Large artificial intelligence (AI) models have demonstrated impressive performance in a wide range of fields. Despite their versatility, adapting large AI models to specific downstream applications often requires fine-tuning on decentralized and privacy-sensitive data, posing significant challenges in resource-constrained wireless networks. In this paper, we propose a novel zeroth-order federated fine-tuning framework for efficient fine-tuning large AI models to alleviate the computation, communication, and memory bottleneck issues. Specifically, to address the computation limitation on edge devices, we adopt the split learning architecture, hosting the most computation-intensive component of the large AI model on the edge server. Besides, we employ a memory-efficient zeroth-order fine-tuning algorithm to further reduce the GPU memory consumption. Furthermore, we conduct a rigorous convergence analysis to illustrate how device scheduling influences the learning performance. Based on the analysis, we formulate a global loss minimization problem that jointly optimizes device scheduling, transmit power, and receive beamforming under the average transmission latency constraint. To tackle this complex mixed-integer nonlinear programming problem, we apply Lyapunov theory to break down the long-term optimization problem into multiple subproblems, followed by designing an effective online algorithm. Simulation results show that the proposed framework can lower the GPU memory consumption by up to 84% and GPU hours by 39% compared to the baselines, while achieving comparable accuracy.

An analytical and optimization framework on rate-Cramér-Rao bound (CRB) tradeoff is proposed in this paper for the continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) system. To evaluate the dual-functional performance, the sensing CRB and communication rate are analyzed concerning the induced electromagnetic (EM) waves of CAPAs. For rate-CRB region characterization, the spatially continuous beamforming of transmit CAPA is optimized under three cases: i) A novel closed-form expression for the optimal CAPA beamformer is derived under the single-user single-target scenario, proven to be aligned within the space spanned by the EM-based sensing and communication channels; ii) A general subspace-based beamforming design approach is proposed to address the intractable continuity, converting the continuous beamforming design in spatial domain to discrete weight design in subspace domain and resorting to the semidefinite relaxation for the globally optimal solution; iii) Moreover, the general beamforming design is specialized to both the low-complexity zero-forcing (ZF) and the conventional spatially discrete array (SPDA)-based designs. Numerical results demonstrate that: i) The proposed subspace-based approach can realize efficient and effective beamforming design for reduced mutual interference, enhanced sensing performance, and guaranteed communication rate; ii) The general CAPA beamforming design achieves broader rate-CRB region than the ZF-oriented design and reaches the ultimate performance of the SPDA-based system.

To support ambient intelligence with federated edge learning (FEEL) over resource-constrained wireless networks, it is essential to jointly design and optimize the sensing, computation, and communication processes. In this paper, we propose an integrated sensing, computation, and communication (ISCC) enabled FEEL framework, where each edge device performs wireless sensing to enrich local datasets, executes local model training with accumulated local datasets, and transmits updated local gradients for global model aggregation. Via analyzing the convergence of ISCC-enabled FEEL, we explicitly characterize the impact of newly sensed dataset size in each training round on the optimality gap. Due to the coupling of the sensing, computation, and communication processes, we formulate a long-term optimality gap minimization problem involving the joint optimization of newly sensed dataset size, computation frequency, communication bandwidth, and transmit power. By leveraging Lyapunov optimization, we develop an online optimization algorithm, where, at each iteration, the optimization variables are all derived in closed-form. Moreover, we prove that the proposed algorithm achieves its asymptotic optimal performance and conduct simulations to show the superiority of the proposed ISCC-enabled FEEL.

Vehicle-to-everything (V2X) collaborative perception has recently gained increasing attention in autonomous driving due to its ability to enhance scene understanding by integrating information from other collaborators, e.g. vehicles or infrastructure. Existing algorithms usually share deep features to achieve a trade-off between accuracy and bandwidth. However, most of these methods require joint training of all agents, which results in privacy leakage and is impractical and unacceptable in the real world. Sharing prediction results seems to be a direct solution, but its performance is suboptimal and sensitive to localization noise and communication delay. In this paper, we propose a privacy-preserving collaborative perception framework, where each agent is separately trained with its own dataset and the ego vehicle needs to integrate with completely unknown collaborators. Specifically, we propose MSD, a multi-scale feature fusion method combined with deformable attention, to better fuse features of different agents. We also propose a plug-in domain adapter to align the features from unknown collaborators to ego-domain. Extensive experiments on the challenging DAIR-V2X and V2V4Real demonstrate that: 1) MSD achieves remarkable performance, outperforming others by at least 2.8% and 6.7% in AP0.7 on DAIR-V2X and V2V4Real, respectively; 2) After domain adaptation, it significantly outperforms the No Fusion, Late Fusion scenarios and can approach or even surpass the performance of joint training. We truly achieves privacy-preserving collaboration, providing a new paradigm for the study of collaborative perception, which is crucial for practical applications.

Multivariate Time Series (MTS) forecasting plays a vital role in various real-world applications, such as traffic management and predictive maintenance. Existing approaches typically model MTS data in either Euclidean or Riemannian space, limiting their ability to capture the diverse geometric structures and complex Spatio-Temporal (ST) dependencies inherent in real-world data. To overcome this limitation, we propose the Hybird Symmetric Positive-Definite Manifold Graph Neural Network (HSMGNN), a novel graph neural network-based model that captures data geometry within a hybrid Euclidean–Riemannian framework. To the best of our knowledge, this is the first work to leverage hybrid geometric representations for MTS forecasting, enabling expressive and comprehensive modeling of geometric properties. Specifically, we introduce a Submanifold-Cross-Segment (SCS) embedding to project input MTS into both Euclidean and Riemannian spaces, thereby capturing ST variations across distinct geometric domains. To alleviate the high computational cost of Riemannian distance, we further design an Adaptive-Distance-Bank (ADB) layer with a trainable memory mechanism. Finally, a Fusion Graph Convolutional Network (FGCN) is devised to integrate features from the dual spaces via a learnable fusion operator for accurate prediction. Experiments on three benchmark datasets demonstrate that HSMGNN achieves up to 13.8% improvement over state-of-the-art baselines in forecasting accuracy.

Enabled by metamaterials, continuous aperture array (CAPA) has been proven to play a crucial role in communication performance enhancement, while its potentials in integrated sensing and communication (ISAC) systems have not been investigated. This paper investigates the performance analysis and optimization of CAPA-based ISAC systems for simultaneous user communication and target sensing. To be specific, communication and sensing rates are evaluated based on electromagnetic channels and a Pareto-optimal problem is formulated for beamforming optimization. Closed-form solutions to CAPA-oriented beamforming are derived under communication-, sensing-, and Pareto-optimal cases, and the attainable ISAC rate region is obtained. Numerical results verify that CAPA-based systems can achieve the ultimate sensing and communication performance of spatially discrete array (SPDA)-based systems and significantly expand the ISAC rate region for Pareto optimality.

Multi-agent reinforcement learning (MARL), as a thriving field, explores how multiple agents independently make decisions in a shared dynamic environment. Due to environmental uncertainties, policies in MARL must remain robust to tackle the sim-to-real gap. We focus on robust two-player zero-sum Markov games (TZMGs) in offline settings, specifically on tabular robust TZMGs (RTZMGs). We propose a model-based algorithm (*RTZ-VI-LCB*) for offline RTZMGs, which is optimistic robust value iteration combined with a data-driven Bernstein-style penalty term for robust value estimation. By accounting for distribution shifts in the historical dataset, the proposed algorithm establishes near-optimal sample complexity guarantees under partial coverage and environmental uncertainty. An information-theoretic lower bound is developed to confirm the tightness of our algorithm's sample complexity, which is optimal regarding both state and action spaces. To the best of our knowledge, RTZ-VI-LCB is the first to attain this optimality, sets a new benchmark for offline RTZMGs, and is validated experimentally.

Collaborative visual perception methods have gained widespread attention in the autonomous driving community in recent years due to their ability to address sensor limitation problems. However, the absence of explicit depth information often makes it difficult for camera-based perception systems, e.g., 3D object detection, to generate accurate predictions. To alleviate the ambiguity in depth estimation, we propose RayFusion, a ray-based fusion method for collaborative visual perception. Using ray occupancy information from collaborators, RayFusion reduces redundancy and false positive predictions along camera rays, enhancing the detection performance of purely camera-based collaborative perception systems. Comprehensive experiments show that our method consistently outperforms existing state-of-the-art models, substantially advancing the performance of collaborative visual perception. Our code will be made publicly available.

Federated learning (FL) with synchronous model aggregation suffers from the straggler issue because of heterogeneous transmission and computation delays among different agents. In mobile wireless networks, this issue is exacerbated by time-varying network topology due to agent mobility. Although asynchronous FL can alleviate straggler issues, it still faces critical challenges in terms of algorithm design and convergence analysis because of dynamic information update delay (IU-Delay) and dynamic network topology. To tackle these challenges, we propose a decentralized FL framework based on gradient descent with momentum, named decentralized momentum federated learning (DMFL). We prove that DMFL is globally convergent on convex loss functions under the bounded time-varying IU-Delay, as long as the network topology is uniformly jointly strongly connected. Moreover, DMFL does not impose any restrictions on the data distribution over agents. Extensive experiments are conducted to verify DMFL’s performance superiority over the benchmarks and to reveal the effects of diverse parameters on the performance of the proposed algorithm.

Connected vehicles utilize vehicle-to-vehicle (V2V) wireless communication networks to gather vital information for platoon tracking control, not only enhancing road traffic efficiency but also preventing collisions. This paper introduces a robust safety platooning control method for connected vehicles, which addresses safety challenges in heterogeneous vehicle platoon systems, including time delays, dynamic uncertainties, and stochastic denial-of-service (DoS) attacks. Specifically, we present an uncertain longitudinal dynamic model for heterogeneous vehicles to account for variable driving conditions. Then, a robust safety platooning controller, based on linear matrix inequality (LMI) and Markov jump system theory, is designed. This controller considers communication delays to ensure vehicle-following stability under dynamic uncertainties and stochastic DoS attacks. Finally, the effectiveness of the proposed algorithm is validated through co-simulations using MatLab and CarSim, as well as actual vehicle experiments, demonstrating its capability to mitigate the impact of communication delays, dynamic uncertainties, and stochastic DoS attacks.

This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), which uses the geometric median for aggregation and allows flexible round number for local updates. Unlike most existing resilient approaches, which base their convergence analysis on strongly-convex loss functions or homogeneously distributed datasets, this work conducts convergence analysis for both strongly-convex and non-convex loss functions over heterogeneous datasets. The theoretical analysis indicates that as long as the fraction of the data from malicious users is less than half, RAGA can achieve convergence at a rate of$\mathcal {O}({1}/{T^{2/3- \delta }})$for non-convex loss functions, where$T$is the iteration number and$\delta \in (0, 2/3)$. For strongly-convex loss functions, the convergence rate is linear. Furthermore, the stationary point or global optimal solution is shown to be attainable as data heterogeneity diminishes. Experimental results validate the robustness of RAGA against Byzantine attacks and demonstrate its superior convergence performance compared to baselines under varying intensities of Byzantine attacks on heterogeneous datasets.

A continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) framework is proposed in this paper, where CAPA transceivers are optimized to enhance both target sensing and user communication performance. Novel expressions for achievable communication and sensing rates are derived and CAPA-oriented beamforming is designed to balance the dual-functional Pareto-optimal tradeoff in two scenarios: i) For the single-user single-target case, closed-form continuous beamformers are derived based on communication-, sensing-, and Pareto-optimal criteria to reveal the interrelation of the ISAC rate region with the antenna aperture and channel gains; ii) For the multi-user multi-target case, a general CAPA-ISAC beamforming design algorithm is developed to achieve the Pareto optimality. Beamformer design in the continuous spatial domain is transformed into weight design in the discrete wavenumber domain using Fourier series expansions. Furthermore, alternating optimization, successive convex approximation, and difference of convex techniques are employed to tackle the coupling and non-convexity issues. Numerical results demonstrate that: i) The proposed CAPA-ISAC framework significantly improves both sensing and communication performance and expands the ISAC Pareto rate region; ii) CAPAs exhibit superior beamforming capabilities and reach the ultimate performance limits of spatially discrete arrays (SPDAs).

Integrated sensing and communication (ISAC) enables the next-generation network to possess networked sensing capability, propelling the proliferation of various intelligent applications but introducing complex sensing and communication interference. To this end, this paper studies the cooperative transceiver beamforming design for a multi-cell anti-unmanned aerial vehicle (UAV) ISAC system, where multiple base stations (BSs) collaboratively perform joint UAV sensing. Specifically, to ensure reliable detection, we jointly optimize the ISAC transmit and receive beamformers at BSs and downlink users via maximizing the signal-to-clutter-plus-noise ratio of sensing, taking into account the communication requirements and power constraints. To handle the nonconvex fractional problem, we first propose a centralized beamforming algorithm resorting to alternating optimization, successive convex approximation, and Dinkelbach methods. Then, to alleviate heavy backhaul overhead, a distributed algorithm is put forward, adopting the primal decomposition technique to decouple the inter-cell interference. Numerical results verify that: i) Compared with the standalone sensing by a single BS, the proposed cooperative beamforming design achieves notable enhancement in sensing performance; ii) The designed transceiver beamforming is constructive for interference and clutter suppression in multi-cell ISAC systems.

One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image. Most existing studies in OSOD endeavor to establish effective cross-image correlation with limited query information, however, ignoring the problems of the model bias towards the base classes and the generalization degradation on the novel classes. Observing this, we propose a novel algorithm, namely Base-class Suppression with Prior Guidance (BSPG) network to achieve bias-free OSOD. Specifically, the objects of base categories can be detected by a base-class predictor and eliminated by a base-class suppression module (BcS). Moreover, a prior guidance module (PG) is designed to calculate the correlation of high-level features in a non-parametric manner, producing a class-agnostic prior map with unbiased semantic information to guide the subsequent detection process. Equipped with the proposed two modules, we endow the model with a strong discriminative ability to distinguish the target objects from distractors belonging to the base classes. Extensive experiments show that our method outperforms the previous techniques by a large margin and achieves new state-of-the-art performance under various evaluation settings.

The dangerous driving action seriously affects traffic safety and may cause severe road disasters. Dangerous driving action recognition in Internet of Vehicles (IOV) has been widely exploited to reduce traffic accident risks by discovering and then transmitting the recognition results to autonomous machines or other vehicles. General action recognition models using deep learning networks usually have a large number of parameters and require a significant amount of memory and computational power, which cannot meet the lightweight and real-time requirements of action recognition models deployed on resource-limited onboard devices, e.g., vehicles. As a result, we introduce the broad learning system (BLS) into onboard dangerous driving action recognition tasks, classify actions based on skeleton data, and design the graph convolutional representation (GCR) algorithm and graph convolutional broad learning system (GCBLS) classification model to speed up the recognition process. We verified through ablation experiments that the GCR algorithm can effectively represent skeleton data and improve classification accuracy datasets. In addition, comparative experiments in State Farm and Driver Skeleton datasets show that the GCBLS model has the characteristics of lightweight, real-time, and high accuracy. We also designed a workflow for "noise" caused by poor pose estimations in practical applications, then deployed the proposed workflow on onboard devices, which can run at speeds above 27 FPS with high accuracy, which proves the effectiveness and practicability of our proposed algorithms and models for IOV.

The thriving field of multi-agent reinforcement learning (MARL) studies how a group of interacting agents make decisions autonomously in a shared dynamic environment. Existing theoretical studies in this area suffer from at least two of the following obstacles: memory inefficiency, the heavy dependence of sample complexity on the long horizon and the large state space, the high computational complexity, non-Markov policy, non-Nash policy, and high burn-in cost. In this work, we take a step towards settling this problem by designing a model-free self-play algorithm \emph{Memory-Efficient Nash Q-Learning (ME-Nash-QL)} for two-player zero-sum Markov games, which is a specific setting of MARL. We prove that ME-Nash-QL can output an $\varepsilon$-approximate Nash policy with remarkable space complexity $O(SABH)$, sample complexity $\widetilde{O}(H^4SAB/\varepsilon^2)$, and computational complexity $O(T\mathrm{poly}(AB))$, where $S$ is the number of states, $\{A, B\}$ is the number of actions for the two players, $H$ is the horizon length, and $T$ is the number of samples. Notably, our approach outperforms in terms of space complexity compared to existing algorithms for tabular cases. It achieves the lowest computational complexity while preserving Markov policies, setting a new standard. Furthermore, our algorithm outputs a Nash policy and achieves the best sample complexity compared with the existing guarantee for long horizons, i.e. when $\min \\{ A, B \\} \ll H^2$. Our algorithm also achieves the best burn-in cost $O(SAB\,\mathrm{poly}(H))$, whereas previous algorithms need at least $O(S^3 AB\,\mathrm{poly}(H))$ to attain the same level of sample complexity with ours.

The expansion of the Internet of Things (IoT) has led to a significant surge in data flow over edge networks, posing substantial challenges to data mining and management. While federated edge learning (FEEL) effectively accomplishes global integration and local training based on the decentralized data sets, its deployment across expansive IoT networks introduces additional challenges. The primary issues stem from managing the interaction between the communication load and learning effectiveness. The communication loads driven by recurrent data exchanges between the user equipment (UE) and central servers exacerbate network congestion and latency issues. Moreover, the learning efficacy is undermined due to the typically nonindependent and identically distributed (non-IID) characteristics of real-world IoT data. In this article, a novel communication-efficient hierarchical FEEL framework is proposed to tackle these challenges. Specifically, UEs are adaptively clustered according to their link conditions, geographic locations, and data distributions. Small base stations (SBSs) collect local model updates from the UEs in their clusters and communicate with a macro base station (MBS) for the global model aggregation. To jointly maximize the communication gain (in terms of reducing latency) and the learning gain (in terms of improving accuracy), a clustering and resource allocation optimization problem is formulated, and a cross entropy-based method with low computational complexity is proposed. Numerical experiments validate that the proposed hierarchical FEEL system achieves fast convergence and significantly improves the system efficiency for various learning tasks and the system settings.

In-network aggregation is a fundamental operation for massive packets in the Internet of Things (IoT). By aggregating ultra-small packets, the energy consumption for data transmission is not related to the packet number, while the average delay performance depends on the delay of all packets even if they are aggregated. In this paper, we propose a low-delay ultra-small packet transmission scheme with in-network aggregation in energy-harvesting multi-hop networks, where each device periodically transmits packets in a collect-wait-forward relaying manner. Considering the resulting extra waiting time during relaying, we first drive the tractable form of the average end-to-end delay by problem transformation. By characterizing the two-dimensional evolution property from the perspective of both hops and time, the delay minimization problem is reformulated as an infinite-horizon average-cost Markov decision process with a two-dimensional optimality equation. To deal with the curse of dimensionality, we decompose the global Bellman equation into several per-device local relay selection problems. Based on the problem decomposition, we propose a distributed ultra-Small Packet Aggregation Relay SElection (SPARSE) algorithm via stochastic learning. The convergence is further proved theoretically and verified by simulation. Simulation results reveal that the proposed scheme achieves significant performance gain over the baselines for ultra-small packets.

The continuous evolution of cellular networks has resulted in the rapid increase in both mobile applications and devices in the Internet of Vehicles. The introduction of the multi-access edge computing method makes it possible for vehicles in remote areas to offload their computational tasks, which can effectively relieve the computing pressure of local devices and reduce the computational delay as well. Tasks offloading for multi-user is a resource competition problem, especially in dynamic environments, which is difficult to be solved by traditional algorithms. In this article, we propose a two-layer hybrid system with local and edge computing, providing convenient computing and offloading services for vehicle users in dual dynamic scenarios of task generation and vehicle mobility. The delay and queuing situations are considered comprehensively in the formulated optimization problem, which can be solved by the proposed deep deterministic policy gradient-based computation offloading algorithm. The offloading process of the vehicle tasks in dynamic scenarios is transformed into a Markov decision process to obtain the offloading strategy. Simulation results demonstrate the performance advantages of two-tier computing architecture. Compared with random offloading, deep Q network-based offloading, and local computing, the algorithm proposed in this article gains the highest average reward of tasks. Besides that, numerical results also prove that our algorithm has the lowest average delay under different computing capabilities of edge servers.

Federated learning (FL) is recognized as a promising privacy-preserving distributed machine learning paradigm, given its potential to enable collaborative model training among distributed devices without sharing their raw data. However, supporting FL over wireless networks confronts the critical challenges of periodically executing power-hungry training tasks on energy-constrained devices and transmitting high-dimensional model updates over spectrum-limited channels. In this paper, we reap the benefits of both energy harvesting (EH) and over-the-air computation (AirComp) to alleviate the battery limitation by harvesting ambient energy to support both the training and transmission of local models, and to achieve low-latency model aggregation by concurrently transmitting local gradients via AirComp. We characterize the convergence of the proposed FL by deriving an upper bound of the expected optimality gap, revealing that the convergence depends on the accumulated errors due to partial device participation and model distortion, both of which further depend on dynamic energy levels. To accelerate the convergence, we formulate a joint AirComp transceiver design and device scheduling problem, which is then tackled by developing an efficient Lyapunov-based online optimization algorithm. Simulations demonstrate that, by appropriately scheduling devices and allocating energy across multiple communication rounds, our proposed algorithm achieves a much better learning performance than benchmarks.

Cell-free massive multiple-input multiple-output (mMIMO) architecture is a promising solution for Industrial Internet of Things (IIoT) because it not only provides massive connectivity but also eliminates the traditional cell edges. Considering the heterogeneous traffic and requirements in the industry, in this paper, we propose a device priority-aware resource allocation policy under cell-free mMIMO IIoT networks. Specifically, we design a priority-aware frame structure that can be used to provide differentiated age of information (AoI) guarantees for devices of different priorities and locations. To characterize the proposed policy, we develop a general analysis framework to evaluate the signal-to-interference ratio meta distribution and the average AoI of a generic device. The framework captures multiple main features under wireless IIoT networks, including cell-free mMIMO architecture, frame structure, finite-sized geographic areas, densely deployed devices, device priority, retransmission, and interaction among different transmission links. The analytical framework is validated by simulations. Based on the analysis, we study a mean-variance optimization problem to improve the network average AoI, while guaranteeing the average AoI per device. Numerical results show that the proposed frame structure works effectively in enhancing the AoI performance of cell-free IIoT networks.

The next-generation wireless network is perceived to integrate with sensing capability and evolve into the perceptive mobile network (PMN), enabling massive sensing-intensive applications. However, the sensing function will affect the communication performance in cellular networks. To study the sensing and communication performance of PMNs and their interactions, this paper investigates a millimeter-wave PMN with dual-functional base stations (BSs) for simultaneous detection of unauthorized unmanned aerial vehicles (UAVs) and user communication via the unified transmit signal and beamforming. We develop a system-level theoretical framework to investigate the sensing and communication performance of PMNs based on stochastic geometry, which captures the mutual interference and resource contention between the two functions and builds a foundation for the optimization of network configurations. In addition, by leveraging the collaboration of multiple BSs in PMNs, we propose a cooperative sensing strategy combining the monostatic and bistatic sensing processes to enhance the reliability of UAV surveillance. Simulation results verify the effectiveness of the proposed theoretical framework and demonstrate the benefits of cooperative sensing in UAV detection and communication performance, as compared with the standalone sensing by individual BSs.

Video surveillance systems are playing increasingly important roles in our everyday lives. To get meaningful surveillance information in a timely and accurate manner, it is vital to optimally allocate computation and communication resources for image classification tasks. In this paper, taking face recognition as an example, we propose a novel end-to-edge collaborative computing system based on a multi-exit network to dynamically allocate computation at the front end (the camera sensor) and back end (the mobile edge computing server). With the ∊-greedy algorithm for reinforcement learning, the decision module decides whether to obtain recognition results from earlier exits at the front end or transmit the feature maps to the back end to obtain more accurate results. The module balances recognition accuracy and time overhead under different channel conditions. Experimental results show that the proposed system can significantly save inference time and maintain competitive accuracy in various communication channel conditions.

Deploying federated learning (FL) in wireless networks faces the critical challenge of communication bottlenecks. To address this issue, in this paper, we consider an over-the-air computation (AirComp) assisted hierarchical FL (HFL) framework, where a cloud-edge-device-based three-tier network architecture is constructed to train a global model. We first theoretically characterize the convergence of the AirComp-assisted HFL framework and formulate a combinatorial optimization problem that jointly optimizes the edge interval control and local device transceiver design to minimize the convergence upper bound to boost the overall learning performance and reduce communication cost. We show that the formulated optimization problem can be decoupled into an edge interval control problem and a transceiver design problem, which can be tackled by developing a relaxation and rounding algorithm and an alternating Lyapunov drift-based algorithm, respectively. Extensive simulations demonstrate that our proposed algorithm significantly outperforms the baseline schemes.

In China, some e-commerce platform (EP) companies such as Alibaba and JD are now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players, with EP companies being VNOs, through appropriate integration of the VNO business and the companies' own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage static Stackelberg game. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Besides, for the scenario lack of knowledge on the interaction between the NO and VNO in a dynamic game, we propose a deep Q-network (DQN) based algorithm to derive the optimal strategies of the NO and VNO. Simulation results show impact of system parameters on the utilities of game players and social welfare. We also study the impact of system parameters on different algorithms and discover that the proposed DQN-based algorithm can learn a good strategy as compared with the Stackelberg equilibrium solution.

The boom in Internet of Things has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination and aroused widespread attention from both academia and industry. In this paper, we develop a theoretical framework to evaluate the statistics of AoI, including its average and violation probability, in wireless networks under different types of sources and updating patterns. The analyses account for the randomness that arises from both the spatial deployment and temporal queueing dynamics, and its accuracy is verified through simulations. Based on the analytical results, we design a locally adaptive power control policy to optimize the sum of average AoI of all nodes, which allows each node to assign transmit power according to its local observation. The proposed scheme has low implementation complexity. Numerical results show that the proposed power control policy can significantly improve information freshness. The scheme is well adapted to variants of network environment and heterogeneous source-destination distance. Further, we evaluate the effect of the retransmission mechanism and updating patterns on the AoI performance.

Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.

The boom in Internet of Things (IoT) has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination node and aroused widespread attention from both academia and industry. In this paper, we develop a locally adaptive power control policy for wireless ad hoc networks, which adjusts each node’s transmit power according to its local observation so as to optimize the sum of average AoI of all destination nodes. The proposed scheme has a low implementation complexity. Numerical results show that the proposed scheme is well adapted to variants of network environment and can significantly improve information freshness.

With the deluge of wireless data, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial small base stations (SBSs) to relieve the load of ground macro base stations by establishing wireless backhaul connections with them and providing high-quality service to users. Thanks to the emergence of integrated access and backhaul (IAB), the access and backhaul communication links can work on the same millimeter wave (mmWave) band with huge available bandwidth. This paper studies the quality-of-service (QoS) performance of heterogeneous traffic under equal partition and average load partition spectrum allocation strategies for mmWave UAV-assisted IAB cellular networks. Specifically, we develop a theoretical framework to analyze the mean packet throughput (MPT) of users based on stochastic geometry and queueing theory. Simulation results demonstrate that the deployment of UAVs can promote MPT performance compared to ground SBSs and appropriate UAV height, UAV density, and spectrum allocation play significant roles in improving QoS performance of heterogeneous traffic in the network.

Recently, 360-degree mobile virtual reality video (MVRV) has become increasingly popular because it can provide users with an immersive experience. However, MVRV is usually recorded in a high resolution and is sensitive to latency, which indicates that broadband, ultra-reliable, and low-latency communication is necessary to guarantee the users’ quality of experience. In this paper, we propose a mobile edge computing (MEC)-based 360-degree MVRV streaming scheme with field-of-view (FoV) prediction, which jointly considers video coding, proactive caching, computation offloading, and data transmission. To meet the requirement of stringent end-to-end (E2E) latency, the user’s viewpoint prediction is utilized to cache video data proactively, and computing tasks are partially offloaded to the MEC server. In addition, we propose an analytical model based on diffusion process to study the packet transmission process of 360-degree MVRV in multihop wired/wireless networks and analyze the performance of the MEC-enabled scheme. The simulation results verify the accuracy of the analysis and the effectiveness of the proposed MVRV streaming scheme in reducing the E2E delay. Furthermore, the analytical framework sheds some light on the impacts of system parameters, e.g., FoV prediction accuracy and transmission rate, on the balance between computation delay and communication delay.

In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user.

The conventional federated learning (FL) framework usually assumes synchronous reception and fusion of all the local models at the central aggregator and synchronous updating and training of the global model at all the agents as well. However, in a wireless network, due to limited radio resource, inevitable transmission failures and heterogeneous computing capacity, it is very hard to realize strict synchronization among all the involved user equipments (UEs). In this paper, we propose a novel asynchronous FL framework, which well adapts to the heterogeneity of users, communication environments and learning tasks, by considering both the possible delays in training and uploading the local models and the resultant staleness among the received models that has heavy impact on the global model fusion. A novel centralized fusion algorithm is designed to determine the fusion weight during the global update, which aims to make full use of the fresh information contained in the uploaded local models while avoiding the biased convergence by enforcing the impact of each UE’s local dataset to be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve fast and smooth convergence and enhance the training efficiency significantly.

To meet the ever-increasing demand for mobile video services, one of the effective solutions is caching some popular videos in edge nodes. In this paper, we propose an online stochastic learning algorithm with two time scales for joint caching and transmission optimization in the video frame level. To overcome the drift distortion caused by the dependency among video frames, the transmission process is formulated as an infinite horizon Markov decision process (MDP). We derive the equivalent Bellman equation and design the online value iteration algorithm via stochastic approximation for transmission. Due to the lack of the expression between the system performance and the caching policy, we design a gradient-free stochastic optimization algorithm to update the caching policy. Finally, simulation results show that our proposed algorithm achieves better performance than conventional caching algorithms.

Most of existing works about data offloading do not consider the participation of mobile subscribers (MSs) when designing the budget allocation, such that the fairness performance is challenging. For a mobile data offloading system consisting of a base station run by a service provider (SP), multiple MSs, and several third-party WiFi access points (APs), in this paper we study how the SP allocates the budget among APs and arranges the offloading data rate for MSs such that the fairness of each MS’s profit is guaranteed. By jointly considering the preferences of MSs for different APs and budget limit, we propose a two-phase participatory budget and rate allocation (PBRA) scheme where a core solution is designed for the budget allocation in the first phase to guarantee the fairness of all MSs, and an optimal rate allocation based on the core solution is designed to maximize the expected amount of data offloading in the second phase. Simulation results demonstrate the efficacy of our proposed PBRA scheme. Specifically, our proposed scheme can achieve the fairness among all MSs with a less performance loss in terms of the expected amount of data offloading by comparing with three benchmark schemes.

In China, some e-commerce platform (EP) companies such as Alibaba and JD have been now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players after EP companies being VNOs through appropriate integration of the VNO business and the companies’ own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage Stackelberg game. In Stage I, the NO decides the price of mobile data for the VNO; in Stage II, the VNO decides its data plan fee for MUs and the ad price for e-commerce merchants (EMs); in Stage III, the MUs make their own decisions on the data plan subscription and the number of ads to be watched, while the EMs decide the number of ad slots they buy from the EP. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Simulation results show the impact of the system parameters on the utilities of game players and social welfare, and reveal that the solution can indeed lead to a quadri-win outcome in some cases. At the same time, we summarize some insights that have economic guidance.

Vehicle-to-everything (V2X) communications face a great challenge in delivering not only the low-latency and ultra-reliable safety-related services but also the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. This paper focuses on the robust resource management of V2X communications with the consideration of channel uncertainties. First, we formulate a transmit power minimization problem, whilst guaranteeing the different quality-of-service (QoS) requirements. To achieve the robustness of QoS provisions against channel uncertainties, a statistical leaning approach is developed to learn the uncertainties from the data samples of the random channel coefficients as a convex ellipsoid set, which is also called high-probability-region (HPR). Then, the highly intractable power minimization problem is converted into a second-order cone program by the robust optimization approach. Afterwards, we propose a joint set partitioning and reconstruction mechanism to further reduce the total transmit power by pruning the rough HPR into a more precise uncertainty set, which leads to a trackable second-order cone program and a linear program. Finally, we prove that the network performance can be effectively enhanced by the improvement mechanism. Simulation results verify the effectiveness of the robust resource allocation approaches over the non-robust one.

This paper investigates the reliable computation offloading in vehicular edge computing (VEC) systems. Compared with the traditional task replication method in which task replicas are typically assigned to multiple service vehicles at the same time, in our work, a task vehicle allocates the computation tasks and communication resources to its neighboring service vehicles through the vehicle-to-vehicle (V2V) links, and avoids the degradation of delay and computation efficiency. Specifically, an optimization problem is formulated to minimize the task completion delay and ensure offloading reliability. Then, an algorithm based on the penalty and the concave-convex procedure (CCCP) method is proposed to effectively solve the formulated optimization problem. The simulation results show that the task completion delay of the proposed algorithm is only 30% of that in the traditional task replication method.

In this work, we study the cooperative and non-cooperative transmission schemes design for live VR video broadcast scenarios by utilizing non-orthogonal multiple access (NOMA), considering that users’ viewports partly overlap due to behavioral coherence. To characterize the performance of the proposed cooperative and non-cooperative transmission schemes, the exact and asymptotic expressions of outage probability, as well as the average outage capacity under imperfect successive interference cancellation (SIC), are derived, respectively. Based on the asymptotic outage probability results, we optimize the power allocation to maximize the average outage capacity of the proposed schemes. Finally, simulation results demonstrate that both of the proposed schemes can achieve a considerable performance gain over the traditional orthogonal multiple access (OMA) scheme in average outage capacity, and each of the proposed schemes has its advantages and applicable scenarios.

Video surveillance systems or Internet of Multimedia Things are playing a more and more important role in our daily life. To obtain useful surveillance information timely and accurately, not only image recognition algorithms but also computing and communication resources can be bottlenecks of the whole system. In this article, taking face recognition application as an example, we study how to build video surveillance systems by utilizing mobile edge computing (MEC), one of the 5G's key technologies. Specifically, to achieve high recognition accuracy and low recognition time, we design image recognition algorithms for both the camera sensor and MEC server, and utilize the action-value methods to train actions of the system by jointly optimizing offloading decision and image compression parameters. The experimental results show the advantages of the proposed system for enabling communication environment-adaptive, efficient, and intelligent video surveillance.

To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralized control. In this paper, we investigate the computing offloading problem for LTE-U-enabled IoT, where computing tasks on an IoT device are either executed locally or offloaded to the edge server on an LTE-U base station. Considering a constrained edge computing cost (e.g., operation power consumption) for offloaded tasks, the task scheduling problem is formulated as a constrained Markov decision process (CMDP) to maximize the long-term average reward, which integrates both task completion profit and task completion delay. In order to address the uncertainty of task arrivals and channel availability, a constrained deep Q-learning-based task scheduling algorithm with provable convergence is proposed, where an adaptive reward function can appropriately bound the average edge computing cost. Extensive simulation results show that the proposed scheme considerably enhances the system performance.

In order to tackle the spectrum scarcity problem and enhance the spectrum efficiency, deploying LTE in unlicensed band (LTE-U) is an emerging technology for supporting massive connections in future networks. By taking into account of the coexistence between the LTE-U cellular user equipments (CUEs) and the legacy Wi-Fi stations (STAs) in the unlicensed band, a partial non-orthogonal multiple access (NOMA)-based scheme is proposed in this paper. By dividing all UEs into two groups and making the Wi-Fi STA as the UE with the weakest channel gain in its group, we can exploit the multiplexing gain of NOMA by introducing no extra modification to Wi-Fi STAs. Accordingly, a fairness-oriented resource allocation framework is formulated as a max-min problem to jointly optimize the inter-group time occupancy ratio and the intra-group power allocation when the guaranteed bit rate (GBR) requirements for each UE are considered. A modified two-dimensional bisection algorithm is proposed to search the optimal time occupancy ratio and the max-min rate in this coexisting network. Numerical results validate the effectiveness of the partial NOMA scheme and outperform the traditional orthogonal multiple access method, in terms of both efficiency and robustness.

We propose a pose-guided approach for appearance preserving video prediction by combining global and local information using Generative Adversarial Networks (GANs). The aim is to predict the subsequent frames based on previous frames of human action videos. Considering that human action videos contain both background scenes which are relatively time-invariant among frames, and human actions which are time-varying components, we use a global GAN to model the time-invariant background and coarse human profiles. Then, a local GAN is utilized to further refine the time-varying human parts. Finally, we use a 3D auto-encoder to fine-tune the frame-by-frame images to obtain the whole predicted video. We evaluate our model on the Penn Action and J-HMDB datasets and demonstrate the superiority of our proposed method over other state-of-the-art methods.

Despite enjoying extensive applications in video analysis, three-dimensional convolutional neural networks (3D CNNs) are restricted by their massive computation and storage consumption. To solve this problem, we propose a three-dimensional regularization-based neural network pruning method to assign different regularization parameters to different weight groups based on their importance to the network. Further we analyze the redundancy and computation cost for each layer to determine the different pruning ratios. Experiments show that pruning based on our method can lead to 2× theoretical speedup with only 0.41% accuracy loss for 3D-ResNet18 and 3.28% accuracy loss for C3D. The proposed method performs favorably against other popular methods for model compression and acceleration.

To facilitate the private deployment of industrial Internet-of-Things (IoT), applying LTE in unlicensed spectrum (LTE-U) is a promising approach, which both tackles the problem of lacking licensed spectrum and leverages an LTE protocol to meet stringent quality-of- service (QoS) requirements via centralized control. In this paper, we investigate the computing offloading problem in an LTE-U-enabled network, where the task on an IoT device is carried out either locally or is offloaded to the LTE-U base station (BS). The offloading policy is formulated as an optimization problem to maximize the long term discounted reward, considering both task completion profit and the task completion delay. Due to the stochastic task arrival process at each device and the Wi-Fi's contention-based random access, we reformulate the computing offloading problem into a Q-learning problem and solve it by a deep learning network-based approximation method. Simulation results show that the proposed scheme considerably enhances the system performance.

Dynamic time-division duplex (D-TDD) transmission in small cell networks have been emerged as one of the promising solutions to support the asymmetric traffic requirements in the next generation cellular mobile communication systems. On the other hand, the backhaul, which carries the traffic between base stations (BSs) and the core network, has great influence on providing reliable and timely connectivity. In this work, we study a two-tier D-TDD network taking the random locations of devices, packet arrival process, scheduling, interference, and backhaul into consideration to understand the influence from backhaul to uplink (UL) and downlink (DL) mean packet throughput per UE (MPT for short). We use an approximate method to derive the interference and successful transmission probability via stochastic geometry, and then achieve the mathematical derivation of DL and UL MPT with different kinds of backhaul using queueing theory tools. Based on the simulation results, we verify the accuracy of our analysis and explore the impact of the service parameters and backhaul.

Small cell networks under dynamic time-division duplex (D-TDD) transmission have emerged as a promising solution to accommodate the varied uplink (UL) and downlink (DL) traffic in next generation cellular mobile communication networks. By allowing each cell to individually configure its communication direction, D-TDD allocates resources to accommodate whichever transmission direction needs it most. However, with unaligned transmissions, the interference increases and limits the performance of mean packet throughput (MPT). In this paper, we study the small cell networks under D-TDD with cell clustering being the interference mitigation technique (clustered D-TDD). By leveraging stochastic geometry and queuing theory, we develop an analytical framework that captures both spatial and temporal randomness. We study the MPT whose analytical expression is verified via simulation, and based on the analysis, we explore the impact from different network and service parameters. In particular, numerical results show that there is an optimal cluster size for DL MPT, while UL MPT always benefits from increasing cluster size. By grouping cells into clusters, the clustered D-TDD can provide the flexible service compared with static time-division duplex (S-TDD), and provide significant improvement over a traditional D-TDD in terms of UL MPT at a small cost of DL MPT.

Wireless energy transfer (WET) is emerging as a promising paradigm that provides sustainability for pervasive battery-powered devices to complete various tasks. Due to high attenuation of WET, it is crucial to design new architecture that conserves energy while guaranteeing task completion. In this paper, we propose an energy bank-based crowdsourcing framework and an incentive mechanism for energy conservation in wireless-powered task-oriented networks. An employer device outsources the whole or a part of its task to several worker devices and pays them energy as reward. Through energy-service trading, the employer consumes less energy and workers make energy profits. The virtual energy bank keeps accounts for all devices, authenticates the trading, and settles payments through a lossless bookkeeping-like manner. We analyze the employer's expense-minimized and workers' profit-maximized decisions and prove that the optimal decisions compose a Stackelberg equilibrium. To quantify the potential in energy saving, we further apply the framework to a relay-based sensor network where a source employs relays to forward data with a minimum rate requirement. An algorithm is developed for the NP-hard expense minimization problem. The simulation results reveal that our proposed framework and mechanism improve the energy efficiency by providing a win-win situation for both sides.

In this paper, we study wireless energy transfer-enabled passive communication networks, where passive nodes (PNs) harvest radio frequency (RF) energy emitted by an active node (AN) and/or scatter the RF wave to a receiver for data transfer. The performance of such networks highly depends on channel state information (CSI), but its acquisition is quite challenging since energy-and-hardware constrained PNs are generally unable to estimate or feedback CSI. We propose a harvest-while-scatter protocol, where every PN uses the time when other PNs scatter to harvest RF energy, while only introducing minimum interference. Furthermore, we develop a channel training approach for this protocol to estimate means and (co)variances of channel gains via collecting and utilizing historical data and energy transmissions. To minimize the energy consumed at the AN with limited statistical CSI, we formulate a distributionally robust energy minimization problem involving a non-convex objective function and a quality-of-service chance constraint. In addition, we develop an iterative algorithm to optimally solve it with low complexity. Simulation results show the effectiveness of our proposed protocol and algorithm, and reveal the effect of relative node locations on energy consumption in terms of energy harvesting and data transfer.

In this paper, we investigate the access mechanisms of LTE Licensed Assisted Access (LAA) co-existing with Wi-Fi over the unlicensed band. To this end, we first develop an analytical model to study the performance of existing Load Based Equipment (LBE) MAC for Unlicensed Long Term Evolution (U-LTE), identify the fairness issues, and quantify the reservation overhead of the protocol. To maximize the network throughput and ensure fair spectrum sharing of U- LTE and Wi-Fi, we propose a hybrid MAC protocol that combines the best features of LBE MAC and Frame Based Equipment (FBE) MAC. A two-level renewal process-based model is also developed to analyze the throughput performance of the proposed MAC. By jointly optimizing the sleep period and the contention window size of U-LTE, the best co-existing performance in terms of the total network throughput and throughput fairness of U-LTE and Wi-Fi can be achieved, with minimal reservation overhead. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed MAC protocol.

The limited computing ability of mobile device constrains its performance on complex mobile applications. Mobile cloud computing (MCC) has therefore emerged to migrate computation-intensive tasks to remote clouds or mobile cloudlets. Most strategies allocate tasks with minimal response time yet few consider the load of the nodes. In this paper, we focus on the load balancing problem for nodes when they conduct offloading. We first establish a five-tuple characterized task model to capture the response time of offloaded tasks. Then, we formulate the task allocation problem as an integer linear problem (ILP) under certain conditions. Furthermore, we propose a two-step appointment- driven strategy to solve this problem with minimal task response time. Specifically, a modified genetic algorithm (GA) is adopted to coordinate the load of the nodes. Simulations are conducted to prove the feasibility of our strategy and evaluate the performance of load coordination.

LTE over unlicensed spectrum (LTE-U) is one of the promising approaches to further improve LTE network throughput. To maximize the benefit of LTE-U, in this work we study joint resource allocation for LTE over the legacy licensed spectrum and the sharing unlicensed spectrum in a multi-cell scenario. Specifically, we formulate a mixed-integer power-channel allocation problem aiming at maximizing the network throughput, with the constraints of protecting the coexisting Wi-Fi networks and hardware limitation of user equipments in the LTE-U networks. To solve the resource allocation problem efficiently, we exploit delay column generation approach to decompose the original optimization problem and then propose a novel algorithm based KKT conditions. Simulation results show the advantage of LTE-U networking and the effectiveness of the proposed algorithm in terms of convergence speed and network throughput.

In vehicular networks, exchanging beacons among neighboring vehicles is a promising solution to guarantee vehicle safety. However, frequent beaconing under high vehicle density will cause collisions, which is harmful to safety and tracking accuracy. In this work, we propose an adaptive beaconing method for vehicle safety and tracking accuracy. Each vehicle broadcasts beacon interval requests, including the intervals needed for safety and for tracking accuracy. The road side unit allocates resources for vehicle's beaconing according to the requests from all vehicles. We formulate the resource allocation problem for maximizing the sum utility which measures the satisfaction of vehicles. We transform the optimization problem into a maximum weighted independent set problem, and propose an algorithm to solve it efficiently. Simulation results show that the proposed method outperforms the benchmark in terms of beacon reception ratio, safety guarantee, and tracking accuracy.

In this paper, we consider a fully sustainable cooperative communication system which consists of multiple source nodes with radio-frequency (RF) energy harvesting capabilities, a half-duplex relay node with renewable energy supplies, and a destination node. Specifically, the relay node is powered by the green energy harvested from renewable sources such as solar or wind, while the source nodes are wirelessly charged by the RF energy from the relay node's forwarding signals to the destination node. An optimal joint time scheduling and power allocation problem is formulated to achieve the maximum system sum-throughput of the users over a finite time horizon. To tackle the formulated NP-hard non-convex mixed integer nonlinear programming problem, we first analyze its upper bound by problem reformulation and relaxation, which can be simplified by the directional water filling algorithm and iteratively solved by sequential parametric convex approximation. We then propose an optimal branch-and-bound framework to solve the formulated problem, and develop an efficient sub-optimal offline algorithm and a heuristic online algorithm to reduce the computational complexity. Finally, extensive simulations are conducted to verify the superiority of the proposed solution and demonstrate that the sub-optimal algorithm approaches the performance upper bound with polynomial time complexity.

Wireless energy transfer (WET) has received considerable attention for green communications. Unbalanced energy distribution and performance outages bring difficulties to the application of WET. This paper considers a network consisting of a WET-enabled access point (AP) and several energy-harvesting and source-powered user equipments (UEs). We investigate time-frequency resource allocation of downlink (DL) and uplink (UL) wireless information transfer (WIT) and DL WET. To improve energy efficiency and resource utilization, WET and WIT are orthogonally assigned at a low-frequency narrowband and a high-frequency wideband, respectively. A practical energy harvesting model is adopted, where power conversion efficiency depends on the received power instead of being a constant. To meet UEs' different energy demands with the highest conversion efficiency, we develop beam switching, where the AP employs beamforming to charge UEs by jointly controlling power and time based on energy distributions and channel conditions. We formulate the overall energy minimization as a non-convex stochastic optimization problem, which is decomposed by fixing DL-UL time allocation ratio, transformed through Markov decision processes, and finally solved via linear programming. Simulation results verify the effectiveness of our proposed scheme on energy conservation and reveal the tradeoff of time allocation between DL and UL.

As an efficient way to collect sensing data, participatory sensing has been receiving more and more attentions and its applications cover various areas such as traffic control and management, environmental monitoring, etc. In a participatory sensing system, Service Provider (SP) works as the task promulgator, Smartphone User (SU) works as the task executor and the Platform handles the sensing task allocation procedure. Incentive mechanism plays a key role in stimulating both SPs and SUs to take part in the participatory sensing system. Most of previous works on the incentive mechanism design do not consider the social relationship of SUs, which may degrade the performance of the participatory sensing system. In this paper, we take the social relationship of SUs into consideration, and design a Social-Aware Incentive Mechanism (SAIM) which can achieve high system performance and satisfy the properties of individual rationality, budget balance, the completely truthful for SPs, and partially truthful for SUs. Simulation results show the better performance of the proposed incentive mechanism compared with two other counterparts in terms of social utility and social effect. Specifically, the proposed mechanism can improve the social utility by 12% and 16% compared with McAfee and random selection, respectively, with the number of smartphone users m = 100.

With the foreseeable explosive growth of small cell deployment, backhaul has become the next big challenge in the next generation wireless networks. Heterogeneous backhaul deployment using different wired and wireless technologies may be a potential solution to meet this challenge. Therefore, it is of cardinal importance to evaluate and compare the performance characteristics of various backhaul technologies to understand their effect on the network aggregate performance. In this paper, we propose relevant backhaul models and study the delay performance of various backhaul technologies with different capabilities and characteristics, including fiber, xDSL, millimeter wave (mmWave), and sub-6 GHz. Using these models, we aim at optimizing the base station (BS) association so as to minimize the mean network packet delay in a macrocell network overlaid with small cells. Numerical results are presented to show the delay performance characteristics of different backhaul solutions. Comparisons between the proposed and traditional BS association policies show the significant effect of backhaul on the network performance, which demonstrates the importance of joint system design for radio access and backhaul networks.

In vehicular ad hoc networks (VANETs), multi-hop wireless broadcast has been considered a promising technology to support safety-related applications that have strict quality-of-service (QoS) requirements such as low latency, high reliability, scalability, etc. However, in the urban transportation environment, the efficiency of multi-hop broadcast is critically challenged by complex road structure, severe channel contention, message redundancy, etc. In this paper, we propose an urban multi-hop broadcast protocol (UMBP) to disseminate emergency messages. To lower emergency message transmission delay and reduce message redundancy, UMBP includes a novel forwarding node selection scheme that utilizes iterative partition, mini-slot, and black-burst to quickly select remote neighboring nodes, and a single forwarding node is successfully chosen by the asynchronous contention among them. Then, bidirectional broadcast, multi-directional broadcast, and directional broadcast are designed according to the positions of the emergency message senders. Specifically, at the first hop, bidirectional broadcast or multi-directional broadcast conducts the forwarding node selection scheme in different directions simultaneously, and a single forwarding node is successfully chosen in each direction. Then, directional broadcast is adopted at each hop in the message propagation direction until the emergency message reaches an intersection area where multi-directional broadcast is performed again, which finally enables the emergency message to cover the target area seamlessly. Analysis and simulation results show that the proposed UMBP significantly improves the performance of multi-hop broadcast in terms of one-hop delay, message propagation speed, and message reception rate.

Traffic offloading and resource sharing are two common methods for delivering cellular data traffic over unlicensed bands. In this paper, we first develop a hybrid method to take full advantages of both traffic offloading and resource sharing methods, where cellular base stations (BSs) offload traffic to WiFi networks and simultaneously occupy certain number of time slots on unlicensed bands. Then, we analytically compare the cellular throughput of the three methods with the guarantee of WiFi per-user throughput in the single-BS scenario. We find that traffic offloading can achieve better performance than resource sharing when existing WiFi user number is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. In the multi-BS scenario where the coverage of small cells and WiFi access points are mutually overlapped, we consider to maximize the minimum average per-user throughput of each small cell and derive a closed-form expression for the throughput upper bound in each method. Meanwhile, practical traffic offloading and resource sharing algorithms are also developed for the three methods, respectively. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the proposed algorithms as well.

In a wireless powered communication network, where user equipments (UEs) harvest radio frequency energy from an access point (AP) and send data to the AP, there exists the near-far problem with respect to energy harvesting efficiency due to UEs' random locations. In this paper, we introduce the concept of delay-aware energy balancing by minimizing the average transmission delay while taking into account the issue of unbalanced harvested energy distribution. In particular, we propose an adaptive harvest-then-cooperate protocol, where every UE first harvests the energy emitted by the AP and then sends data to the AP directly or via other UEs acting as relays in a time-division multiplexing manner. In this protocol, the AP selects the combination of transmission power and routing topology by matching load and energy distributions in the network while minimizing the average transmission delay. Furthermore, we develop a method generating scheduling schemes for this protocol to avoid data overflow in the UE relay. To determine the combination with minimum delay, we approximate the average delay as a Markov decision process and propose a low-complexity sample path-based algorithm to obtain a near-optimal solution. Simulation results demonstrate that the proposed protocol is able to balance the energy distribution while minimizing the transmission delay.

Network densification is one of the dominant evolutions to increase network capacity toward future cellular networks. However, the complex and random interference in the resultant interference-limited heterogeneous cellular networks (HCN) may deteriorate packet transmission reliability and increase transmission delay, which are essential performance metrics for system design in HCN. By modeling the locations of base stations (BSs) as superimposed of independent Poisson point process, we propose an analytical framework to investigate delay and reliability tradeoffs in HCN in terms of timely throughput and local delay. In our analysis, we take the BS activity and temporal correlation of transmissions into consideration, both having significant effects on the performances. The effects of mobility, BS density, and association bias factor are evaluated through numerical results.

As the number of vehicles increases significantly, traffic congestion has become a major social problem in recent years. Such a situation can be alleviated effectively with the emerging of vehicular ad hoc network (VANET)-based real-time path planning systems. However, existing systems face the challenges of poor anti-congestion capability and high complexity. To address the related issues, an road side unit (RSU)-based architecture is proposed in this paper, in which a city is divided into different areas, each with an RSU. Based on the architecture, a decentralized and hierarchical real-time path planning algorithm is proposed. The path planning problem is formulated from two layers, i.e., area path selection in upper layer and intra-area routing in bottom layer, both of which target to minimize the average travel time. Numerical results show that, our proposed algorithm inherits the anti-congestion capability and owns the advantage of low complexity, as compared with the shortest path algorithm and centralized algorithm.

In this paper, two novel mechanisms are developed for the coexistence of cellular and WiFi systems in unlicensed spectrum. In the opportunistic method, the small cell base station opportunistically selects traffic offloading or resource sharing on each WiFi access point (AP). In the hybrid method, the base station simultaneously offloads users and shares the unlicensed spectrum of each AP. The performances of the proposed methods are analyzed and compared. We find that traffic offloading can achieve better performance than resource sharing when the number of existing WiFi users is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. Numerical results are presented to demonstrate the effectiveness of the proposed methods.

In this paper, we study the data offloading via device-to-device (D2D) communications with proactive caching in mobile cellular networks. The problem is formulated as the optimal data caching problem, where mobile nodes have different mobility and limited cache capacities and data files have different popularity and content sizes, and is proved to be NP-hard. To deal with the problem, the contacts between mobile nodes are used to adjust the cached data files with a well-designed (1 + α)-approximation algorithm. Based on the analysis, we first propose a distributed infrastructure-assisted data offloading algorithm (IADOA), where the base station (BS) needs to provide information to mobile nodes. To further increase the flexibility, a fully-distributed data offloading algorithm (FDDOA) is designed, where mobile nodes exchange control information via D2D communications and make parameter estimations. Finally, simulation results show that our proposed algorithms have significant performance gains compared with two conventional baseline algorithms.

Time-frequency resource conversion (TFRC) is a recently proposed network resource allocation strategy. By exploiting user behavior, it withdraws spectrum resources strategically from connection(s) not focused on by the user, to relieve network congestion effectively. Aiming at supporting the exponentially increasing traffic volume, especially on-demand data services, in this work we propose TFRC-based scheduling techniques. Considering an LTE-type cellular network, we formulate the problem of service scheduling as a joint request, channel, and slot allocation problem, which is a mixed integer nonlinear programming (MINLP) problem. A deflation and sequential fixing based algorithm with only polynomial-time complexity is proposed to solve the MINLP problem. Simulation results not only demonstrate the efficiency of the proposed algorithm in terms of quality-of-service (QoS) provisioning and network resource utilization, but also show the effectiveness of the proposed TFRC-based scheduling techniques when integrating with the existing scheduling strategies such as first in first served (FIFS) and earliest deadline first (EDF).

Network densification via deploying dense small cells is one of the dominant evolutions towards future cellular network to increase spectrum efficiency. Packet transmission delay and reliability in the resultant interference-limited heterogeneous cellular network (HCN) are essential performance metrics for system design. By modeling the locations of base stations (BSs) in HCN as superimposed of independent Poisson point processes, we propose an analytical framework to derive the timely throughput of HCN, which captures both the delay and reliability performance. In the analysis, the BS activity and temporal correlation of transmissions are taken into consideration, both of which have significant effect on network performance. The effect of mobility, BS density, and association bias factor is investigated through numerical results, which shows that network performance derived ignoring the temporal correlation of transmissions is optimistic.

With the foreseeable explosive growth of small cell deployment, backhaul has become the next big challenge in the next generation wireless networks in terms of capacity and latency, especially for delay-sensitive services and network functionalities. Heterogeneous backhaul deployment using different wired and wireless technologies may be a potential solution to meet this challenge. Therefore, it is cardinal to evaluate and compare the performance characteristics of various backhaul technologies as a means to understand the effect of backhaul on the total network performance. In this paper, we propose relevant backhaul models and study the delay performance of promising technologies, including fiber, xDSL, millimeter wave (mmWave), and sub-6 GHz, which have different characteristics. Numerical results are presented to show the delay performance characteristics of different backhaul solutions.

Distributive service data storage at the caching-enabled base stations (BSs) can reduce the traffic load in future cellular networks. Taking the limited caching space into account, it is necessary for the BSs to adjust their caching data based on service popularity in order to achieve better caching efficiency. In this paper, we investigate the cache replacement strategy for BSs to minimize the transmission cost between BSs in cellular networks. The cache replacement problem is modelled as a Markov Decision Process (MDP). Without extra information exchange about caching data between the BSs, we propose a distributed cache replacement strategy based on Q-learning. Especially, we calculate the transmission cost for possible cache replacement actions according to the previous data request and transmission between BSs. The convergence of the proposed distributed cache replacement strategy is proved by sequential stage game model. Simulation results verify the convergence of the proposed cache replacement strategy and show its performance gain compared to conventional strategies.

Rapid increase of large-scale high-rise structures results in three-dimensional distribution of wireless traffic, and thus raises a new demand of three-dimensional coverage of the common control signals in cellular mobile communication networks. The control approach of three-dimensional coverage is investigated in this paper, based on planar array of macro basestation and three-dimensional beamforming technology. A novel algorithm is proposed for obtaining the excitation weight matrix of planar array. Coverage requirement is extracted according to the target coverage distances and the actual landforms in different directions, and then mapped into an anisotropic desired array pattern. The optimization model is established as a joint minimization of array pattern design error and transmission power. An approximately optimal tradeoff coefficient and the corresponding weight matrix are solved through iteration. Numerical results validate that the proposed algorithm has the advantages of excellent coverage performance, considerable saving of transmission power and effective suppression of inter-cell interference.

Instant Messaging (IM) Service is an integrated service composed by multiple types of subservices, which have diverse performance requirements and evaluation criteria. IM service has drawn a lot of attention, yet a suitable Quality of Experience (QoE) model for IM service can rarely be found. In this paper, we propose a Representative Service (RS) based QoE modeling criterion for IM service. We discover that the QoE of IM service users mainly depends on the Representative Service Quality (RSQ) by exploiting the patterns of the IM service user behaviors. In the proposed RS based QoE model, the Normalized Quality (NQ) is defined for different subservices with various performance evaluation criteria to unify their qualities, and the Attention Factor (AF) is proposed to estimate the subservice that a user is focusing on. By performing a small-scale but illustrative subjective test, we verify the proposed QoE model and indicate the effectiveness of the model in tracing the IM service user behaviors.

In cognitive vehicular ad hoc networks (CVANETs), channel conditions are highly dynamic due to both vehicle mobility and primary user activity. In this paper, to support high-quality video playback in such a challenging scenario, an adaptive video streaming algorithm built on scalable video coding (SVC) is proposed for reducing interruption ratio and improving visual quality. The proposed streaming algorithm is capable of deciding the proper number of video layers for vehicle users, by taking into account several important factors including vehicle position, velocity, the activity of primary users. Simulation results demonstrate the superiority of the proposed algorithm on playback interruption ratio and visual quality over the compared algorithm.

To support delay-sensitive traffic in multi-channel cognitive radio systems, designing a channel access scheme faces two major challenges, namely, the long waiting time due to continuous channel occupancy of primary users (PUs) and the performance degradation due to transmission collisions among secondary users (SUs). To address both issues, we propose a two-phase channel access scheme, which consists of a distributed channel negotiation phase and a hopping-based channel access phase for each SU. Specifically, in its first phase, an SU attempts to negotiate a specific initial slot/channel differing from the ones chosen by other SUs. Then, in its second phase, the SU chooses a channel in each time slot in a hopping-based manner to transmit data, where the hopping starts from its initial channel and follows a common hopping sequence. Virtual channels are introduced to accommodate the situation when the number of SUs is larger than that of actual channels. The average maximal waiting time due to the channel negotiation phase is derived, and the effective capacity of the service process for each SU in the channel access phase is analyzed. Numerical results show that the proposed scheme can support a higher traffic load under the statistical delay constraint, as compared with fixed or random channel access schemes.

In this paper, to address network congestion stemmed from traffic generated by advanced user equipment, we propose a novel network resource allocation strategy, i.e., time-frequency resource conversion (TFRC), via exploiting user behavior, a specific kind of context information. The key idea is to use radio resources mainly on the traffic/connection to which a user pays attention. The TFRC withdraws spectrum resources strategically from connection(s) not focused on by the user, providing reuseable spectrum called “virtual spectrum hole”. Considering an LTE-type cellular network, a double-threshold guard channel policy is proposed to facilitate the implementation of TFRC. An analytical model is established to study benefits of exploiting TFRC in terms of call-level performance, including new call blocking, handoff call dropping, and recovering call dropping probabilities. Numerical results demonstrate the effectiveness of the proposed approach, in increasing the cell capacity (maximum user number per cell) while limiting potential service quality degradation introduced by the newly proposed technique.

In cellular networks, the proactive storage at caching-enable BSs is an efficient way to reduce traffic load of backhaul links. In this paper, we investigate the storage allocation problem with network coding. By decomposing the NP-hard problem, we propose a low-complexity storage allocation to solve two subproblems in an iterative way. Combining a heuristic initial allocation scheme and the iterative process, we can obtain the storage allocation result very close to the optimal solution. The convergence of the proposed algorithm is proved and its computation complexity is analyzed. The simulation results evaluate the performance of the proposed algorithm, which has graceful performance degradation on total storage with much lower complexity.

In multi-channel multi-user cognitive radio systems, there are two main challenges to support traffic with delaysensitive QoS requirements, namely, the coordination among the secondary users (SUs) and the long channel occupancy time of the primary users (PUs). In this paper, we propose a hopping-based channel access scheme for SUs to improve their performance. Each SU chooses one of the channels allocated to PUs in a hopping-based manner in each time slot to opportunistically sense and transmit. This scheme not only avoids collisions among SUs, but also reduces the effect of the long channel occupancy time of PUs on the QoS performance of the SUs' delay-sensitive applications. We analyze the effective capacity of SU's service process with our proposed scheme. Numerical results show that our proposed scheme can achieve a higher effective capacity compared to random or fixed channel allocation.

Cooperative multicast is an effective solution for the bottleneck problem of single-hop broadcast in wireless networks. By incorporating with the random linear network coding technique, the previously proposed schemes can reduce the number of retransmissions significantly. However, these schemes may incur a large decoding delay at the receivers, In addition, the centralized scheduling methods of these schemes depend on the explicit feedback mechanism, which is not practical for a large size network. In this paper, we address the decoding delay and feedback storm problems in cooperative multicast. A cooperative multicast protocol named MWNCast is proposed based on a novel moving window network coding technique. Theoretical models are developed for analyzing the performance of the proposed scheme. Simulation results show that MWNCast outperforms the existing schemes by achieving better tradeoff between the throughput and decoding delay, meanwhile keeping the packet loss probability and decoding complexity at a very low level without explicit feedback.

In vehicular ad hoc networks (VANETs), improving uploading efficiency is crucial to enabling the copious applications such as reporting sensed data for traffic management or environment monitoring. Depending on the applications, the contents to be uploaded can be of large volumes. Therefore, there exist the fundamental demands of the delivery with high throughput. In this paper, we derive the achievable throughput capacity scaling law for such applications in VANETs as Θ(1/log n), with the number of road-side units scaling as Θ(n/log n). Furthermore, by exploring the mobility diversity among vehicles, we propose a novel two-hop forwarding scheme to improve the throughput performance approaching the throughput capacity. Specifically, the source vehicle distributes the contents to multiple relay vehicles with the largest mobility diversity so that the number of concurrent transmissions can be increased. The simulation results demonstrate the effectiveness of the proposed transmission scheme in terms of the increased throughput performance.

Network coding techniques have absorbed much attention for providing reliable broadcasting services in wireless networks. However, the intrinsic tradeoff among throughput, decoding delay, and feedback overhead has obstructed the application of the previously proposed schemes in practice. In this paper, we firstly propose a rate-controlled network coding scheme (RANC), which can effectively reduce the decoding delay of the receiver suffering from a poor channel condition, without compromising the system throughput. Based on RANC, we further propose a moving window network coding scheme together with an early loss alarm mechanism (MWNC-ELA), which achieves similar decoding delay performance to RANC, but greatly simplifies its feedback mechanism. As a benchmark of MWNC-ELA, we analyze the decoding delay performance of RANC using the random walk theory. Simulation results show that the proposed schemes outperform the existing solutions in terms of throughput, decoding delay, and feedback overhead.

In this paper, we propose a stopping rule-driven channel access scheme for a secondary user pair in multichannel cognitive radio networks (CRNs), aiming to achieve a desired tradeoff between channel sensing and channel access. In the proposed approach, we first formulate the sensing-access tradeoff problem as a 1-stage look-ahead stopping problem. We then derive two stopping conditions, namely power-limited stopping and bandwidth-limited stopping, whereby a desired tradeoff between sensing overhead and throughput increase can be achieved. Once a stopping condition is reached, a secondary user pair stops sensing and starts accessing previously sensed free channels for packet transmission. Simulation results show that, in the case of perfect sensing, the proposed approach outperforms a greedy approach by at least 80% in terms of throughput. Imperfect sensing and its impact are also addressed and evaluated.

In this paper, we investigate the energy efficiency of cooperative communications in wireless body area network (WBAN). We first analyze the outage performance of three transmission schemes, namely direct transmission, single-relay cooperation, and multi-relay cooperation. To minimize the energy consumption, we then study the problem of optimal power allocation with the constraint of targeted outage probability. Two strategies of power allocation are considered: power allocation with and without posture state information. Simulation results verify the accuracy of the analysis and demonstrate that: 1) power allocation making use of the posture information can reduce the energy consumption; 2) within a possible range of the channel quality in WBAN, cooperative communication is more energy efficient than direct transmission only when the path loss between the transmission pair is higher than a threshold; and 3) for most of the typical channel quality due to the fixed transceiver locations on human body, cooperative communication is effective in reducing energy consumption.

In this paper, we study medium access control (MAC) protocol design for distributed cooperative wireless networks. We focus on beneficial node cooperation by addressing two fundamental issues of cooperative communications, namely when to cooperate and whom to cooperate with, from a cross-layer protocol design perspective. In the protocol design, taking account of protocol overhead we explore a concept of cooperation region, whereby beneficial cooperative transmissions can be identified. We show that a rate allocation in the cooperation region provides higher link utilization than in a non-cooperation region. To increase network throughput, we propose an optimal grouping strategy for efficient helper node selection, and devise a greedy algorithm for MAC protocol refinement. Analysis of a successful transmission probability with cooperative or direct transmission is presented. Simulation results show that the proposed approach can effectively exploit beneficial cooperation, thereby improving system performance. Further, analytical and simulation results shed some light on the tradeoff between multi-user diversity gain at the physical layer and the helper contention overhead at the MAC layer.

In this paper, we study two fundamental issues of cooperative communications in distributed wireless networks, namely when to cooperate and whom to cooperate with. In specific, we focus on cross-layer medium access control (MAC) protocol design with beneficial node cooperation. To increase long-term network throughput, we propose an optimal grouping strategy for relay node selection, and devise a greedy algorithm for MAC protocol parameter refinement. Quantitative relationships among the channel state, payload length, protocol overhead, and cooperation gain are illustrated. Simulation results show that the proposed approach can effectively exploit beneficial cooperation, thereby improving system performance.

In this paper, we investigate benefits of cooperative communication in mobile ad hoc networks (MANETs). Cooperative communication as an effective way to mitigate channel impairments has attracted much attention, especially on the physical layer. However, without properly designed higher- layer protocols, the cooperation gain can decrease and even disappear, due to factors such as limited payload and nonnegligible overhead. A two-hop interference model from a medium access control (MAC) point of view is proposed to study the performance of a cooperative network. Analysis based on the model demonstrates that cooperation may not be beneficial when the number of blocked nodes increases. Further, a busy- tone based cooperative MAC scheme is presented to investigate the gain from cooperative communication and the relationship among influential factors. Simulation results demonstrate that the node density and traffic load greatly impact the effectiveness of cooperative communication.

Multi-user multiple-input multiple-output (MIMO) systems have been emerging and attracting considerable attention recently for its potential to substantially improve system capacity via space division multiple access. In this paper, we propose a distributed multi-user (MU) medium access control (MAC) protocol for wireless local area networks (WLANs) with MIMO capability, using a leakage-based preceding scheme. By exploiting the multi-user degree of freedom in a MIMO system to allow the access point (AP) to communicate with multiple users in the same frequency band simultaneously, the proposed MU MAC can effectively minimize the AP-bottleneck effect in legacy WLANs. We then develop an analytical model to study the performance of the proposed MU MAC, in terms of the maximum number of users that can be supported and the network throughput. The analysis and simulation results show that the proposed MU MAC significantly outperforms the single-user MAC.

In this paper, with the cross-layer design principle, a novel cooperative triple busy tone multiple access (CTBTMA) scheme is proposed for wireless networks to achieve cooperative diversity gain. A utility-based algorithm is presented to determine the capability of a node in helping other nodes' transmissions. With the use of three busy-tone channels, not only collisions can be avoided, but also an optimal helper can be determined without disturbing existing transmissions. Simulation results demonstrate that the proposed scheme can effectively increase the throughput in a low SNR environment, as compared with IEEE 802.11a single-hop transmissions. On the other hand, transmit power can be greatly reduced in the proposed scheme in order to achieve the same throughput as in the single-hop transmissions.