks. Our proposed model has a two-fold objective: the first is to identify the time when to explore the network to collect network information. The second is to determine key influential users from the explored network, using optimal seed-selection strategy considering the competition in the social network. Moreover, we integrate the transfer learning in DRL to improve the training efficiency of DRL models. Experimental results show that our proposed DRL and transfer learning-based DRL models achieve significantly better performance than heuristic-based methods.

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we first reformulate this problem as a Markov Decision Process (MDP) and develop a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement especially in the low wireless resource and high user number scenario compared to other baselines.

Mobile/multi-access edge computing (MEC) is therefore developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading and migration decision need to be jointly handled to maximize the utility of networks within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision problems in the MEC system. M-DRL is composed of two parts: DRL specialized in supporting multiple users joint training, and glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a “glimpse” of future. Through integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the service provision problem in MEC with acceptable computation complexity and near-optimal performance.

With the rapid development of deep learning technology, the modern Internet-of-Things (IoT) cameras have very high demands on communication, computing, and memory resources so as to achieve low latency and high accuracy live video analytics. Thanks to the mobile-edge computing (MEC), intelligent offloading to the MEC nodes can bring a lot of benefits, especially when the decomposable pipeline is adopted in the cloud-edge architecture. In this article, we provide decomposable intelligence on a cloud-edge IoT (DICE-IoT) framework to support joint latency- and accuracy-aware live video analytic services. Specifically, the intelligent framework enables the pipeline-sharing mechanism to reduce MEC resource usage. A Nash bargaining is proposed to incentivize cooperative computing provision between the MEC and the cloud, and a generalized benders decomposition (GBD)-based approach is utilized to optimize the social welfare. The results show that the proposed DICE-IoT framework can achieve a win–win–win solution to the IoT device, the MEC, and the cloud stratum.

The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system.

Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending the fog computing to conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a VFC system by combining both PVA and smart parking. A single- round multi-item parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatible, individual rational and budget balance. The simulation results confirmed the win-win performance enhancement to fog node controller (FNC), vehicles, and parking places from the proposed design.

In emergency medical services, the lag time between injury and treatment is one of the most critical parameters with respect to patient survivability. Ambulance services aim to maximize the likelihood of prompt medical treatment to prevent death and/or potential non-reversible damages. The emerging Tactile Internet has a vital role to play on that frontier by allowing next generation of ambulances to be equipped with advanced haptic/tactile devices to allow pre-hospital treatment/diagnosis or even remote surgery while en route. In this paper we propose a novel reliable multi-modal e- health high mobility service optimization framework for ambulances utilizing mobile edge clouds to efficiently transport real time patient information to the hospital. The main challenge of the proposed e-health service is to guarantee the heterogeneous QoS requirements of all involved data flows between the ambulance and the medical personnel. To this end, we formulate the service configuration problem as an optimization problem. In addition, a set of low- complexity algorithms are proposed to provide competitive solutions in real-time. A comprehensive set of numerical investigations are presented to characterize the attainable system performance of the proposed schemes.

Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.

Mobile social networks (MSNs) enable users to discover and share contents with each other, especially at ephemeral events such as exhibitions and conferences. Nevertheless, the incentive of users to actively share their contents in MSNs may be lacking if the corresponding cost is high. In this paper, we propose a content pricing and sharing framework in MSN that is built on users' collective bidding and content cost sharing. The content sharing problem is formulated as a distributed system that achieves cooperative outcome while preserving non-cooperative decision making among the users through the proposed collective bidding and broadcast nature of wireless communication. That is, co-located peers individually propose payments to their encounters whose contents they are interested in based on their perceived values of the contents. The respective content owners share their contents if the proposed payments can collectively compensate the cost of sharing their contents with these peers. We show that this guarantees individual rationality and promotes content sharing among the opportunistic encounters in the network. Performance evaluation shows that the proposed mechanism reduces the time and cost to collect contents of interest in the network and significantly improves network utilization.

Fog computing is proposed to mitigate cloud micro services, such as IoT gateway or AR data cache, from the centralized cloud to nearby base station (BS) or devices. The mobile users' devices can access service from nearby devices directly through Device to Device (D2D) communications. However, the incentive of devices to provide micro service is a challenge since the devices would spend their storage, computation, and energy resource. In this paper, we propose Fog Micro Service Market Game (FoMG), a game-theoretic framework to promote Fog computing in Fog-enabled cellular system on mobile devices through free market mechanism. The mobile users may decide the micro services they are going to rent (renting strategy) and the price of the services (pricing strategy) freely. The optimal strategy and Nash equilibrium of FoMG are derived through proposed algorithms. The simulation results showed that the overall system utility can be significantly improved through the proposed system.

Companies aim to promote their products under competitions and try to gain more profit than other companies. This problem is formulated as a Competitive Influence Maximization (CIM). Recently, a reinforcement learning has been used to solve the CIM problem, that is, to find an optimal strategy against competitor in order to maximize the commutative reward under the competition from other agents. However, reinforcement learning agents require huge training time to find an optimal strategy whenever the settings of the agents or the networks change. To tackle this issue, we propose a transfer learning method in reinforcement learning to reduce the training time and utilize the knowledge gained on source network to target network. Our method relies on two ideas, the first one is the state representation of the source and target networks in order to efficiently utilize the knowledge gained on source network to target network. The second idea is to transfer the final Q-solution of source network while learning on the target network. We validate our transfer learning method in similar or different settings of source and target networks while competing against the competitor's known strategies. Experimental results show that our proposed transfer learning method achieves similar or better performance as a baseline model while significantly reducing training time in all settings.

Most existing or currently developing Internet of Things (IoT) communication standards are based on the assumption that the IoT services only require low data rate transmission and therefore can be supported by limited resources such as narrow-band channels. This assumption rules out those IoT services with burst traffic, critical missions, and low latency requirements. In this paper, we propose to utilize the idle devices in mission-critical IoT networks to boost the transmission data rate for critical tasks through multiple concurrent transmissions. This approach virtually expands the existing narrow-band IoT protocols to break the bandwidth limitation in order to provide low latency services for critical tasks. In this approach, we propose the task-balance method and the first-link descending order to determine the relay order and data partition in a given relay set. We theoretically prove that the optimal relay configuration that minimizes the uploading latency in single source scenario can be derived by the proposed algorithms in polynomial time when we have sufficient number of available channels. We also propose a greedy algorithm to approximate the optimal solution within a 1/2 performance lower bound in general scenarios. The simulation results shows that the proposed approach can reduce the latency of critical tasks up to 76% comparing with traditional approaches.

Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.

With the large expected demand of wireless communication, Device-to-Device (D2D) communication has been proposed as a promising technology to enhance network performance. Nevertheless, the selfish nature of potential D2D users may impale the performance of D2D-enabled network. In this paper, we propose a D2D-enabled cellular network framework, which support a novel group D2D mode under overlay D2D communication. The group-based design is derived from the discussions of two common D2D modes, divided and shared D2D modes, regarded as special cases. The proposed framework provides a pricing-based dynamic Stackelberg game for optimal mode selection and spectrum partitioning. We propose the incentive compatible pricing strategy to provide proper incentive for these selfish potential D2D pairs to make optimal choices in mode selection. Our results show that the pricing and spectrum partition strategy effectively prevents selfish potential D2D users from harming the system performance while fully exploits the potential of D2D communication.

Most existing or developing IoT communication standards are based on the assumption that IoT services only require low data rate transmission and therefore can be supported by limited resources such as narrow-band channels. This assumption rules out those IoT services with burst traffic, critical tasks, and low latency requirements. In this paper, we propose to utilize idle devices in IoT networks to boost the transmission data rate for critical tasks through multiple concurrent transmissions. This approach virtually expands the existing narrow-band IoT protocols to support channel aggregation in order to realize low latency services for critical tasks in IoT networks. We propose task-balance method (TBM) and first-link descending order (FDO) to determine the relay order and data partition in a given relay set. We theoretically prove that the optimal relay configuration that minimizes the uploading latency can be derived in polynomial time. We then show that relay selection problem is NP-hard and propose a greedy algorithm to approximate the optimal solution within a 1/2 performance lower bound. The simulation results shows that the proposed approach can reduce the latency of critical tasks up to 76% comparing with traditional approaches.

Massive machine type communication (MTC) brings new challenges to next generation wide-area networks. Random Access Channel procedure (RACH) used in LTE-Advanced system for request delivery, for instance, is inefficient in handling massive number of concurrent access requests. The S-Aloha approach in RACH will lead to unsustainable delays and resource wastage due to collisions from concurrent access in massive MTC networks. To address this issue, we propose a distributed queue based MTC communication protocol that exploits multiple packet reception (MPR) capability of MTC devices to self-organize themselves into a logical queue in a distributed way such that the devices connect to BS depending on their positions in the queue. We derive the algorithm and present analysis of its performance in regard to delay and throughput. The algorithm behaves like S-Aloha when MTC traffic load is low and seamlessly and smoothly switches to a reservation based protocol during heavy traffic. From the simulation results, the proposed scheme exhibits better performance compared to conventional RACH procedure as it increases number of successful transmissions by 2.7 times and reduces both the average access delay and transmission failure probability by 20%.

Carrier aggregation is introduced in LTE-Advanced to aggregate multiple bands of spectrum into a virtual carrier. User equipment (UE) with carrier aggregation capability can increase peak data rates by transmitting through an aggregated virtual carrier that provides greater transmission bandwidth. Nevertheless, further study is needed to determine how carrier aggregation should best be implemented and configured to effectively address the range of UE carrier quality and their heterogeneous quality of service (QoS) requirements. In addition, most existing resource allocation methods rely on the assumption that UE always reports information truthfully, which may be unrealistic when UEs act rationally from a game-theory perspective. To address these concerns, we provide a strategy-proof auction approach to carrier aggregation design in an LTE-Advanced system. We first formulate the resource allocation problem in carrier aggregation as a non-linear optimization problem, which is proved to be NP-hard. We then propose a strategy-proof auction with a greedy resource allocation algorithm to 1) find an efficient carrier activation and resource allocation solution under the QoS requirements of UEs, and 2) guarantee that all rational UEs truthfully report their QoS requirements. Finally, we conduct extensive simulations to evaluate system performance for the proposed auction design.

An overlay macrocell-femtocell system aims to increase the system capacity with a low-cost infrastructure. To construct such an infrastructure, we need to solve some existing problems. First, there is a tradeoff between femtocell coverage and overall system throughput, which we defined as the cell-breathing phenomenon. In light of this, we propose a femtocell downlink cell-breathing control framework to strike a balance between the coverage and data rate. Second, due to the selfish nature of mobile stations, the system information collected from them does not necessarily reflect the true status of the system. Thus, we design FEmtocell Virtual Election Rule (FEVER), a voting-based direct mechanism that only requires users to report their channel quality information to the femtocell base station. Not only is it proved to be truthful and has low implementation complexity, but it also strikes a balance between efficiency and fairness to meet the different needs. The simulation results verify the enhanced system performance under the FEVER mechanism.

Since uplink (UL) and downlink (DL) traffic loads are time-variant in femtocells, it is essential to adopt dynamic time-division duplexing (TDD) to effectively adjust the uplink and downlink transmission resources. However, the cross-link interference between dynamic TDD femtocells decreases the throughput gain of dynamic TDD. In this paper, we propose an evolutionary game-based distributed approach to choose the UL-DL configuration in order to minimize interference and maximize the system throughput in a large-scale femtocell network. A multiple populations evolutionary game is formulated to model femtocells with different traffic loads. We prove that the evolutionarily stable strategy (ESS) of the considered multiple populations evolutionary game is the optimal configuration which maximizes the system throughput. Simulation results confirm the effectiveness of the proposed evolutionary game-based approach for system throughput optimization in femtocells employing dynamic TDD.

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. One of the challenges is maintaining the quality of service due to scarce resources in wireless communications and heavy loadings from heterogeneous demands. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design, which has been shown to effectively enhance the quality of multimedia content delivery service in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios, where a snapshot of user demands is given and remains the same. In addition, the economic value of the SVC multicasting system, which is an important issue from the service provider's perspective, has seldom been explored. In this paper, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on the multidimensional Markov decision process (M-MDP) is proposed to study the negative network externality existing in the proposed system and theoretically evaluate the corresponding system efficiency. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users under different subscription pricing schemes. By transforming the original dynamic and complex M-MDP revenue optimization problem into a traditional average-reward MDP problem, we show that the optimal pricing strategy that maximizes the expected revenue of the service provider can be derived efficiently. Moreover, the overall user's valuation on the system, e.g., social welfare, is maximized under such an optimal pricing strategy. Finally, the efficiency of the proposed solutions is evaluated through simulations.

The privacy issue of online social networks (OSNs) has been getting attention from the public, especially when data privacy has caused the disagreement between users and OSN providers. While the providers utilize users' data as a commercial usage to make profit; on the other hand, users feel their privacy has been violated by such behavior. In this paper, we propose a privacy preserving protocol for users' data sharing in OSNs, where the OSN provider cannot retrieve the users' social content while the users can efficiently add or remove a social contact and flexibly perform the data access control. Moreover, we prove that the users would allow the OSN provider to perform keyword search over the encrypted content for advertising profit, so that the OSN provider can commercialize its products without the knowledge of content.

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.

With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.

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.

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design which has been shown to be effectively solution in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios. In addition, the economic value of SVC multicasting system has seldom been explored. In this work, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on Multi-dimensional Markov Decision Process (M-MDP) is proposed to study the negative network externality existing in the proposed system. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users the subscription economic model. We show that the optimal pricing strategy which maximizes the expected revenue of the service provider can be derived through dynamic iterative updating techniques. Moreover, the overall user's valuation on the system is maximized under such an optimal pricing strategy. Finally, the solution efficiency is evaluated through simulations.

Most service providers offer an unlimited data service plan under a flat, fixed-rate contract to meet the huge demand. However, because service quality and user experience can vary dramatically in wireless communications, such a contract design is unable to provide equal service quality for all users, which greatly limits the profit potential of service providers. As a result, mobile industries look to femtocell technology to improve service quality and increase profit by attracting customers. Meanwhile, differentiated contracts for different types of users also show great potential for profit increase. In this paper, we investigate unlimited data service plans in terms of enhancements from both femtocell systems and differentiated contracts. The incentive compatibility (IC) issue in differentiated contract design is considered under the overlay macrocell-femtocell system in both split-spectrum and shared-spectrum models. The profits under optimal differentiated contracts, with and without the IC condition are compared to traditional flat fee contracts, and numerical results show that optimal differentiated contracts indeed generate more profits and serve more 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.

In cognitive radio networks, secondary users (SUs) are allowed to opportunistically exploit the licensed channels by sensing primary users' (PUs) activities. Once finding the spectrum holes, SUs generally need to share the available licensed channels. Therefore, one of the critical challenges for fully utilizing the spectrum resources is how the SUs obtain accurate information about the PUs' activities and make right decisions of accessing channels to avoid competition from other SUs. In this paper, we formulate SUs' learning and decision making process as a Chinese Restaurant Game by considering the scenario where SUs sense channels simultaneously and make access decisions sequentially. In the proposed game, SUs build the knowledge of the PUs' activities by their own sensing and learning the information from other SUs. They also predict their subsequent SUs' decisions to maximize their own utilities. We analyze the interactions among SUs in the proposed game and study specifically the impact of SUs' prior belief and sensing accuracy on their decisions. We also derive the theoretic results for the two-user two-channel case. Finally, we demonstrate the effectiveness and efficiency of the proposed scheme through simulations.

In this letter, by introducing the strategic decision making into the Chinese restaurant process, we propose a new game, called Chinese Restaurant Game, as a new general framework for analyzing the individual decision problem in a network with negative network externality. Our analysis shows that a balance in utilities among the customers in the game will eventually be achieved under the strategic decision making process. The equilibrium grouping is defined to describe the predicted outcome of the proposed game, which can be found by a simple algorithm. The simulation results confirm that the rational customers in Chinese restaurant game automatically achieve a balance in loading in order to reduce the impact from the negative network externality.

Game theory has been applied to model power control in wireless systems for years. Conventional power control games tend to consider unlimited backlogged user traffic. Different from the conventional methodology, this paper aims to investigate limited backlogged data traffic and construct a game- theoretic model tackling both one-shot and repeated power control problem in which variable user traffic demand needs to be taken into consideration. To improve the network performance in such situation, we devise a new SINR pricing scheme and propose an algorithm to calculate an optimal price. With this optimal price, we prove that the Nash equilibrium is Pareto efficient and max-min fair.

Interference in unlicensed band is a severe problem due to sharing of the limited spectrum resource and uncoordinated power transmission of wireless access points. To solve this problem, we aim for minimizing interference among Access Points'coverage while preserving throughput fairness. We investigate this problem by modelling the spectrum sharing WLAN networks as a cooperative game. A bargaining game model is desirable in this frequently changing WLAN networking environment. Our solution is developed based on Nash Bargaining Solution, which can derive desirable properties such as Pareto Efficiency and proportional fairness. In this paper, we first derive the Nash Bargaining Solution for a 2-AP case and a 3-AP case. Then we provide a general solution for scenario with arbitrary number of APs. Simulation results show that the total networking throughput in maximum in the proposed scheme, compared with all other possible transmission power strategies. The interference between WLAN APs is minimal. Our work can lead to a cooperative spectrum sharing WLAN networking design with allocation fairness and optimal throughput performance.