Semantic communication is a novel communication paradigm that focuses on conveying the user's intended meaning rather than the bit-wise transmission of source signals. One of the key challenges is to effectively represent and extract the semantic meaning of any given source signals. While deep learning (DL)-based solutions have shown promising results in extracting implicit semantic information from a wide range of sources, existing work often overlooks the high computational complexity inherent in both model training and inference for the DL-based encoder and decoder. To bridge this gap, this paper proposes a rate-distortion-complexity (RDC) framework which extends the classical rate-distortion theory by incorporating the constraints on semantic distance, including both the traditional bit-wise distortion metric and statistical difference-based divergence metric, and complexity measure, adopted from the theory of minimum description length and information bottleneck. We derive the closed-form theoretical results of the minimum achievable rate under given constraints on semantic distance and complexity for both Gaussian and binary semantic sources. Our theoretical results show a fundamental three-way tradeoff among achievable rate, semantic distance, and model complexity. Extensive experiments on real-world image and video datasets validate this tradeoff and further demonstrate that our information-theoretic complexity measure effectively correlates with practical computational costs, guiding efficient system design in resource-constrained scenarios.

Federated Learning (FL) is a promising paradigm for realizing edge intelligence. However, its practical deployment remains vulnerable to model perturbations, such as communication noise and quantization errors, which can significantly degrade model accuracy. Moreover, this degradation is further amplified in the presence of data heterogeneity. To address the above issues, we propose a novel FL framework, termed Sharpness-aware Minimization-based Robust Federated Learning (SMRFL), which provides intrinsic model robustness against perturbations by leveraging an intriguing geometrical property of the loss landscape: models around a flat minimum tend to exhibit similarly low loss values and thus higher robustness. Specifically, SMRFL encourages model convergence toward flat minima by solving a min-max optimization problem that minimizes the worst-case loss within a neighborhood of the model. To further mitigate the impact of data heterogeneity, we proposed Locally Aligned-SMRFL (LA-SMRFL), which incorporates a consensus constraint into the min-max optimization and solves it via inexact Alternating Direction Method of Multipliers (ADMM). We provide theoretical analysis for the convergence of SMRFL and LA-SMRFL in the non-convex FL setting, and derive robustness bounds via the certified radius. Extensive experiments on two real-world datasets under three perturbation scenarios demonstrate that SMRFL and LASMRFL substantially outperform baseline methods in terms of robustness.

Semantic communication is a novel communication paradigm focusing on the transmission of meaningful, task-oriented information. Recent results have shown that graphical structures represent the most robust and structurally faithful formalism for modeling the semantic knowledge within a wide range of source signals. However, previous solutions focus primarily on the pairwise relational graphs, which inherently lack the capacity to encapsulate complex, higher-order interactions fundamental to specific semantic contexts. In contrast, hypergraphs provide a more flexible mathematical framework that can more accurately map the multidimensional dependencies found in intricate data sources. In this paper, we investigate hypergraph-based semantic representation for the semantic communication system. We propose a novel hypergraph information bottleneck-based implicit semantic communication framework (HIB-SC), in which the semantic encoder is developed and optimized to extract the minimally sufficient representation of the hypergraph-based semantic information source, that maximizes the mutual information between the encoded representation and the implicit high-order semantic relations that are intended for the receiver. We theoretically prove that the proposed framework is able to extract the most informative subgraphs or motifs that are significantly more robust against adversarial attacks with improved generalization performance. Extensive experiments verify that the proposed HIB-SC achieves superior semantic compression efficiency, higher accuracy in implicit semantic inference, and enhanced resilience against noise, compared to the state-of-the-art solutions.

The Internet-of-Things (IoT) enables the connection of myriad wireless devices, generating a massive influx of data that can overwhelm central servers. Federated learning (FL) mitigates these issues by distributing computation tasks across edge devices, thus preserving data privacy by eliminating the need for raw data transmission. However, when deployed in large-scale IoT networks, FL encounters several challenges such as device energy constraints, heterogeneous network conditions, and the straggler effect, which inevitably impedes model convergence. In response, this paper proposes a semi-decentralized hybrid FL (SDHFL) scheme leveraging an unmanned aerial vehicle (UAV) as a mobile data center to collect data from distributed IoT clusters. To further expedite FL convergence, we formulate a non-convex mixed-integer nonlinear programming (MINLP) problem to minimize overall completion time while ensuring quality of service (QoS) requirements, considering both the UAV's energy constraints and network stability. We demonstrate that efficient learning is achieved by optimizing both power allocation and device computational capability. Furthermore, we prove the convergence of the proposed SDHFL scheme and derive the minimum number of global iterations required. Exploiting these insights, we introduce a low-complexity suboptimal algorithm for dynamic cluster selection and resource allocation optimization, leveraging Lyapunov optimization theory to obtain optimal solutions efficiently, demonstrating its scalability for large-scale networks. Our simulation results validate the effectiveness of the proposed SDHFL framework, revealing a non-trivial tradeoff where the overall completion time initially decreases and then increases as the number of devices or clusters grows, indicating the necessity of optimizing both cluster counts and intra-cluster device allocations. Furthermore, compared to several baseline schemes, our proposed optimal algorithms significantly reduce overall completion time and enhance model convergence, demonstrating the potential of SDHFL for enabling efficient and scalable FL in IoT networks.

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decisions, dynamic environmental adaptation, and complex missions. AgentNet has the potential to facilitate real-time network management and optimization functions, including self-configuration, self-optimization, and self-adaptation across diverse and complex environments, laying the foundation for fully autonomous networking systems. Despite its promise, AgentNet is still in the early stages of development and still lacks an effective networking framework to support automatic goal discovery, multi-agent self-orchestration, and task assignment. This paper proposes SANet, a novel semantic-aware AgentNet architecture for wireless networks. SANet can infer the semantic goal of the user and automatically assign agents associated with different layers of the network stack to fulfill the inferred goal. Motivated by the fact that AgentNet is a decentralized framework in which collaborating agents may generally have different and even conflicting objectives, we formulate the decentralized optimization of SANet as a multi-agent multi-objective problem, and focus on finding the Pareto-optimal solution for agents with distinct and potentially conflicting objectives. We propose three novel metrics for evaluating SANet: (the agents' objective) optimization error, (dynamic environment) generalization error, and (multi-objective) conflicting error. Furthermore, we develop a model partition and sharing (MoPS) framework in which large models, e.g., deep learning models, of different agents can be partitioned into shared and agent-specific parts that are jointly constructed and deployed according to agents' local computational resources. Two decentralized optimization algorithms, static-weighting and dynamic-weighting algorithms, are introduced to optimize the above three metrics. A bandwidth-adaptive compression framework is also proposed to enable different agents to perform in situ compression of their intermediate embeddings, dynamically adjusting to localized resource constraints and task requirements. We derive theoretical bounds for all these performance metrics and prove that there exists a three-way tradeoff among optimization, generalization, and conflicting errors. Finally, to validate our theoretical results, we develop an open-source Radio Access Network (RAN) and core network-based hardware prototype that implements three Transformer-based time-series prediction agents to interact with three different layers of the network. Experimental results show that the proposed MoPS framework achieves performance gains of up to$14.61\%$while requiring only$44.37\%$of the Floating-Point Operations (FLOPs) for inference at each agent compared to state-of-the-art algorithms. Also, compared to the static-weighting algorithm, the dynamic-weighting algorithm achieves up to$83.81\%$reduction in training errors caused by conflicting objectives.

Agentic AI networking (AgentNet) has attracted significant interest due to its promising potential to move traditional AI-based networking solutions beyond closed-loop and passive learning to proactive interaction and goal-driven action, offering a path to self-learning and generally intelligent networking systems. Despite its promise, ensuring the security and trustworthiness of such systems presents significant challenges, particularly concerning identity management, agent capability verification, and data integrity during collaborative learning. To address these issues, this paper proposes TrustAgentNet, a novel consortium blockchain-based framework for unified and trusted agent identification, traceable skillset and tag descriptions, and secure on-chain collaborative learning in AgentNet. In TrustAgentNet, a chain of skillset (CoS) is introduced, consisting of a skillset chain to distributedly store all the verified skillsets and associated tags, and a dedicated training chain for each distinct skillset can be jointly constructed and maintained by the authorized agents using a collaborative learning-based approach. Theoretical analysis suggests that there exists a three-way trade-off among the security level, skillset performance, and resource cost. This tradeoff is also empirically validated by the experimental results obtained from a hardware prototype implemented based on a Hyperledger Fabric-based consortium blockchain. To verify the practical performance of TrustAgentNet, we consider a real-world scenario of multi-agent collaborative learning under malicious attack. Experimental results suggest that TrustAgentNet can effectively guarantee the security of skillset training and enable rapid response and recovery from potential attacks within seconds.

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm that relies on a large number of specialized AI agents to collaborate and coordinate for autonomous decision-making, dynamic environmental adaptation, and complex goal achievement. It has the potential to facilitate real-time network management alongside capabilities for self-configuration, self-optimization, and self-adaptation across diverse and complex networking environments, laying the foundation for fully autonomous networking systems in the future. Despite its promise, AgentNet is still in the early stage of development, and there still lacks an effective networking framework to support automatic goal discovery and multi-agent self-orchestration and task assignment. This paper proposes SANNet, a novel semantic-aware agentic AI networking architecture that can infer the semantic goal of the user and automatically assign agents associated with different layers of a mobile system to fulfill the inferred goal. Motivated by the fact that one of the major challenges in AgentNet is that different agents may have different and even conflicting objectives when collaborating for certain goals, we introduce a dynamic weighting-based conflict-resolving mechanism to address this issue. We prove that SANNet can provide theoretical guarantee in both conflict-resolving and model generalization performance for multi-agent collaboration in dynamic environment. We develop a hardware prototype of SANNet based on the open RAN and 5GS core platform. Our experimental results show that SANNet can significantly improve the performance of multi-agent networking systems, even when agents with conflicting objectives are selected to collaborate for the same goal.

Federated Learning (FL) is a promising paradigm for realizing edge intelligence, allowing collaborative learning among distributed edge devices by sharing models instead of raw data. However, the shared models are often assumed to be ideal, which would be inevitably violated in practice due to various perturbations, leading to significant performance degradation. To overcome this challenge, we propose a novel method, termed Sharpness-Aware Minimization-based Robust Federated Learning (SMRFL), which aims to improve model robustness against perturbations by exploring the geometrical property of the model landscape. Specifically, SMRFL solves a min-max optimization problem that promotes model convergence towards a flat minimum by minimizing the maximum loss within a neighborhood of the model parameters. In this way, model sensitivity to perturbations is reduced, and robustness is enhanced since models in the neighborhood of the flat minimum also enjoy low loss values. The theoretical result proves that SMRFL can converge at the same rate as FL without perturbations. Extensive experimental results show that SMRFL significantly enhances robustness against perturbations compared to three baseline methods on two real-world datasets under three perturbation scenarios.

Semantic-aware communication (SAC) has attracted significant interest recently due to its potential to revolutionize the traditional communication framework by focusing on delivering the meaning of information, enabling more efficient, reliable, and intelligent communication. Most existing AI-based solutions for multi-user SAC focus on developing encoding models for all users and a decoding model for a specific receiver. While powerful, this single-task-oriented codec design faces significant challenges when deployed in more general multi-task scenarios. In this paper, we investigate codec design problems in multi-user multitask SAC based on distributed information bottleneck (DIB) theory. We propose a novel task-aware DIB scheme (TADIB) for joint codec designing. In TADIB, the receiver, when performing a specific task, first estimates the relevance between users' datasets and the task based on their mutual information, which is incorporated into the training phase of both encoders and decoders. Then most task-relevant users are selected to send the encoded signals for task inference during the deployment phase. Furthermore, we employ variational approximation to derive tractable upper bounds for the DIB-based objective, which would otherwise be computationally prohibitive for high-dimensional data. This approach also reduces the computational complexity of the user selection procedures. Extensive results show that the proposed TADIB can achieve up to 3.38% improvements in inference accuracy of classification tasks, compared to existing solutions for task-oriented SAC.

This paper investigates the joint source-channel coding (JSCC) problem for semantic-aware communication (SAC) systems with multi-view data sources. We extend the theory of multi-view information bottleneck by incorporating a constraint that characterizes the impact of channel corruption on the recovery of task signals at the receiver, referred to as the multiview information bottleneck with channel protection (MVIB-CP). We propose a computationally efficient optimization framework for calculating the MVIB-CP problem in which a contrastive log-ratio upper bound and a simplified variation approximation solution are introduced to estimate mutual information and approximate the divergence of task signal recovery caused by the noisy channel corruption. The proposed MVIB-CP can investigate the tradeoff of the multi-view source compression and the protection of channel corruptions while the above optimization framework can significantly reduce the computational complexity of addressing the MVIB-CP. We present the optimization algorithm for developing the JSCC schemes in multi-view SAC systems. Extensive experiments are conducted based on realworld multi-view source datasets. Our results show that the MVIB-CP-based JSCC achieves up to$\mathbf{5. 2 8 \%}$improvement in task signal recovery accuracy, compared to the classical JSCC solution.

Network slicing is one of the key enablers for B5G and 6G to support diversified IoT services and application scenarios. In this paper, the problem of network slicing for supporting time-sensitive edge learning in massive-scale IoT networks is studied. In particular, a novel distributed network slicing framework based on a new control plane entity, called D-orchestrator, is proposed. This framework can jointly optimize the allocation and orchestration of communication and edge computational resources without requiring exchanges of the local data or resource information between base stations (BSs) and edge servers. A distributed joint resource allocation algorithm is developed based on the alternating direction method of multipliers with partial variable splitting (DistADMM-PVS) that minimizes the average service response-time of a set of service instances when the coordination among the D-orchestrator, BSs, and edge servers is perfectly synchronized. Motivated by the observation that the synchronization of coordination may result in high coordination delay that can be intolerable in many practical scenarios, particularly for large IoT networks, a novel asynchronized ADMM (AsyncADMM) algorithm is proposed. In AsyncADMM, the D-orchestrator, BSs, and edge servers can be coordinated asynchronously. AsyncADMM is then shown to converge to the global optimal solution with improved scalability and negligible coordination delay. The performance of the proposed framework is evaluated using two-month of traffic data collected in an in-campus smart transportation system supported by a 5G network. Extensive simulations are conducted for both pedestrian and vehicular-related services during peak and non-peak hours. Simulation results show that the proposed distributed network slicing framework offers a significant reduction in the service response time for both supported services.

Autonomous aerial vehicles (AAV)-enabled IoT networks have shown promising potential in a range of novel applications and service scenarios, such as extending the network coverage, extending the battery lifetime of IoT networks, and also supporting temporary data collecting and processing needs in various emergency situations. This article studies a federated edge intelligence (FEI) network based on the data collected and uploaded by a AAV-enabled IoT network. More specifically, a set of AAVs has periodically collected and uploaded the data generated by an IoT network to a set of edge servers. Edge servers will then collaboratively construct shared models based on the uploaded datasets. The data uploading performance of a AAV-enabled IoT network and the computational capacity of edge servers are entangled with each other in influencing the overall model training process. We propose a new framework called AAV-enabled IoT network for FEI (U-FEI). This framework enables edge servers to assess how many data samples need to be collected based on the energy costs of the AAV-enabled IoT network. It also considers the local data processing capacity of the edge servers. As a result, the edge servers can request just the right amount of data from the AAVs, which is enough to train a satisfactory model. We evaluate the energy cost for data uploading of AAVs when the data can be uploaded from two different types of frequencies: 1) licensed bands (e.g., using 5G) and 2) unlicensed bands (e.g., using Wi-Fi, ZigBee, or 5G NR-U). We prove that the cost minimization problem of the entire AAV-enabled IoT network is separable and can be divided into a set of subproblems, each of which can be solved by an individual edge server. We also introduce a mapping function to quantify the computational load of edge servers under the combinations of three key parameters: 1) size of the dataset; 2) local batch size; and 3) number of local training passes. Finally, we adopt an alternative direction method of multipliers (ADMM)-based approach to jointly optimize the energy cost of the AAV-enabled IoT network and average resource utilization of edge servers. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the AAV-enabled IoT networks. Simulation results show that our proposed framework significantly improves the resource efficiency of both the AAV-enabled IoT network and edge servers.

Semantic-and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of the next-generation mobile networks by transmitting only the most relevant information needed to complete a specific task at the receiver. This is particularly advantageous for machine-oriented communication of high-data-rate content, such as images and videos, where the goal is rapid and accurate inference, rather than perfect signal reconstruction. While semantic-and task-oriented compression can be implemented in conventional communication systems, joint source–channel coding (JSCC) offers an alternative end-to-end approach by optimizing compression and channel coding together, or even directly mapping the source signal to the modulated waveform. Although all digital communication systems today rely on separation, thanks to its modularity, JSCC is known to achieve higher performance in finite blocklength scenarios and to avoidcliffand theleveling-off effectsin time-varying channel scenarios. This article provides an overview of the information theoretic foundations of JSCC, surveys practical JSCC designs over the decades, and discusses the reasons for their limited adoption in practical systems. We then examine the recent resurgence of JSCC, driven by the integration of deep learning techniques, particularly through DeepJSCC, highlighting its many surprising advantages in various scenarios. Finally, we discuss why it may be time to reconsider today’s strictly separate architectures and reintroduce JSCC to enable high-fidelity, low-latency communications in critical applications such as autonomous driving, drone surveillance, or wearable systems.

Contactless device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications using ubiquitously available radio frequency (RF) signals. Traditional approaches focus on developing a single global model based on a combined dataset collected from different locations. However, wireless signals are known to be location and environment specific. Thus, a global model results in inconsistent and unreliable sensing results. It is also unrealistic to construct individual models for all the possible locations and environmental scenarios. Motivated by the observation that signals recorded at different locations are closely related to a set of physical-layer semantic features, in this paper we propose SANSee, a semantic-aware networking-based framework for distributed wireless sensing. SANSee allows models constructed in one or a limited number of locations to be transferred to new locations without requiring any locally labeled data or model training. SANSee is built on the concept of physical-layer semantic-aware network (pSAN), which characterizes the semantic similarity and the correlations of sensed data across different locations. A pSAN-based zero-shot transfer learning solution is introduced to allow receivers in new locations to obtain location-specific models by directly aggregating the models trained by other receivers. We theoretically prove that models obtained by SANSee can approach the locally optimal models. Experimental results based on real-world datasets are used to verify that the accuracy of the transferred models obtained by SANSee matches that of the models trained by the locally labeled data based on supervised learning approaches.

To address the limitations in the wireless communication courses in our university including lack of in-class hours, practical experiences, integration with latest development and discussions on latency, we have developed and implemented a comprehensive research-oriented online laboratory since 2021, focusing on 5G-V2X Latency measurements, modeling and optimization in our campus environment. By incorporating recent research outcomes of our faculty members, we design four progressive and game-like task modules, all utilizing self-developed open-source software/algorithms and students' own laptops so as to support the scalability and accessibility. The curriculum and lesson planning of this laboratory follows the flipped-classroom model and BOPPPS model, respectively, to create an effective and student-oriented learning environment. After two rounds of practice, fruitful outcomes are obtained including dataset collection and algorithm design, as well as positive feedback from students particularly regarding their growing interest and self-motivation in the experiment contents and their satisfaction with the teaching and mentoring provided.

RF-based wireless sensing is a promising technology for enabling applications such as human activity recognition, intelligent healthcare, and robotics. However, the growing concern in data privacy and also the complexity in modeling and keeping track of the statistical heterogeneity of RF signal hinders its wide application, especially in large-scale wireless networking systems. In this paper, we introduce a hierarchical clustering-based federated learning framework, called Uniform Manifold Clustering Federated Learning (UMCFL), that has the potential to address the above challenges. UMCFL first divides all the RF signal receivers into different clusters according to the similarity of their data distributions and then construct an individual model for receivers within each cluster. To measure the data distribution similarity between receivers in a computationally efficient way, UMCFL first adopts a uniform manifold approximation and projection (UMAP)-based solution to convert data samples at each receiver into a low-dimensional representation and then use maximum mean discrepancy (MMD) to calculate the similarity score between datasets of different receivers. We prove the convergence of UMCFL and perform extensive experiments to evaluate its performance. Experimental results show that UMCFL achieves up to 63% improvement in sensing accuracy, compared to the traditional FedAvg-based wireless sensing solution.

Wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications without requiring any extra devices to be carried out by human users. However, previous studies have shown that wireless sensing performance can be significantly degraded if the relative locations of the transmitters, human users, and receivers are non-ideal and/or the distances between the user and receivers are large. These constraints hinder the wide applications of wireless sensing in many practical scenarios. A promising approach for wireless sensing is through the use of reconfigurable intelligent surfaces (RISs) that can control the propagation environment to create a customizable wireless environment for wireless sensing. To this end, in this paper, the use of RIS-assisted wireless sensing for enhanced human gesture recognition is investigated. A novel RIS-assisted distributed wireless sensing framework that utilizes federated learning (FL) is proposed to enable collaborative model training among decentralized receivers. Then, a novel metric, called human-influencing signal-to-interference ratio (HSIR), is introduced to characterize the quality of locally recorded data as well as its impact on the performance of wireless sensing. To alleviate the model draft problem of FL-assisted wireless sensing, caused by spatial heterogeneity of the quality of wireless sensing data at different receivers, the optimal amplitudes and phases of the RIS are derived so as to improve the HSIR of a set of low-performance receivers located at non-ideal locations. Simulation results show that the proposed RIS-assisted system can significantly improve wireless sensing accuracy by up to 20.1% compared to traditional distributed system.

Developing sustainable and environmentally friendly network AI solutions has attracted significant interest recently. Unfortunately, analyzing the overall environmental impact, the greenhouse emissions in particular, of a network AI implementation is known to be a notoriously challenging task. As a popular distributed AI framework, federated edge intelligence (FEI) has been promoted as a candidate technology for implementing network AI in 6G. Unfortunately, recent studies suggest that the FEI network may generate more carbon emissions than the traditional centralized AI solutions. In this paper, we propose a novel analytical framework to quantify and optimize the carbon emissions of FEI networks. We develop an analytical model for quantifying the overall carbon emissions required for constructing a shared model in FEI with the guaranteed accuracy level. We propose a server-dropping-based algorithm that removes the highest-emitting and low-contributing edge servers from participating in the model training to minimize the overall carbon emissions. We conduct extensive experiments and the experimental results show that our proposed algorithm reduces up to 80% of carbon emissions, compared to the traditional FL-based solution.

The Digital Twin Network (DTN) holds immense potential in shaping future networks by seamlessly integrating physical networks and virtual representations. However, high-fidelity twin network modeling proves complicated and resource-intensive. To address this challenge, this paper proposes a federated generative model with multiple generators for DTN (FMG-DTN) modeling, which can generate high-fidelity data without increasing communication overhead. Firstly, generative learning is employed, enabling FMG to accurately synthesize data following the distribution of real data. Secondly, the model of FMG and its corresponding loss function are designed to mitigate the mode collapse problem in the presence of data heterogeneity by encouraging different generators to synthesize data with different categories. The mode collapse problem, referring to the generated data lacking diversity, can be exacerbated by data heterogene-ity. Finally, complexity analysis reveals that FMG requires a smaller communication overhead than a state-of-the-art method. Empirical results on a real-world traffic dataset demonstrate that the FMG can be trained to synthesize data accurately within a few training iterations, even under heterogeneous data settings. Moreover, the FMG model exhibits a higher diversity in the categories of synthesized samples compared to the state-of-the-art method.

There is still lacking a simple and comprehensive framework to model and optimize the overall energy consumption of an FEI network, especially in heterogeneous scenarios. This paper proposes a comprehensive framework to characterize the overall energy consumption of FEI networks. The computation and communication overhead as well as the number of coordination rounds required to train a satisfactory model are analytically modeled and evaluated. We investigate and compare the energy consumption of FEI networks with two popular distributed algorithmic implementations: FedAvg and FedMeta. We observe that although FedMeta consumes more energy than FedAvg in each single coordination round, the overall energy consumption of FedMeta is much lower than that of FedAvg. Finally, we evaluate the energy consumption of both algorithms based on a hardware prototype. Numerical results show that the overall energy consumption of FedMeta is 77.9% less than that of FedAvg.

Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security/privacy concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over$20\%$improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.

Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristics that cannot be removed from model training, we develop an analytical expression to characterize the impact of both staleness effect of asynchronous coordination and straggler effect of FL on the time consumption of TS-FL-ASC. We propose a load forwarding-based solution that allows a slow edge server to offload part of its training samples to trusted edge servers with higher processing capability. We develop a hardware prototype to evaluate the model training time of a heterogeneous FEI system. Experimental results show that our proposed TS-FL-SC and TS-FL-ASC can provide up to 63% and 28% of reduction, in the overall model training time, respectively, compared with traditional FL solutions.

Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventionalFedAvgapproach when the straggling probability varies between 20% and 80%. Moreover, the proposed framework can improve the MSP’s profit up to 2.84 times compared with other baseline FL approaches without MEN-assisted training.

This article proposes a novel framework that utilizes an over-the-air Brain-Computer Interface (BCI) to learn Metaverse users’ expectations. By interpreting users’ brain activities, our framework can optimize physical resources and enhance Quality-of-Experience (QoE) for users. To achieve this, we leverage a Wireless Edge Server (WES) to process electroencephalography (EEG) signals via uplink wireless channels, thus eliminating the computational burden for Metaverse users’ devices. As a result, the WES can learn human behaviors, adapt system configurations, and allocate radio resources to tailor personalized user settings. Despite the potential of BCI, the inherent noisy wireless channels and uncertainty of the EEG signals make the related resource allocation and learning problems especially challenging. We formulate the joint learning and resource allocation problem as a mixed integer programming problem. Our solution involves two algorithms: a hybrid learning algorithm and a meta-learning algorithm. The hybrid learning algorithm can effectively find the solution for the formulated problem. Specifically, the meta-learning algorithm can further exploit the neurodiversity of the EEG signals across multiple users, leading to higher classification accuracy. Extensive simulation results with real-world BCI datasets show the effectiveness of our framework with low latency and high EEG signal classification accuracy.

Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users’ quality-of-experience (QoE). Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal. This paper investigates the implicit semantic-aware communication in which the hidden information, e.g., hidden relations, concepts and implicit reasoning mechanisms of users, that cannot be directly observed from the source signal must be recognized and interpreted by the intended users. To this end, a novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A graph-inspired structure is first developed to represent the complete semantics, including both explicit and implicit, of a message. A projection-based semantic encoder is then proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. To enable the destination user to learn and imitate the implicit semantic reasoning process of source user, a generative adversarial imitation learning-based solution, called G-RML, is proposed. Different from existing communication solutions, the source user in G-RML does not focus only on sending as much of the useful messages as possible; but, instead, it tries to guide the destination user to learn a reasoning mechanism to map any observed explicit semantics to the corresponding implicit semantics that are most relevant to the semantic meaning. By applying G-RML, we prove that the destination user can accurately imitate the reasoning process of the source user and automatically generate a set of implicit reasoning paths following the same probability distribution as the expert paths. Compared to the existing solutions, our proposed G-RML requires much less communication and computational resources and scales well to the scenarios involving the communication of rich semantic meanings consisting of a large number of concepts and relations. Numerical results show that the proposed solution achieves up to 92% accuracy of implicit meaning interpretation.

Automatic Modulation Classification (AMC) is a crucial task in the field of wireless communication, allowing for the identification of the modulation scheme of a received radio signal without prior knowledge of the communication system. Recently, AMC approaches based on Deep Learning (DL) have achieved outstanding results. However, the majority of current DL-based AMC methods face challenges in achieving high recognition accuracy while remaining computationally efficient. Some researchers have designed autoencoder-based models to generate low-dimensional temporal feature embeddings of the radio signal, thereby reducing the number of model parameters while maintaining high performance in recognizing modulation formats. However, when further improving AMC performance via learning low-dimensional spatial-temporal feature representations, traditional autoencoder models require both a convolutional decoder and an LSTM decoder to reconstruct temporal and spatial features separately, which unavoidably raises the model parameters. In this paper, we propose a spatiotemporal feature sharing reconstructing network (STARNet) to simultaneously extract low-dimensional spatial and temporal feature representations of radio signals using a single autoencoder structure, thereby reducing the number of model parameters and improving AMC performance. Additionally, we construct a Hybrid Attentive Ghost (HA-Ghost) to automatically extract discriminative radio signal spatial information according to signal reconstruction performance. Extensive experiments on benchmark datasets demonstrate that the proposed STARNet achieves an average modulation classification accuracy of 63.64%, outperforming previous state-of-the-art models. Despite extracting more types of features, STARNet has only 14,860 parameters, which is smaller than existing spatiotemporal autoencoder-based methods.

Open radio access network (O-RAN) slicing allows the flexible control of network components and resources to satisfy the ever increasing demand of mobile applications. To optimize service provisioning, efficient management of limited radio resources is challenging due to the orchestration among network slices in the long-timescale and the slice configurations according to the mobile user (MU) statistics in the short-timescale. In this paper, we first propose a novel meta Markov decision process framework to mathematically formulate the problem of two-timescale radio resource management (RRM) in O-RAN slicing. The original RRM problem is then decoupled into a long-timescale master problem and a short-timescale subproblem, which are solved by a hierarchical reinforcement learning (RL) mechanism. Our proposed hierarchical RL mechanism includes a deep RL algorithm, solving the optimal long-timescale RRM policy, and a linear-decomposition based meta-RL algorithm, solving the optimal short-timescale RRM policy. Numerical experiments verify the theoretical analysis and show that our proposed hierarchical RL mechanism outperforms the most representative state-of-the-art baselines.

Semantic communication is a novel communication paradigm that focuses on recognizing and delivering the desired meaning of messages to the destination users. Most existing works in this area focus on delivering explicit semantics, labels or signal features that can be directly identified from the source signals. In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user. We develop a novel adversarial learning-based implicit semantic-aware communication (iSAC) architecture in which the source user, instead of maximizing the total amount of information transmitted to the channel, aims to help the recipient learn an inference rule that can automatically generate implicit semantics based on limited clue information. We prove that by applying iSAC, the destination user can always learn an inference rule that matches the true inference rule of the source messages. Experimental results show that the proposed iSAC can offer up to a 19.69 dB improvement over existing non-inferential communication solutions, in terms of symbol error rate at the destination user.

Semantic communication has attracted significant interest recently due to its capability to meet the fast growing demand on user-defined and human-oriented communication services such as holographic communications, eXtended reality (XR), and human-to-machine interactions. Unfortunately, recent study suggests that the traditional Shannon information theory, focusing mainly on delivering semantic-agnostic symbols, will not be sufficient to investigate the semantic-level perceptual quality of the recovered messages at the receiver. In this paper, we study the achievable data rate of semantic communication under the symbol distortion and semantic perception constraints. Motivated by the fact that the semantic information generally involves rich intrinsic knowledge that cannot always be directly observed by the encoder, we consider a semantic information source that can only be indirectly sensed by the encoder. Both encoder and decoder can access to various types of side information that may be closely related to the user's communication preference. We derive the achievable region that characterizes the tradeoff among the data rate, symbol distortion, and semantic perception, which is then theoretically proved to be achievable by a stochastic coding scheme. We derive a closed-form achievable rate for binary semantic information source under any given distortion and perception constraints. We observe that there exists cases that the receiver can directly infer the semantic information source satisfying certain distortion and perception constraints without requiring any data communication from the transmitter. Experimental results based on the image semantic source signal have been presented to verify our theoretical observations.

Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. However, data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. Motivated by the observation that signals recorded by wireless receivers are closely related to a set of physical-layer semantic features, in this paper we propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data. We develop a novel physical-layer semantic-aware network (pSAN) framework to characterize the correlation between physical-layer semantic features and the sensing data distributions across different receivers. We then propose a pSAN-based zero-shot learning solution in which each receiver can obtain a location-specific gesture recognition model by directly aggregating the already constructed models of other receivers. We theoretically prove that models obtained by our proposed solution can approach the optimal model without requiring any local model training. Experimental results once again verify that the accuracy of models derived by our proposed solution matches that of the models trained by the real labeled data based on supervised learning approach.

Semantic communication has recently attracted significant interest from both industry and academia due to its potential to transform the existing data-focused communication architecture towards a more generally intelligent and goal-oriented semantic-aware networking system. Despite its promising potential, semantic communications and semantic-aware networking are still in their infancy. Most existing works focus on transporting and delivering the explicit semantic information, e.g., labels or features of objects, that can be directly identified from the source signal. The original definition of semantics as well as recent results in cognitive neuroscience suggest that it is the implicit semantic information, in particular the hidden relations connecting different concepts and feature items that play the fundamental role in recognizing, communicating, and delivering the real semantic meanings of messages. Motivated by this observation, we propose a novel reasoning-based implicit semantic-aware communication network architecture that allows destination users to directly learn a reasoning mechanism that can automatically generate complex implicit semantic information based on a limited clue information sent by the source users. Our proposed architecture can be implemented in a multi-tier cloud/edge computing networks in which multiple tiers of cloud data center (CDC) and edge servers can collaborate and support efficient semantic encoding, decoding, and implicit semantic interpretation for multiple end-users. We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users. We model the semantic reasoning process as a reinforcement learning process and then propose an imitation-based semantic reasoning mechanism learning (iRML) solution to learning a reasoning policy that imitates the inference behavior of the source user. A federated graph convolutional network (GCN)-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized semantic message samples. Extensive experiments have been conducted based on real-world datasets to evaluate the performance of our proposed architecture. Numerical results confirm that iRML offers up to 25.8 dB improvement on the semantic symbol error rate, compared to the semantic-irrelevant communication solutions.

We address the problem of detecting misbehavior in the coexistence etiquette between LTE and Wi-Fi systems operating in the 5GHz U-NII unlicensed bands. We define selfish misbehavior strategies for the LTE that can yield an unfair share of the spectrum resources. Such strategies are based on manipulating the operational parameters of the LTE-LAA standard, namely the backoff mechanism, the traffic class parameters, the clear channel access (CCA) threshold, and others. Prior methods for detecting misbehavior in homogeneous settings are not applicable in a spectrum sharing scenario because the devices of one system cannot decode the transmissions of another. We develop implicit sensing techniques that can accurately estimate the operational parameters of LTE transmissions under various topological scenarios andwithout decoding.These techniques apply correlation-based signal detection to infer the required information. Our techniques are validated through experiments on a USRP testbed. We further apply a statistical inference framework for determining deviations of the LTE behavior from the coexistence etiquette. By characterizing the detection and false alarm probabilities, we show that our framework yields high detection accuracy at a very low false alarm rate. Although our methods focus on detecting misbehavior of the LTE system, they can be generalized to detect Wi-Fi misbehavior and to other coexistence scenarios.

In this article, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain's security and performance.

This paper analyzes the fundamental limit of the strategic semantic communication problem in which a transmitter obtains a limited number of indirect observations of an intrinsic semantic information source and can then influence the receiver’s decoding by sending a limited number of messages over an imperfect channel. The transmitter and the receiver can have different distortion measures and can make rational decisions about their encoding and decoding strategies, respectively. The decoder can also have some side information (e.g., background knowledge and/or information obtained from previous communications) about the semantic source to assist its interpretation of the semantic information. We focus particularly on the case that the transmitter can commit to an encoding strategy and study the impact of the strategic decision making on the rate distortion of semantic communication. Three equilibrium solution concepts including the optimal Stackelberg equilibrium, robust Stackelberg equilibrium, as well as Nash equilibrium are studied and compared. The optimal encoding and decoding strategy profiles under various equilibrium solutions are derived. We prove that committing to an encoding strategy cannot always bring benefit to the encoder. We provide a feasible condition under which committing to an encoding strategy can always reduce the distortion of semantic communication. We consider an example with a dictionary-based semantic information source to verify our observation.

Edge computing is an indispensable technology that overcomes delay limitations of cloud computing. In edge computing, computational resources are deployed at the network edge, and computational tasks and data of end terminals can be efficiently processed by edge nodes. Considering the computational resource limitations of edge nodes, collaborative edge computing integrates computational resources of edge nodes and provides more efficient computing services for end terminals. This article considers a computation offloading problem in collaborative edge computing networks, where computation offloading and resource allocation are optimized by means of a collaborative load shedding approach: a terminal can offload a computing task to an edge node, which either can process the task with its computing resource or further offload the task to other edge nodes. Long-term objectives and long-term constraints are considered, and Lyapunov optimization is applied to convert the original nonconvex computation offloading problem into a second problem that approximate the original problem and it is still nonconvex but has a special structure, which gives rise to a new distributed algorithm that optimally solves the second problem. Finally, the performance and provable bound of the distributed algorithm is theoretically analyzed. Numerical results demonstrate that the distributed algorithm can achieve a guaranteed long-term performance, and also demonstrate the improvement in performance achieved over the case of computation offloading without collaborating edge nodes.

Harvesting wireless power to energize miniature devices has been envisioned as a promising solution to sustain future-generation energy-sensitive networks, e.g., Internet-of-Things systems. However, due to the limited computing and communication capabilities, wirelessly powered networks (WPNs) may be incapable of employing complex security practices, e.g., encryption, which may incur considerable computation and communication overheads. This challenge makes securing energy harvesting communications an arduous task and, thus, limits the use of WPNs in many high-security applications. In this context, security at the physical layer (PHY) that exploits the intrinsic properties of the wireless medium to achieve secure communication has emerged as an alternative paradigm. This article first introduces the fundamental principles of primary PHY attacks, covering jamming, eavesdropping, and detection of covert, and then presents an overview of the prevalent countermeasures to secure both active and passive communications in WPNs. Furthermore, a number of open research issues are identified to inspire possible future research.

Mobile service providers (MSPs) are particularly vulnerable to roaming frauds, especially ones that exploit the long delay in the data exchange process of the contemporary roaming management systems, causing multi-billion dollars loss each year. In this paper, we introduce BlockRoam, a novel blockchain-based roaming management system that provides an efficient data exchange platform among MSPs and mobile subscribers. Utilizing the Proof-of-Stake (PoS) consensus mechanism and smart contracts, BlockRoam can significantly shorten the information exchanging delay, thereby addressing the roaming fraud problems. Through intensive analysis, we show that the security and performance of such PoS-based blockchain network can be further enhanced by incentivizing more users (e.g., subscribers) to participate in the network. Moreover, users in such networks often join stake pools (e.g., formed by MSPs) to increase their profits. Therefore, we develop an economic model based on Stackelberg game to jointly maximize the profits of the network users and the stake pool, thereby encouraging user participation. We also propose an effective method to guarantee the uniqueness of this game's equilibrium. The performance evaluations show that the proposed economic model helps the MSPs to earn additional profits, attracts more investment to the blockchain network, and enhances the network's security and performance.

Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing and workload processing latencies associated with fog computing, ignoring the fact that wireless access latency can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month measurement study of the wireless access latency between connected vehicles and a fog/cloud computing system supported by commercially available LTE networks. We proposeAdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement a fog/cloud infrastructure. AdaptiveFog's main objective is to maximize theservice confidence level, defined as the probability that the latency of a given service type is below some threshold. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two scenarios based on finite- and infinite-horizon optimization of short-term and long-term confidence are investigated. For each scenario, a simple threshold policy based on weighted K-R distance is proposed and proved to maximize the latency confidence for smart vehicles. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30 to 50 percent improvement in the confidence levels of fog and cloud latencies, respectively.

This paper studies a multi-Intelligent Reflecting Surfaces (IRSs)-assisted wireless network consisting of multiple base stations (BSs) serving a set of mobile users. We focus on the IRS-BS association problem in which multiple BSs compete with each other for controlling the phase shifts of a limited number of IRSs to maximize the long-term downlink data rate for the associated users. We propose MDLBI, a Multi-agent Deep Reinforcement Learning-based BS-IRS association scheme that optimizes the BS-IRS association as well as the phase-shift of each IRS when being associated with different BSs. MDLBI does not require information exchanging among BSs. Simulation results show that MDLBI achieves significant performance improvement and is scalable for large networking systems.

The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains. In this paper, a graph-based framework, called SMART, is proposed to model and keep track of the spatial and temporal statistics of vehicle-to-infrastructure (V2I) communication latency across a large geographical area. SMART first formulates the spatio-temporal performance of a vehicular network as a graph in which each vertex corresponds to a subregion consisting of a set of neighboring location points with similar statistical features of V2I latency and each edge represents the spatio-correlation between latency statistics of two connected vertices. Motivated by the observation that the complete temporal and spatial latency performance of a vehicular network can be reconstructed from a limited number of vertices and edge relations, we develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm in order to capture the spatial and temporal statistic of feature map pf latency performance for a large-scale vehicular network. Extensive simulations have been conducted based on a five-month latency measurement study on a commercial LTE network. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and reconstructing the latency performance of large vehicular networks.

With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges. This paper introduces a novel framework, Federated Generative Adversarial Networks and Automatic Classification (FGAN-AC), which integrates decentralized data synthesizing with traffic classification. FGAN-AC is able to synthesize and classify multiple types of service data traffic from decentralized local datasets without requiring a large volume of manually labeled dataset or causing any data leakage. Two types of data synthesizing approaches have been proposed and compared: computation-efficient FGAN (FGAN-I) and communication-efficient FGAN (FGAN-II). The former only implements a single CNN model for processing each local dataset and the later only requires coordination of intermediate model training parameters. An automatic data classification and model updating framework has been proposed to automatically identify unknown traffic from the synthesized data samples and create new pseudo-labels for model training. Numerical results show that our proposed framework has the ability to synthesize highly mixed service data traffic and can significantly improve the traffic classification performance compared to existing solutions.

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. NIS is a bargaining mechanism whose terms and conditions must be carefully determined based on mutual benefits in a market with uncertainties. In this work, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator with limited resources and uncertain demands, and a more resourceful operator with excessive capacity. The backup reservation agreement requires the local operator (say, operator A) to reserve a certain amount of resources (e.g., spectrum) for future sharing from the resource-owning operator (say, operator B). In return, operator B guarantees availability of its reserved resources to meet the need of operator A. We characterize the bargaining between the operators in terms of the optimal reservation prices and quantities with and without consideration of their competitions in market share, respectively. The conditions under which competing operators have incentive to cooperate are explored. The impact of competition intensity and redundant capacity on performance under backup reservation are also investigated. Our study shows that NIS through backup reservation improves both resource utilization and profits of operators, with the potential to support higher target service levels for end users. We also find that, under certain conditions, operator B may still have the incentive to share its resources even at the risk of impinging on its own users.

Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connected to cloud/fog servers via a commercial LTE network. We establish empirical models for AoI in connected vehicles and investigate the impact of major factors on the performance of AoI. We also propose a Deep Q-Learning Network (DQN)-based algorithm to decide the optimal driving route for each connected vehicle with maximized confidence level. Numerical results show that the proposed approach can lead to a significant improvement on the AoI confidence for various types of services supported.

Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator, which can be deployed between base stations and fog nodes to coordi- nate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that DistADMM-PVS minimizes the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that DistADMM-PVS converges much faster than some other existing algorithms. In addition, the joint network slicing with both bandwidth and computational resources offers around 15% overall latency reduction compared to network slicing with only a single resource.

Dynamic spectrum access enables opportunistic users (OUs) to access underutilized licensed bands by querying spectrum databases. However, the operational details of the incumbent users may leak to OUs during the query process. Privacy and exclusion zones have been proposed as effective countermeasures to protect the IUs' privacy, while also managing interference. In the case of multiple heterogeneous coexisting IUs, there is an inherent tradeoff between their achieved throughput, which is controlled by the received interference, and the utility provided to OUs, under a fixed privacy constraint. In this paper, we address the problem of maximizing the utility of rational IUs, defined as the weighted sum between the IUs' capacity and compensation from allowing OUs' opportunistic access while meeting the individual IUs' privacy constraints. We formulate the interaction between the heterogeneous IUs as a non-cooperative continuous game and derive the Nash equilibrium that maximizes the utility of each IU. Our simulations show that the NE solution improves the individual utilities of the IUs compared to a joint optimization approach, where the sum of the utilities is maximized while providing more fairness to the IUs.

Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems. The quality of the molded results rely heavily on the number and quality of samples which can vary significantly due to the sensor deployment density as well as traffic volume and density. This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive spatio-temporal of wireless access latency of a connected vehicles across a wide geographical area. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Variational Autoencoder (VAE), a deep generative model, to create latency samples with similar probability distribution with the original samples. Finally, LaMI establishes the empirical PDF of latency performance and maps the PDFs into the confidence levels of different vehicular service requirements. Extensive performance evaluation has been conducted using the real traces collected in a commercial LTE network in a university campus. Simulation results show that our proposed model can significantly improve the accuracy of latency modeling especially compared to existing popular solutions such as interpolation and nearest neighbor-based methods.

This paper studies the potential performance improvement that can be achieved by enabling multi-operator wireless connectivity for cloud/fog computing-connected vehicular systems. Mobile network operator (MNO) selection and switching problem is formulated by jointly considering switching cost, quality-of-service (QoS) variations between MNOs, and the different prices that can be charged by different MNOs as well as cloud and fog servers. A double deep Q network (DQN) based switching policy is proposed and proved to be able to minimize the long-term average cost of each vehicle with guaranteed latency and reliability performance. The performance of the proposed approach is evaluated using the dataset collected in a commercially available city-wide LTE network. Simulation results show that our proposed policy can significantly reduce the cost paid by each fog/cloud-connected vehicle with guaranteed latency services.

The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the DCOs and the SSs are regarded as the leaders and followers, respectively. In the proposed game, each SS selects its serving DCO with preferred price and purchases the optimal amount of resources for the SS's computing requirements. Based on the responses of the SSs' and the other DCOs', the DCOs decide their resource prices so as to receive the highest profit. When the coordination among DCOs is weak, we consider all DCOs are noncooperative with each other, and propose a sub-gradient algorithm for the DCOs to approach a sub-optimal solution of the game. When all DCOs are sufficiently coordinated, we formulate a coalition game among all DCOs and apply Kalai-Smorodinsky bargaining as a resource division approach to achieve high utilities. Both solutions constitute the Stackelberg Equilibrium. The simulation results verify the performance improvement provided by our proposed approaches.

Edge computing is a promising technology that offers data analysis and computing for Internet of Things (IoT) services at the network edge. It has the potential to significantly reduce the latency and improve the reliability of IoT services by allowing computation workloads and local data generated by IoT devices to be offloaded to edge nodes. This paper aims to develop algorithms for efficient provision of both job assignment and resource allocation for edge computing networks. The main objective is to minimize the long-term average of the response time delay subject to constraints on computation resources and power consumption. We apply a drift-plus-penalty based Lyapunov optimization approach to convert the original problem into an upper bound optimization problem. We then relax the latter to a convex optimization problem. Finally, a distributed algorithm based on branch-and-bound approach is provided and the gap between the distributed algorithm solution and the optimal solution of the original problem is theoretically analyzed. Numerical results based on extensive experiments have demonstrated that our distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic.

In this paper, we consider the scenario in which mobile network operators (MNOs) share network infrastructure for operating 5G new radio (NR) services in unlicensed bands, whereby they reduce their deployment cost and extend their service coverage. Conserving privacy of MNOs' users, maintaining fairness with coexisting technologies such as Wi-Fi, and reducing communication overhead between MNOs are among top challenges limiting the feasibility and success of this sharing paradigm. To resolve above issues, we present MatchMaker, a novel framework for joint network infrastructure and unlicensed spectrum sharing among MNOs. MatchMaker extends the 3GPP's infrastructure sharing architecture, originally introduced for licensed bands, to have privacy-conserving protocols for managing the shared infrastructure. We also propose a novel privacy-conserving algorithm for channel assignment among MNOs. Although achieving an optimal channel assignment for MNOs over unlicensed bands dictates having global knowledge about MNOs' network conditions and their interference zones, our channel assignment algorithm does not require such global knowledge and maximizes the cross-technology fairness for the coexisting systems. We let the manager, controlling the shared infrastructure, estimate potential interference among MNOs and Wi-Fi systems by asking MNOs to propose their preferred channel assignment and monitoring their average contention delay overtime. The manager only accepts/rejects MNOs' proposals and builds contention graph between all colocated devices. Our results show that MatchMaker achieves fairness up to 90% of the optimal alpha-fairness-based channel assignment while still preserving MNOs' privacy.

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. To collaborate with competitors, NIS is a bargain whose terms and conditions need to be carefully determined to guarantee profitability in a market with uncertainties. In this work, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator of limited resources and uncertain demands, and one resourceful operator with potentially redundant capacity. The backup reservation agreement requires the local operator (say, operator A) to pay a fixed reservation fee to the resource-owning operator (say, operator B) at fixed time intervals. In return, the operator B guarantees availability of its resource (e.g., spectrum) up to a predetermined level. In such a way, a certain amount of backup resource capacity is reserved for future use under high traffic demand. We characterize the bargaining between the operators in terms of the optimal reservation prices and resource reservation quantities w/o considerations of the competitions between operators in market share. The conditions under which the competitive operators will cooperate are explored. The impacts of competition intensity, redundant capacity, and demand uncertainty on performance under backup reservation are also investigated. Our study shows that NIS through backup reservation leads to both higher resource utilization and profits for operators, as well as higher service levels for end users. We also find that, under certain conditions, operator B will share its resources with operator A even at the risk of impinging on its own users, and the impact of competition intensity on the sharing decisions is highly dependent on the amount of potential redundant capacity.

Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and optimizing the queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the end-to-end service latency. This motivates the work in this paper, where we report a five-month urban measurement study of the wireless access latency between a connected vehicle and a fog computing system supported by commercially available multi-operator LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE operators that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that the tolerable latency threshold for each supported type of service can be guaranteed. AdaptiveFog has been implemented on a smart phone app, running on a moving vehicle. The app periodically measures the round-trip time between the vehicle and fog/cloud servers. An empirical spatial statistic model is established to characterize the spatial variation of the latency across the main driving routes of the city. To quantify the performance difference between different LTE networks, we introduce the weighted Kantorovich-Rubinstein (K-R) distance. An optimal policy is derived for the vehicle to dynamically switch between LTE operators' networks while driving. Extensive analysis and simulation are performed based on our latency measurement dataset. Our results show that AdaptiveFog achieves around 30% and 50% improvement in the confidence level of fog and cloud latency, respectively.

In this paper, content caching is considered at the edge of the network with an objective of offloading recurrent traffic on the capacity-stringent backhaul links to the vicinity of end users. A radio access network equipped with edge servers is considered for caching contents of various sizes, based on which problems of maximizing the edge cache-hit-ratio and minimizing the average content-provisioning cost are respectively formulated. To solve the underlying 0-1 Knapsack problem, a size-weighted popularity (SWP)-based caching framework is proposed, where both content popularity and content size are taken into account when determining the contents to be cached. Depending on the available knowledge and the manner in which the contents are pre-fetched and cached at the edge servers, two algorithms: proactive and reactive, are proposed for the implementation of SWP-based caching. Simulation results are presented to evaluate the performance of our proposed algorithms. We observe a fundamental tradeoff between the average content-provisioning cost and the cache-hit-ratio, and the proactive algorithm outperforms the reactive algorithm.

Network slicing has been considered as a key enabling technology for 5G due to its ability to customize and "slice" a common resource to support diverse services and verticals. This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can cooperate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For the licensed band slicing, we propose the inter-operator spectrum aggregation method which allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Since all MNOs enjoy equal rights to access unlicensed bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a modified back-of-the-envelop method for the MNOs to evaluate their value of rights when coexisting with other wireless technologies. We develop a network slicing game based on the overlapping coalition formation game to investigate the possible cooperation between MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To evaluate the practical performance of our proposed framework, we develop a C++-based discrete-event simulator and simulate a possible implementation of our proposed framework over 400 base station locations deployed by two primary cellular operators in the city of Dublin. Numerical results show that our proposed framework can almost double the capacity for all supported services for each operator under certain conditions.

In this paper, we consider the fair coexistence between LTE and Wi-Fi systems in unlicensed bands. We focus on the misbehavior opportunities that stem from the heterogeneity of the coexisting systems and the lack of explicit coordination mechanisms. We show that a selfishly behaving LTE can gain an unfair share of the spectrum resources through the manipulation of the parameters defined in the LAA-LTE standard, including the manipulation of the backoff mechanism of LAA, the traffic class, the clear channel assignment threshold and others. We develop a detection mechanism for the Wi-Fi system that can identify a misbehaving LTE system. Our mechanism advances the state of the art by providing an accurate monitoring method of the LTE behavior under various topological scenarios, without explicit cross-system coordination. Deviations from the expected behavior are determined by computing the statistical distance between the protocol-specified and estimated distributions of the LAA-LTE protocol parameters. We analytically characterize the detection and false alarm probabilities and show that our detector yields high detection accuracy at very low false alarm rate, for a wise choice of statistical parameters.

Network slicing is one of the key enabling technologies for 5G due to its ability to customize and “slice” a common resource to support diverse services and verticals. This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can coordinate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For licensed band slicing, we propose an inter-operator spectrum aggregation method that allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Because all MNOs enjoy equal rights to access these bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a modified back-of-the-envelope method for MNOs to evaluate their Value-of-Rights when coexisting with other wireless technologies. A network slicing game based on the overlapping coalition formation game is formulated to investigate the possible cooperation among MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To implement our proposed framework without requiring MNOs to reveal private information to other MNOs, we develop a distributed algorithm called distributed alternating direction method of multipliers with partially variable splitting. Performance evaluation of our proposed framework is provided using a discrete-event simulator that is driven by real MNO deployment scenarios based on over 400 base station locations deployed by two primary cellular operators in the city of Dublin. Numerical results show that our proposed frameworks can almost double the capacity for all supported services for each MNO in an urban setting.

Tactile Internet is an emerging concept that focuses on supporting high-fidelity, ultra-responsive, and widely available human-to-machine interactions. To reduce the transmission latency and alleviate Internet congestion, fog computing has been advocated as an important component of the Tactile Internet. In this paper, we focus on an energy-efficient design of fog computing networks that support low-latency Tactile Internet applications. We investigate two performance metrics: Service response time of end-users and power usage efficiency of fog nodes. We quantify the fundamental tradeoff between these two metrics and then extend our analysis to fog computing networks involving cooperation between fog nodes. We introduce a novel cooperative fog computing concept, referred to as offload forwarding, in which a set of fog nodes with different computing and energy resources can cooperate with each other. The objective of this cooperation is to balance the workload processed by different fog nodes, further reduce the service response time, and improve the efficiency of power usage. We develop a distributed optimization framework based on dual decomposition to achieve the optimal tradeoff. Our framework does not require fog nodes to disclose their private information nor conduct back-and-forth negotiations with each other. Two distributed optimization algorithms are proposed. One is based on the subgradient method with dual decomposition and the other is based on distributed alternating direction method of multipliers via variable splitting. We prove that both algorithms can achieve the optimal workload allocation that minimizes the response time under the given power efficiency constraints of fog nodes. Finally, to evaluate the performance of our proposed concept, we simulate a possible implementation of a city-wide self-driving bus system supported by fog computing in the city of Dublin. The fog computing network topology is set based on a real cellular network infrastructure involving 200 base stations deployed by a major cellular operator in Ireland. Numerical results show that our proposed framework can balance the power usage efficiency among fog nodes and reduce the service latency for users by around 50% in urban scenarios.

This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area. Each node relies on harvested energy from the surrounding environment to provide computational services to local users. We propose the concept of dynamic network slicing, in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain quality-of-service guarantees. The resources allocated to each slice can be dynamically adjusted according to service demands and energy availability. A stochastic overlapping coalition-formation game is developed to investigate the distributed cooperation and joint network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes. We observe that the overall processing capacity of the fog computing network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes. An algorithm based on a belief-state partially observable Markov decision process is proposed to achieve the optimal resource slicing structure among all fog nodes. We describe how to implement our proposed dynamic network slicing within the 3GPP network sharing architecture and evaluate the performance of our proposed framework using the real base station (BS) location data of a real cellular system with over 200 BSs deployed in the city of Dublin. Our numerical results show that our framework can significantly improve the workload processing capability of fog computing networks. In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios.

How to allocate the limited wireless resource in dense radio access networks (RANs) remains challenging. By leveraging a software-defined control plane, the independent base stations (BSs) are virtualized as a centralized network controller (CNC). Such virtualization decouples the CNC from the wireless service providers (WSPs). We investigate a virtualized RAN, where the CNC auctions channels at the beginning of scheduling slots to the mobile terminals (MTs) based on bids from their subscribing WSPs. Each WSP aims at maximizing the expected long-term payoff from bidding channels to satisfy the MTs for transmitting packets. We formulate the problem as a stochastic game, where the channel auction and packet scheduling decisions of a WSP depend on the state of network and the control policies of its competitors. To approach the equilibrium solution, an abstract stochastic game is proposed with bounded regret. The decision making process of each WSP is modeled as a Markov decision process (MDP). To address the signalling overhead and computational complexity issues, we decompose the MDP into a series of single-agent MDPs with reduced state spaces, and derive an online localized algorithm to learn the state value functions. Our results show significant performance improvements in terms of per-MT average utility.

This paper studies the workload offloading problem for fog computing networks in which a set of fog nodes can offload part or all the workload originally targeted to the cloud data centers to further improve the quality-of-experience (QoE) of users. We investigate two performance metrics for fog computing networks: users' QoE and fog nodes' power efficiency. We observe a fundamental tradeoff between these two metrics for fog computing networks. We then consider cooperative fog computing networks in which multiple fog nodes can help each other to jointly offload workload from cloud data centers. We propose a novel cooperation strategy referred to as offload forwarding, in which each fog node, instead of always relying on cloud data centers to process its unprocessed workload, can also forward part or all of its unprocessed workload to its neighboring fog nodes to further improve the QoE of its users. A distributed optimization algorithm based on distributed alternating direction method of multipliers (ADMM) via variable splitting is proposed to achieve the optimal workload allocation solution that maximizes users' QoE under the given power efficiency. We consider a fog computing platform that is supported by a wireless infrastructure as a case study to verify the performance of our proposed framework. Numerical results show that our proposed approach significantly improves the performance of fog computing networks.

Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.

It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs' interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

This paper considers machine-to-machine (M2M) communication for wireless-powered Internet-of-Things (IoT) based networking systems. Motivated by the observation that transmitting signals generally requires more energy than receiving signals for most IoT-based systems, we study a special wireless-powered M2M communication system in which the receiver can send its surplus energy to the transmitter. We propose a framework of wireless powered full-duplex M2M communication (WP-FD-M2M) in which the energy transfer from the receiver to the transmitter and the data transmission from the transmitter to the receiver take place at the same time over the same frequency. We establish a stochastic game-based model, referred to as the M2M game, to characterize the interaction between autonomous M2M transmitter and receiver. We prove that, if the transmitter and receiver can sequentially optimize their data transmission and energy transfer based on the Markov strategy, it is possible to achieve the maximum long-term performance for M2M communication without a centralized controller or coordination between the transmitter and receiver. Numerical results show that our proposed approach can significantly improve the performance for M2M communication under various situations.

With the increasing popularity of data services and applications, data center networks have been introduced to serve users in a centralized fashion. Furthermore, the flexibility of the data service subscribers' (DSSs') requirements motivates data center virtualization so as to optimize the resource allocation among all DSSs. However, as the massive data centers are usually far away from the DSSs, the quality of services are severely affected for delay-sensitive DSSs. Accordingly, fog computing is considered to solve the problem, where some virtualized edge data centers acting as fog nodes (FNs) are added in the network and help massive data center operators (MDCOs) serve DSSs. In this paper, we analyze the resource management problem in the multi-FN multi-MDCO, and multi-DSS networks. We model the network architecture with 3-layer model, where the FNs are in the upper layer, MDCOs in the middle layer, the DSSs in the bottom layer. The FNs first share computing resource with the MDCOs, and thus the MDCOs are able to serve their DSSs with low delay. Based on the model, we propose a hierarchical game, where the interaction between FNSs and MDCOs is regarded as a multi-leader multi-follower Stackelberg game, and the interactions between MDCOs and DSSs are regarded as the single-leader single-follower Stackelberg games. By making decisions distributively, all FNs, MDCOs, and DSSs receive high utilities. Simulation results show the correctness of the analysis and the performance improvement of the proposed strategies in the fog computing networks.

This paper studies carrier aggregation between multiple mobile network operators (MNOs), referred to as interoperator carrier aggregation (IO-CA). In IO-CA, each MNO can transmit on its own licensed spectrum and aggregate the spectrum licensed to other MNOs. We focus on the case that MNOs are partitioned and distributed into small groups, called IO-CA pairs, each of which consists of two MNOs that mutually agree to share their spectrum with each other. We model the IO-CA pairing problem between MNOs as a stable roommate market and derive a condition for which a stable matching structure among all MNOs exist. We propose an algorithm that achieves a stable matching if it exists. Otherwise, the algorithm results in a stable partition. For each IO-CA pair, we derive the optimal transmit power for each spectrum aggregator and establish a Stackelberg game model to analyze the interaction between the licensed subscribers and aggregators in the spectrum of each MNO. We derive the Stackelberg equilibrium of our proposed game and then develop a joint optimization algorithm that achieves the stable matching structure among MNOs as well as the optimal transmit powers for the aggregators and prices for the subscribers of each MNO.

This paper considers an energy harvesting communication system consisting of a communication link powered by the energy harvested from the natural environment and the energy wirelessly transferred from a remote dedicated energy source. We study three optimization approaches for this system: transmit power scheduling which allows the transmitter to sequentially optimize its transmit power, energy requesting which lets the transmitter to sequentially decide the amount of energy to be requested from the dedicated energy source, and a joint optimization approach for the transmitter to jointly decide the transmit power scheduling and energy requesting. We derive the optimal policy for each of the above approaches that can maximize the long-term average payoff of the communication link. We present numerical results to compare the performances of different optimizations under different settings and conditions.

Allowing cellular operators to offload data traffic to unlicensed spectrum has the potential to significantly increase the capacity of the cellular network systems. This paper considers the spectrum sharing among multiple cellular operators in the unlicensed spectrum. One of the main challenges for this system is how to control the interference between the cellular users and the unlicensed users in other networks, e.g., Wi-Fi, and the interference among cellular users of different operators. As such, we develop a hierarchical game where there is a Kalai-Smorodinsky bargaining game among leaders and a Stackelberg game between operators and mobile users (MU). Accordingly, multiple operators can negotiate with each other for the revenue obtained from the unlicensed spectrum and use a pricing mechanism to control the interference caused by each MU to other operators and users in other unlicensed networks. Simulation results show that our proposed strategy significantly increases the revenue and utility for both operators and MUs.

This paper studies energy harvesting communication systems in which a transmitter sends data packets using the energy harvested from the surrounding natural environment. In many practical situations, the uncertainty of the environment makes the transmitter difficult to keep track of the future change of the physical environment. In addition, the reward value of each successful transmission can be a random variable affected by some factors unknown to the transmitter. We propose a Bayesian reinforcement learning approach for the transmitter to learn the statistic features about the future evolution of the natural environment and probability distribution of the reward value from its previous experience. We derive the optimal policy for the transmitter to sequentially decide its transmit power and the number of transmit data packets to maximize the long-term expected reward. Numerical results show that our proposed algorithm can significantly improve the system performance even when the future environment change and reward value are uncertain for the transmitter.

This paper considers a cognitive radio network where the licensed network, referred to as the primary user (PU) network, consists of a hierarchical structure in which multiple operators coexist in the same coverage area where each of the operators controls an exclusive set of frequency sub-bands. Unlicensed users, referred to as the secondary users (SUs), first send their requests to the operators, and can only access the sub-bands controlled by the operators that accept their requests. SUs are selfish and cannot exchange private information with each other. We model the dynamic spectrum access (DSA) problem of the SUs as a Bayesian game, referred to as the DSA game. We model the PU network as a forest where the roots represent the operators and the leaves represent the operators' sub-bands. We propose a novel forest matching market to model the interaction between the SUs and the PU network. In this market, a set of SUs can be first matched to a set of operators and the SUs matched to the same operator can then be matched to the corresponding sub-bands. We propose a distributed algorithm that results in a stable forest matching structure, which coincides with the optimal Bayesian Nash equilibrium of the DSA game. We prove that the Bayesian hierarchical mechanism associated with our proposed algorithm incentivizes truth-telling by SUs. Our algorithm does not require each SU to know the preference and conflict-solving rule of the PU network or the payoffs and actions of other SUs, and the complexity of each iteration in the worst case is given by O(L2N2K) where L is the number of operators, N is the maximum number of sub-bands of each operator, and K is the number of SUs.

This paper studies energy-harvesting communication systems in which different energy-harvesting devices (EHDs) can harvest different amounts of energy and transmit different numbers of data packets in different time slots. We introduce a dynamic energy trading framework that allows the EHDs to transfer and trade their harvested energy with each other. The EHDs are divided into two groups: seller EHDs that can harvest more energy than they can use, and buyer EHDs that cannot harvest sufficient energy to support their required communication services. In the proposed framework, the role of each EHD as a seller EHD or a buyer EHD as well as the amount of energy that each EHD can buy or sell to others change over time. Each EHD cannot observe complete information regarding the harvested energy or the number of data packets transmitted by other EHDs. We introduce a simple energy trading scheduling protocol for the EHDs to discover their nearby EHDs and establish energy trading links with each other. We formulate a new game theoretic model called stochastic energy trading game to analyze the dynamic energy trading among EHDs in a stochastic environment. We derive an optimal energy-trading policy for each EHD to sequentially optimize its decisions. We prove that the proposed policy can achieve a stable and optimal sequence of matchings between buyer and seller EHDs. We present numerical results to compare our proposed energy trading policy with an existing transmit packet scheduling approach, under various network settings and conditions.

We consider the spectrum sharing problem between a set of device-to-device (D2D) links and multiple co-located cellular networks. Each cellular network is controlled by an operator which can provide service to a number of subscribers. Each D2D link can either access a sub-band occupied by a cellular subscriber or obtain an empty sub-band for its exclusive use. We introduce a new spectrum sharing mode for D2D communications in cellular networks by allowing two or more D2D links with exclusive use of sub-bands to share their sub-bands with each other without consulting the operators. We establish a new game theoretic model called Bayesian non-transferable utility overlapping coalition formation (BOCF) game. We show that our proposed game can be used to model and analyze the above spectrum sharing problem. However, we observe that the core of the BOCF game can be empty, and we derive a sufficient condition for which the core is non-empty. We propose a hierarchical matching algorithm which can detect whether the sufficient condition is satisfied and, if it is satisfied, achieve a stable and unique matching structure which coincides with the overlapping coalition agreement profile in the core of the BOCF game.

We consider cooperative energy harvesting communication networks in which a set of source-to-destination pairs competes for a limit number of relay nodes with energy harvesting ability. The performance of each source has been affected by two interactions: the interaction between the sources and relay nodes and the interaction among sources. We model the first interaction as a college admission market and then fits this market into a stochastic environment. We formulate an interactive partially observable Markov decision process (I-POMDP) to study the second interaction. We derive the optimal policy for the sources to sequentially optimize their decisions. Numerical results show that our proposed policy significantly improves the performance of sources.

Spectrum pooling has been shown to have a great potential to improve the spectrum utilization, especially when primary users (PUs) and secondary users (SUs) are allowed to utilize a common spectrum pool. This paper studies the joint optimization problem for a spectrum pooling system with both PUs and SUs. We develop a novel hierarchical game theoretic model which consists of an overlapped coalition formation game model to analyze the pricing cooperation/competition strategy among PUs and a non-cooperative game model to investigate the resource competition among SUs. These two game models are interrelated in a hierarchical game structure, in which we also study the interaction between SUs and PUs. Our model does not require SUs to have information about spectrum access scheduling of PUs. Furthermore, we propose a simple distributed joint optimization algorithm that can optimize the coalition formation of PUs as well as the sub-band allocation and transmit powers of SUs. To study different fairness criteria and their effects on the payoff divisions among PUs, we derive the optimal payoff division schemes of two popular fairness criteria, namely Nash bargaining solution and Shapley value fairness.

In this paper, we investigate the performance of the dynamic allocation of resources between separate cellular networks. We propose a Dynamic Internetworking Carrier Aggregation (DI-CA) framework which involves every network operator releasing some of its exclusive, but excess, spectrum to another network operator for a limited time. We derive the basic condition for which DI-CA can improve the performance for all the operators and then propose a distributed scheduling framework that uses coalition formation with uncertainty, in which each independent operator can decide whether or not DI-CA can improve its performance without having information regarding channel conditions or load experienced by other operators. We propose a distributed Bayesian coalition formation algorithm to approach a neighborhood of the Bayesian Nash equilibrium.

We propose a general framework to analyze incentives for user cooperation, and characterize the tradeoff between fairness and efficiency for cooperative networks. More specifically, we define the incentive region as a set of action profiles that provides cooperation benefits to all users and focus on the optimization of efficiency and fairness within this region. We introduce a linear resource allocation (LRA) scheme and show that most existing fairness measures can be converted to LRA with different linear coefficient vectors. We then propose the concept of strong price of fairness (SPoF) to study the network efficiency of the strong equilibrium. We show that both the SPoF and fairness measures are connected to the linear coefficient vector of LRA, which makes it possible to study the fairness and efficiency relationship. We then use the random access (RA) system as an example to show how to use the proposed framework to study a specific wireless network.

This paper considers spatial spectrum sharing-based carrier aggregation (SSS-CA) from a game theoretic perspective. In SSS-CA, a network operator can not only transmit on its own licensed spectrum but it can also access and aggregate the licensed spectrum of other operators on payment of a certain price. The difference between operators and the aggregators in each licensed spectrum makes this network heterogeneous. We first model the pairing problem between potential operators as a pairing game and then derive the condition for which both operators are incentivized to form an SSS-CA pair. We then introduce the power control game to derive the optimal transmit power of each spectrum aggregator. Finally, we consider the pricing optimization problem by forming a pricing adjustment game. We observe that these three problems are linked by the price function of the operators and hence can be jointly optimized by using a hierarchical game theoretic framework. We derive the Stackelberg equilibrium for the pricing and power joint optimization problem and present the numerical results to compare the performance improvement brought by our proposed joint optimization method.

This paper studies spatial spectrum sharing (SSS) based multi-user cognitive radio (CR) networks that allow secondary users (SU) to access the licensed spectrum as long as the interference powers of primary users (PU) to be lower than a certain threshold. Although recent results have shown that multi-hop relaying has a great potential on improving the performance of CR networks, finding effective methods to control and manage SUs to achieve the optimal performance is still a challenging problem. In this paper, we model CR networks as a non-cooperative game in which each SU obtains benefits through both spectrum sharing by paying prices to PUs and multi-hop relaying by paying price to nearby SUs. Optimal power allocation methods for SUs are investigated under different assumptions and pricing functions. The conditions under which the optimal Nash Equilibrium (NE) is obtained when all SUs use multi-hop relaying are discussed. Our results are extended into large multi-user CR networks with K source-to-destination pairs. Two distributed algorithms are proposed. The first one is a sub-gradient based power allocation algorithm in which SUs can iteratively adjust their transmit powers to approach the payoff of a NE. The other one is a Q-learning based relay selection algorithm which enables each SU to iteratively search for a NE-achieving relaying scheme.

This paper studies the power control problem for spectrum sharing based cognitive radio (CR) networks with multiple secondary source-to-destination (SD) pairs. A simple distributed algorithm is proposed for the secondary users (SUs) to iteratively adjust their transmit powers to improve the performance of the network. The proposed algorithm does not require each SU (or PU) to negotiate with other SUs (or PUs) during the communication. It is proved that the proposed algorithm can obtain a time average performance as good as that achieved when the Nash equilibrium (NE) is chosen in hindsight. More specifically, the average performance of CR networks will converge to an ε-Nash equilibrium at a rate of Tε= O (exp (1/ε)). A sub-optimal algorithm is also introduced to further improve the convergence rate to Tε'/log Tε'= O (1/ε'). Numerical results are presented to show the performance of the proposed algorithms under different settings.