Although the latest artificial intelligence technologies can greatly improve work efficiency by automatically generating feasible solutions in the digital world (DW), they are incapable of discovering or creating new knowledge, i.e., lack of human intelligence or creativity. To break this limitation, this article describes and elaborates the masterplan of the digital intelligent world (DIW), wherein everyone has an intelligent agent (IA) for searching, exchanging, and processing information and knowledge autonomously. First, a data-information-knowledge-intelligence (DIKI) model is proposed to illustrate the challenges of creating intelligence from raw data, and of realizing the DIW from the DW. Specifically, the DIW adopts knowledge-driven approaches and could achieve huge productivity enhancement through cross-domain innovations and deep intelligentization with broader creativity. Second, at the individual level, a knowledge processing architecture of IA is defined to support knowledge-centric operations and services. Third, at the system level, a framework of knowledge market (KM) is established for fair, effective, and autonomous collaborations among massive IAs. Inspired by basic laws in statistical thermodynamics, information sciences, and economics, three fundamental principles are developed and discussed for guaranteeing a prosperous KM and the sustainable DIW.

Intelligent reflecting surfaces (IRSs) mounted on maneuverable aerial platforms to form aerial IRS (AIRS) relays represent a novel paradigm for large-scale downlink transmission in smart cities. However, the challenge of multivariate dynamic coupling hinders most existing studies due to high computational complexity and limited scalability. To address these issues, this paper proposes a soft-attention-actor-critic (SAAC) optimization framework that efficiently decomposes the joint optimization of multi-AIRS deployment, passive beamforming, and active beamforming at the base station into two sequential subproblems. The objective is to maximize average downlink spectral efficiency and service fairness, while minimizing deployment energy consumption. In the first stage, a conservative lower bound of spectral efficiency is formulated to guide multiple AIRSs toward near-optimal deployment positions. In the second stage, refined optimization is performed for both passive and active beamforming matrices. Furthermore, multi-head attention modules are incorporated into the critic and actor networks in each phase, enabling AIRS to adaptively attend to the observations and actions of other agents, and enhancing the ability to handle high-dimensional observation-action spaces. Extensive simulation results validate that the proposed SAAC framework consistently outperforms mainstream deep reinforcement learning baselines across diverse network conditions, highlighting its superior performance and scalability.

Mobile users can offload their tasks to adjacent edge servers to enhance service quality. These servers require suitable reimbursements to cover the operational and energy consumption costs incurred while assisting with offloaded tasks. Although previous studies have examined market mechanisms for multiple users offloading tasks to multiple servers, most of them have not investigated the market mechanism for splittable task offloading, where tasks can be divided into multiple subtasks and offloaded to multiple servers. In this work, we propose a novel edge computing marketplace that focuses on splittable task offloading in multi-user multi-server scenarios with the aim of maximizing social welfare. Designing such a marketplace presents several challenges. First, the problem of task and computing resource division introduced in this context results in a complex solution space, and the division decisions are interdependent. Second, the users and edge servers have conflicting objectives and hidden utility/cost information. To overcome these challenges and achieve socially optimal market operation, we devise an Iterative DoublE Auction (IDEA) mechanism.IDEAemploys a broker to facilitate the interactions between users and edge servers and induces truthful reporting of hidden information through iterative updates to the allocation and pricing rules. Rigorous theoretical analysis and extensive simulations demonstrate the effectiveness of the proposedIDEAmechanism in achieving optimal social performance.

Vehicular edge computing (VEC) enhances computational efficiency by strategically offloading tasks from vehicles to edge servers. Integrating semantic communication into vehicular networks introduces further benefits by leveraging semantic information to reduce task transmission delays. However, semantic-aware computation offloading encounters dual challenges: adaptively selecting semantic features to preserve task-critical meaning and dynamically allocating communication and semantic resources under varying network conditions. To cope with these challenges, we propose an importance-based hybrid-action multi-agent proximal policy optimization (I-HAMAPPO) algorithm for the semantic-aware vehicular computation offloading system in this paper. By assessing the importance scores of semantic features, an importance evaluation module (IEM) is designed to selectively transmit task-relevant information. A utility function, integrating task delay, energy consumption, and semantic similarity, is developed to provide a multi-dimensional performance evaluation of the system. Subsequently, the optimization problem is formulated with the objective of maximizing system utility by optimizing communication resources and semantic compression ratios. Considering the presence of mixed decision variables in the formulated problem, we employ the proposed I-HAMAPPO algorithm to optimize the continuous and discrete actions jointly. Based on real-world vehicle trajectories from the highD dataset, extensive experimental results demonstrate the convergence of I-HAMAPPO and its efficacy in maximizing system utility.

The distributed deep learning architecture between front-deployed sensors and edge-deployed gateways attracts increasing interest. However, the inference performance of distributed deep models is also impacted by the delivery loss of intermediate representation in the wireless link, especially in the harsh industrial fading environments. Traditional communication systems usually focus on transmission errors at bit level, which treat all bits in the packets equally and fail to suit the varying importance in distributed deep models, which urges the essential evolution of the communication method to form a joint co-design paradigm for distributed deep models. This article then proposes to optimize the Mean Time To First Failure (MTTFF) of wireless link instead of traditional bit error rate, which enables a guaranteed transmission window. This paper first derives the analytical model of MTTFF under MIMO systems, then utilizes the kernel mixture distribution to obtain a closed-form solution of MTTFF, which forms a optimization algorithm minimizing the transmitted power while achieving the aiming MTTFF. Extensive reallife experiments show more than 70% satisfaction rate of MTTFF, which leads to more than 10 times higher inference accuracy than the original deep model.

Spatial-temporal causal time series (STC-TS) involve region-specific temporal observations driven by causally relevant covariates and interconnected across geographic or network-based spaces. Existing methods often model spatial and temporal dynamics independently and overlook causality-driven probabilistic forecasting, limiting their predictive power. To address this, we propose STOAT (Spatial-Temporal Probabilistic Causal Inference Network), a novel framework for probabilistic forecasting in STC-TS. The proposed method extends a causal inference approach by incorporating a spatial relation matrix that encodes interregional dependencies, enabling spatially informed causal effect estimation. The resulting latent series are processed by deep probabilistic models to estimate the parameters of the distributions, enabling calibrated uncertainty modeling. Experiments on COVID-19 data across six countries demonstrate that STOAT outperforms state-of-the-art probabilistic forecasting models in key metrics, particularly in regions with strong spatial dependencies. By bridging causal inference and geospatial probabilistic forecasting, STOAT offers a generalizable framework for complex spatial-temporal tasks, such as epidemic management.

Crack segmentation is pivotal for structural health monitoring, enabling the timely maintenance of critical infrastructure such as bridges and roads. However, existing deep learning models are often too computationally intensive for deployment on resource-constrained devices. To address this limitation, we introduce UltraFastCrackSeg, a lightweight model designed for real-time crack segmentation that effectively balances high accuracy with low computational demands. Featuring an efficient encoder-decoder architecture, our model significantly reduces parameter count and floating-point operations (FLOPs) compared to current methods, as illustrated in Figure 1. We further enhance performance through a self-supervised pretraining approach that employs a novel, task-oriented masking strategy, thereby improving feature extraction. Experiments across multiple datasets demonstrate that UltraFastCrackSeg achieves state-of-the-art Intersection over Union (IoU) and F1 scores while maintaining a compact model size and high inference speed. Evaluations on a low-power CPU device confirm its capability to achieve up to 80 frames per second (FPS) with ONNX runtime optimization, making it highly suitable for real-time, on-site applications. These findings establish UltraFastCrackSeg as a robust and efficient solution for practical crack detection tasks. Code is available at: https://github.com/weiqingq/UltraFastCrackSeg.

Scene understanding enables intelligent agents to interpret and comprehend their environment. While existing large vision-language models (LVLMs) for scene understanding have primarily focused on indoor household tasks, they face two significant limitations when applied to outdoor large-scale scene understanding. First, outdoor scenarios typically encompass larger-scale environments observed through various sensors from multiple viewpoints (e.g., bird view and terrestrial view), while existing indoor LVLMs mainly analyze single visual modalities within building-scale contexts from humanoid viewpoints. Second, existing LVLMs suffer from missing multidomain perception outdoor data and struggle to effectively integrate 2D and 3D visual information. To address the aforementioned limitations, we build the first multidomain perception outdoor scene understanding dataset, named SVM-City, deriving from multi-Scale scenarios with multi-View and multi-Modal instruction tuning data. It contains 420k images and 4, 811M point clouds with 567k question-answering pairs from vehicles, low-altitude drones, high-altitude aerial planes, and satellite. To effectively fuse multimodal data in the absence of one modality, we introduce incomplete multimodal learning to model outdoor scene understanding and design the LVLM named City-VLM. Multimodal fusion is realized by constructed as a joint probabilistic distribution space rather than implementing directly explicit fusion operations (e.g., concatenation). Experimental results on three typical outdoor scene understanding tasks show City-VLM achieves 18.14 % performance surpassing existing LVLMs in question-answering tasks averagely. Our method demonstrates pragmatic and generalization performance across multiple outdoor scenes.

Reliable identification of gunshot events is crucial for reducing gun violence and enhancing public safety. However, current gunshot detection and recognition methods are still affected by complex shooting scenarios, various nongunshot events, diverse firearm types, and scarce gunshot datasets. To address these issues, based on triaxial acceleration of guns, a novel general deep transfer learning approach is proposed for gunshot detection and recognition, which combines a temporal deep learning model with transfer learning and automated machine learning (AutoML) to improve the accuracy, reliability and generalization performance. First, a new gunshot recognition model named as MobileNetTime is proposed for the two-class gunshot event detection, three-class coarse firearm recognition, and 15-class fine firearm recognition, which utilizes 1-D convolution and inverted residual modules to autonomously extract higher-level features from the time series acceleration data. Second, considering the impact of nongunshot events, the AutoML is employed for model fine tuning, to transfer the pretrained MobileNetTime from the handgun to various firearm types. In addition, we propose a low-power versatile gunshot recognition system framework employing a triaxial accelerometer for both of wrist-worn and gun-embedded scenarios, which adopts a two-stage wake-up mechanism that selectively monitors gunshot events using temporal and spectral energy features. The experimental results on the two gunshot datasets DGUWA and GRD show that the proposed model can achieve up to 100% accuracy on the DGUWA dataset and 98.98% accuracy on the GRD dataset for the two-class gunshot detection. Moreover, the proposed deep transfer learning approach achieves a 98.98% accuracy for 16-class firearm classification, which is 6.21% higher than the model without transfer learning.

The integration of sensing, communication, and computation (ISCC) is a critical technology that will support various emerging wireless services in future 6G networks. The unmanned aerial vehicles (UAVs) equipped with edge servers can be used as an aerial service platform in intelligent transportation systems (ITSs) to offer ISCC services to vehicles. This article studies an aerial UAV network comprising a central UAV and secondary UAVs to realize sensing of the global ITS environment and data fusion computation through collaborative UAVs. To enhance the service performance of ISCC, we maximize the success rate of ISCC services and the energy efficiency of UAVs by jointly optimizing bandwidth allocation, power allocation, and computing capacity control while ensuring the sensing and data processing latency requirements. Leveraging the network architecture and collaboration requirements of UAVs, we propose the multi-UAV collaborative Air-ISCC (MCAI) algorithm based on the asynchronous advantage actor-critic algorithm, which obtains the optimal ISCC service policy by co-training a deep reinforcement learning model with multiple UAVs. Sufficient experimental results show that MCAI enhances energy efficiency by 10.51% to 80.12% compared with the baselines. Moreover, MCAI exhibits good scalability, strengthening its feasibility in real scenarios.

The rapidly evolving Internet of Things (IoT) continues to serve as a critical bridge between the physical world and digital space, driving increasing demands for optimized communication capabilities and time-sensitive data acquisition. However, traditional cloud computing architectures are becoming increasingly insufficient to meet these stringent demands. Mobile Edge Computing (MEC) has thus emerged as a promising paradigm. By delegating data processing tasks from centralized cloud servers to edge nodes (ENs) located near end-users, MEC significantly enhances service efficiency while simultaneously introducing heightened privacy and security risks. To mitigate both external threats during task interactions and internal adversaries within the network, this paper proposes a Lightweight Verification and Protection (LVP) strategy for ENs. LVP primarily leverages secure computation techniques to ensure the anonymity and confidentiality of private information during transmission and verification. It further incorporates a reputation-based evaluation framework to mitigate the impact of insider threats. Specifically, the integrity of user-uploaded task data is first verified, followed by anonymous matching between users and ENs through paired index. A reputation-based anonymous authentication algorithm is then designed to prevent exposure or linkage of reputation information during usage. Finally, reputation values are dynamically updated by jointly considering temporal relevance and historical behavior. Theoretical analysis confirms the robustness and correctness of all LVP sub-algorithms, while experimental evaluations demonstrate the scheme’s effectiveness and feasibility under low computational and communication overhead.

The converging trends of reinforcement learning (RL) control and cloud-fog automation in industrial cyber-physical systems impose multiple challenges for communications to cope with stringent requirements in latency, reliability, control effectiveness and bifurcating user demands. Progressive goal-oriented (GO) communication is a promising technology to tackle the above challenges. This paper takes a two-step approach to design the first progressive codec of GO communications tailored for RL control tasks. The first step is to design a variable-rate coding scheme that extends the boundaries of rate regimes. This step is achieved by empowering the hierarchical variational autoencoder (HVAE) framework with novel algorithms such as mutual information based soft state abstraction (MISA). The second step is to transform variable-rate encoding into progressive encoding. This is achieved by applying residual-based encoding techniques upon latent representations learned by deep neural networks. Experiments on the Cartpole Swingup task demonstrate that the proposed progressive codec can facilitate smooth transitions from the ultra-low rate regime to regular rate regime, while achieving the state-of-the-art performance in terms of rate-distortion-effectiveness tradeoff.

Abstract The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

Cloud-Edge Collaborative Architecture (CEA) is a prominent framework that provides low-latency and energy-efficient solutions for video stream processing. In Cloud-Edge Collaborative Video Streaming Systems (CEAVS), efficient online offloading strategies for video tasks are crucial for enhancing user experience. However, most existing works overlook budget constraints, which limits their applicability in real-world scenarios constrained by finite resources. Moreover, they fail to adequately address the heterogeneity of video task redundancies, leading to suboptimal utilization of CEAVS's limited resources. To bridge these gaps, we propose an Efficient Online Computing framework for CEAVS (EOCA) that jointly optimizes accuracy, energy consumption, and latency performance through adaptive online offloading and redundancy compression, without requiring future task information. Technically, we formulate computing offloading and adaptive compression under budget constraints as a stochastic optimization problem that maximizes system satisfaction, defined as a weighted combination of accuracy, latency, and energy performance. We employ Lyapunov optimization to decouple the long-term budget constraint. We prove that the decoupled problem is a generalized ordinal potential game and propose algorithms based on generalized Benders decomposition (GBD) and the best response to obtain Nash equilibrium strategies for computing offloading and task compression. Finally, we analyze EOCA's performance bound, convergence rate, and worst-case performance guarantees. Evaluations demonstrate that EOCA effectively improves satisfaction while effectively balancing satisfaction and computational overhead.

In the evolution of the Internet of vehicles (IoV), the increasing demand for vehicular computation tasks presents significant challenges, particularly in the context of constrained local computation resources and high processing delays. To mitigate these challenges, multi-access edge computing (MEC) offers a potential solution by leveraging edge servers for lowlatency processing. However, it also encounters issues such as sub-channel competition and workload imbalance owing to the uneven distribution of vehicle densities. This paper introduces a novel IoV architecture that incorporates multi-task and multi-roadside unit (RSU) capabilities, enabling edge-toedge collaboration for efficient task offloading among RSUs. The optimization problem is formulated with the objective of minimizing the overall task delay, which is further divided into two sub-problems: communication resource allocation and load balancing. Considering the non-deterministic polynomial (NP)- hard nature of these sub-problems, we propose a two-stage deep reinforcement learning-based communication resource allocation and load balancing (DRLCL) algorithm to address them sequentially. Based on realistic vehicle trajectories, comprehensive evaluation results demonstrate the superiority of the proposed algorithm in reducing system delay compared to existing stateof-the-art baselines, offering an effective approach for optimizing the performance of vehicular edge computing (VEC) networks.

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connectsupplyanddemand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On amacroscopiclevel, it impacts the power grid's peak load and reliability, whilelocally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2.

In urban environments, direct communication links between a base station (BS) and user equipment (UEs) are often obstructed by buildings. To mitigate these blockages, we integrate unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance system flexibility and improve transmission efficiency. This paper investigates an RIS-assisted multi-user multiple-input single-output (MU-MISO) downlink system, where the RIS is mounted on a UAV. To maximize the system rate while minimizing the UAV's energy consumption and flight duration, we formulate a multi-objective optimization problem. To address this problem, we propose a hybrid algorithm that integrates the soft deep deterministic policy gradient (SD3) algorithm with a graph neural network (GNN) architecture, named SD3-GNN-RIS. The original problem is decomposed into two subproblems: joint active beamforming at the BS and passive beamforming at the RIS, optimized via a GNN-based approach, and three-dimensional (3D) UAV trajectory optimization, formulated as a Markov decision process and solved using the SD3 algorithm. Simulation results demonstrate the superior performance of the proposed algorithm compared to baseline methods in terms of system rate, energy efficiency, and UAV trajectory optimization.

To efficiently execute tasks, computation resource requesters (CRRs) with limited resources can offload their tasks to nearby computation resource providers (CRPs) with spare computing capacity. These CRPs require appropriate incentives to compensate for their incurred costs when helping process the offloaded tasks. Although several mechanisms have been designed to incentivize CRPs, none of them have investigated the incentive mechanism considering price-setting and price-taking CRPs simultaneously. In this work, we propose an incentive framework for task offloading in the edge computing marketplace that includes both price-setting and price-taking CRPs. We model the CRR's interactions with both types of CRPs as a three-stage Stackelberg game to maximize the profit for both the CRR and CRPs. We prove the existence of a unique subgame perfect equilibrium (SPE) of the formulated game and further develop iterative algorithms for the CRR and price-setting CRPs to achieve the equilibrium. Through the designed algorithms, each CRP does not require complete information about the CRR and other CRPs. Extensive simulations demonstrate that offloading tasks to both price-setting and price-taking CRPs achieves higher profits for the CRR and price-setting CRPs compared to offloading tasks solely to price-setting CRPs. Additionally, the obtained SPE can achieve near-optimal social welfare.

Edge computing significantly enhanced the growth of edge-assistant video streaming applications. However, challenges such as unpredictable wireless conditions, resource constraints, and task redundancy have intertwined impacts on the overall performance of edge video streaming systems (EVS). Therefore, it is essential to have an integrated framework that addresses resource management, computational offloading, and video task preprocessing. Existing optimization strategies often neglect the simultaneous management of computational offloading, resource allocation, and video task preprocessing, leading to a suboptimal system utility. Moreover, they struggle to handle high-dimensional decision variables. On the other hand, learning-based adaptive schemes fall short in integrating distributed decisions and ensuring the scalability of wireless devices. Additionally, current approaches lack adaptive incentives. To bridge these gaps, we propose a novel framework called AIRA, which is based on improved multi-agent reinforcement learning (MARL) and smart contracts. AIRA manages resources, video compression, and adaptive incentives in a distributed manner. It consists of a MARL-driven cooperative learning algorithm (CLA) and a smart contract-guided adaptive incentive mechanism. Leveraging an actor-critic structure, the CLA enables wireless devices to master strategies for resource allocation, video task compression, and offloading, utilizing historical data. Notably, the CLA incorporates an attention mechanism to select pivotal tuples from the observation-action pairings among different agents, ensuring improved scalability and computational prowess. Evaluations based on real-world trajectories demonstrate that AIRA enables adaptive incentives. Compared to state-of-the-art approaches, CLA effectively enhances the long-term system utility and scalability of EVS.

In modern wireless communication systems, a profound grasp of the channel impulse response (CIR) is pivotal for optimizing the design and functionality of algorithms and systems, especially for multiple-input-multiple-output (MIMO) technology. Conventional methods for gauging and modeling the channels rely on evaluating spatial points sampled discretely, resulting in limitations in acquiring pertinent channel information across a broad region. To overcome these limitations, this study embeds the physical principles of electromagnetic wave propagation into data-driven deep learning models, achieving second-level regional CIR computing efficiency that is hundreds of times faster. The proposed physics-informed deep ray tracing network (PIDRTN) integrates multiple U-shaped network (U-Net) encoder-decoder blocks, capturing radio wave propagation characteristics within a specific region surrounded by buildings, including two equivalent signal propagation directions in a two-dimensional space and a signal intensity correction term. Then, the network employs a parameter-free nonlinear signal transmission module to emulate the physical principles of signal propagation and obtain accurate CIRs from limited anchor locations, which will iteratively generate CIRs for various times within a specified region subjected to enhancement and denoising operations. Furthermore, the PIDRTN-A model, which utilizes anchor data to improve model accuracy, is proposed. A dataset encompassing diverse fading scenarios is constructed using the ray tracing (RT) method. Extensive experiments demonstrate that the proposed models effectively capture directional and reflective properties of signals; using the RT model as a benchmark, normalized root mean squared errors (NRMSEs) of 0.1226 and 0.0969 are obtained for the PIDRTN and PIDRTN-A models, respectively.

Accurate online link quality metrics represented by the Rician parameter are critical to enhancing the reliability of industrial wireless networks subject to temporal fading channels. The Rician parameters can be estimated by fitting the received I/Q symbols with GMM (Gaussian Mixture Model). However, the classical Expectation-Maximization estimations of GMM rely on the preset hyper-parameter of kernel numbers to guarantee the convergence, making it hard to work under adaptive modulation schemes. To address this challenge, we first reveal that the derivative of likelihood is less capable of representing the global optimal, which leads to the well-known local optimal problem and the failure to recognize the false convergence caused by incorrectly configured kernel numbers. A new empirical metric derived from KLD (Kullback-Leibler divergence) has been proposed to identify the local optimal convergence, as well as a new metric tuple to discriminate redundant kernels. A novel estimation algorithm has then been designed to shift the number of kernels from the preset hyper-parameter to the adjustable parameter. This improvement guarantees the global optimal convergence of the GMM with any initial number of kernels. Extensive experiments demonstrate that the proposed method achieves over ten times better accuracy, while requires less than half the iterations.

Designing an everyone-centric customized service system is a crucial stage in the intelligent transformation of the digital world. As such, Quality of Experience (QoE) for user requirements design has become an essential research topic. Among the existing works, part of them assumes user requirements through ideal distributions. Another portion uses real-world data, but they analyze it from the system side. Both do not give a pervasive description of user requirements to support future research efforts. To tackle the above challenges, based on the Service Requirements Zone (SRZ), we propose an extended integrated multi-dimensional QoE, Acceptable Performance Zone (APZ), which includes both preferred and acceptable user requirements. We also detail the eight Key Performance Indicators (KPIs) in the SRZ. To provide more math support to the user requirements, we adopt real-world cluster trace datasets from Alibaba for analysis, aiming to explore the characteristics of real-world user requirements. The results show that the characteristics of user tasks generally obey some specific distributions. Specifically, the task size and memory requirement both follow bimodal log-Gaussian distributions, whereas the delay and computing requirements follow unimodal log-Gaussian distributions. At the same time, the energy consumption follows the log-beta distribution.

Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level retrieval-based models, such as RIM, have demonstrated remarkable performance. However, they face challenges including inference inefficiency and high resource consumption due to the retrieval process, which hinder their practical application in industrial settings. To address this, we propose a universal plug-and-play retrieval-oriented knowledge (ROK) framework that bypasses the real retrieval process. The framework features a knowledge base that preserves and imitates the retrieved & aggregated representations using a decomposition-reconstruction paradigm. Knowledge distillation and contrastive learning optimize the knowledge base, enabling the integration of retrieval-enhanced representations with various CTR models. Experiments on three large-scale datasets demonstrate ROK's exceptional compatibility and performance, with the neural knowledge base serving as an effective surrogate for the retrieval pool. ROK surpasses the teacher model while maintaining superior inference efficiency and demonstrates the feasibility of distilling knowledge from non-parametric methods using a parametric approach. These results highlight ROK's strong potential for real-world applications and its ability to transform retrieval-based methods into practical solutions. Our implementation code is available to support reproducibility1.

This paper presents an intelligent reflecting surface (IRS)-assisted data harvesting scheme for unmanned aerial vehicle (UAV) networks. This scheme leverages the high maneuverability of the UAV and the channel gain enhancement from the IRS. By jointly optimizing the UAV trajectory and the IRS phase shift, we aim to minimize the completion time of data harvesting missions. Specifically, we devise a softmax operator applicable to deterministic policy gradients and propose a softmax deep double deterministic policy gradients (SD3) method to facilitate the design of three-dimensional trajectory for UAV. In addition, we propose a practical coherent combining (CC) strategy for IRS phase control. Simulation results demonstrate that the proposed SD3-CC algorithm surpasses other mainstream baseline methods relying on deep reinforcement learning (DRL).

In the rapidly evolving field of heterogeneous computing networks, efficient task offloading plays a pivotal role in optimizing system throughput and resource utilization. However, existing task offloading methods often fall short of adequately modeling the dependency topology relationships between of-floaded tasks, which limits their effectiveness in capturing the complex interdependencies of task features. To address this limitation, we propose a framework named MOGR: Multi-task Offloading via Graph Representation. Our modeling approach takes into account factors such as task characteristics, network conditions, and available resources at the edge, and embeds these captured features into the graph structure. By utilizing Graph Convolutional Networks (GCN), our mechanism can capture and analyze the intricate relationships between task features, enabling a more comprehensive understanding of the underlying dependency topology. Through extensive evaluations in heteroge-neous networks, our proposed algorithm improves 15.1%-30.5% over greedy and approximate algorithms in optimizing system throughput and resource utilization. Our experiments showcase the advantage of considering the intricate interplay of task features using GCN-based modeling.

Cloud-edge collaboration Architecture (CEA) enables the co-training of AI models by cloud servers and edge servers, offering a promising solution for large-scale model training. An efficient data trading mechanism helps encourage edges to invest data resources to participate in training while reducing the cost of cloud servers. Existing research on data trading within CEA focuses on static scenarios, either overlooking the dynamics of data demand and the fairness of the selected edges or assuming unknown future communication overheads. To bridge these gaps and consider the long-term fairness constraints, we propose an Online Data Trading mechanism for the CEA, called ODT, to improve the long-term utility. Technically, ODT decouples the long-term fairness constraint into a series of single time-slot sub-problems using the Lyapunov optimization method and applies dynamic programming to solve the single time-slot edge selection sub-problems. We prove the NP-hardness of the sub-problems, the performance bounds, and the computational complexity of the proposed algorithm. Evaluation results demonstrate that the proposed mechanism effectively improves long-term utility and achieves an efficient trade-off between fairness and utility.

3D multimodal question answering (MQA) plays a crucial role in scene understanding by enabling intelligent agents to comprehend their surroundings in 3D environments. While existing research has primarily focused on indoor household tasks and outdoor roadside autonomous driving tasks, there has been limited exploration of city-level scene understanding tasks. Furthermore, existing research faces challenges in understanding city scenes, due to the absence of spatial semantic information and human-environment interaction information at the city level.To address these challenges, we investigate 3D MQA from both dataset and method perspectives. From the dataset perspective, we introduce a novel 3D MQA dataset named City-3DQA for city-level scene understanding, which is the first dataset to incorporate scene semantic and human-environment interactive tasks within the city. From the method perspective, we propose a Scene graph enhanced City-level Understanding method (Sg-CityU), which utilizes the scene graph to introduce the spatial semantic. A new benchmark is reported and our proposed Sg-CityU achieves accuracy of 63.94 % and 63.76 % in different settings of City-3DQA. Compared to indoor 3D MQA methods and zero-shot using advanced large language models (LLMs), Sg-CityU demonstrates state-of-the-art (SOTA) performance in robustness and generalization.

Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization. Recent studies have shown that introducing posterior auction information contributes to the performance of CTR prediction. However, existing work doesn’t fully capitalize on the benefits of auction information and overlooks the data bias brought by the auction, leading to biased and suboptimal results. To address these limitations, we propose Auction Information Enhanced Framework (AIE) for CTR prediction in online advertising, which delves into the problem of insufficient utilization of auction signals and first reveals the auction bias. Specifically, AIE introduces two pluggable modules, namely Adaptive Market-price Auxiliary Module (AM2) and Bid Calibration Module (BCM), which work collaboratively to excavate the posterior auction signals better and enhance the performance of CTR prediction. Furthermore, the two proposed modules are lightweight, model-agnostic, and friendly to inference latency. Extensive experiments are conducted on a public dataset and an industrial dataset to demonstrate the effectiveness and compatibility of AIE. Besides, a one-month online A/B test in a large-scale advertising platform shows that AIE improves the base model by 5.76% and 2.44% in terms of eCPM and CTR, respectively.

The proliferation of edge computing has facilitated the edge-based artificial intelligence-generated content (AIGC) for ubiquitous and distributed end devices. To exemplify, we focus on the distributed implementation of one established instance, generative adversarial network (GAN), yielding the distributed GAN task. Practically speaking, this task usually is impeded by concerns including the unknown latency (of processing and transmission), the fairness requirement induced by heterogeneous distributed data and the limited energy budget of end devices. Besides, an often neglected factor is how to exploit feedback from networked end devices among which social ties indicate the flow of shared information. In practice, such social ties are partially observable to lack of exact knowledge of users, e.g., resulted from scarce historical data and privacy issues. Under this setting, we propose an online algorithm via integration of 1) online learning aided by graph neural network (GNN), aiming to recover social ties with GNN-based edge prediction, for accelerated learning of uncertainty and 2) online control to adaptively guarantee the constraints. We theoretically show that it not only achieves a sub-linear regret with guaranteed energy consumption and fairness but also leads to a superior global GAN. We also conduct simulations to justify its outperformance over online baselines.

Advanced edge systems have brought intelligence to networked end devices at the network edge. In such systems, privacy preservation has been an integral role since users' privacy may be violated via edge-device interaction given unsafe decision-making on information sharing. Therefore, we in this paper study privacy preservation for decision-making under bandit models. Particularly, a canonical bandit model features an agent that aims to maximize attainable rewards based on feedback from arm selection. However, upon application in edge systems, such feedback becomes more complex given 1) privacy concern and 2) non-negligible cost feedback. Confronting such concerns during decision-making, we study a privacy-preserving constrained bandit variant where we face the challenge of guaranteeing privacy preservation and within-budget cost while striving for high rewards. In this paper, we address the challenge with an integration of local differential privacy mechanism, online control, and online learning. Theoretically, we prove that our algorithm maintains adjustable privacy, adheres to cost constraints, and achieves a sub-linear regret (i.e., loss of reward). Numerically, we conduct simulations to demonstrate the outperformance of our algorithm over baselines.

Unmanned aerial vehicle (UAV)-based edge computing has been widely applied in intelligent transportation systems (ITSs) owing to its ease of deployment and high mobility. In this article, we study intelligent over-the-air computation (AirComp) in UAV swarm-assisted ITS. To develop a holistic service framework for UAV swarm, we consider the heterogeneity of Internet of Things Devices (IoTDs) and UAVs. We model the 3-D deployment of UAVs, service configuration, bandwidth allocation, the control of computing capacity, and transmission power as a joint optimization problem. To tackle this complex problem, we first propose a dual time-scale architecture based on deep reinforcement learning (DRL). This architecture enables UAVs to achieve seamless coverage of IoTDs on larger time scales, while collaborative UAVs dynamically provide services on smaller time scales. Next, we propose an intelligent AirComp algorithm D2IAC based on distributed DRL to obtain the optimal UAV deployment and dynamic service policies on different time scales. The D2IAC algorithm consists of three subalgorithms, i.e., TD3-based UAV deployment (TBUD), UAV services configuration (USC), and REINFORCE-based dynamic service (RBDS). Sufficient experimental results show that the proposed algorithm can achieve 3-D deployment of UAVs with coverage improvement from 9% to 36% compared to clustering, center layout, and random algorithms. Regarding dynamic services, compared with the deep deterministic policy gradient algorithm, greedy, fixed, and random strategies, the service durations of UAV swarm are improved by 32.95%–93.72% and the resource utilization is improved by 36.19%–49.61%.

Unmanned aerial vehicles (UAVs) are regarded as a promising solution for mobile edge computing (MEC) systems due to their flexibility and capability to provide computing services to ground terminals (GTs). By leveraging UAVs, the latency in computation tasks can be reduced significantly, particularly in disaster scenarios. Additionally, Reconfigurable Intelligent Surfaces (RIS) have emerged as a novel technology for enhancing the wireless propagation environment in wireless networks. This paper proposes a multi-UAV assisted MEC system where computation tasks of GTs can be computed locally or partially offloaded to UAVs. Furthermore, practical RIS phase shift designs are considered to enhance the communication performance between GTs and UAVs. To minimize the system delay and achieve fairness among GTs, the computation offloading strategy, trajectory of the UAVs are optimized using a markov decision process. Simultaneously, the RIS phase shift is optimized through an alternating optimization algorithm. Additionally, a cooperative multi-agent deep reinforcement learning framework is developed to obtain a optimal solution by employing the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm. Numerical results indicate that MATD3 can effectively improve the system delay and fairness performance of the RIS-assisted multi-UAV MEC system, as compared to benchmark solutions.

To enable the inference of high-precision deep neural networks (DNNs) on resource-constrained devices, DNN offloading has been widely explored in recent years. Some works have also integrated the chain-topology DNN (CDNN) offloading with pipeline processing to further reduce inference delay when processing streaming tasks. To improve the accuracy of the inference results, the topology of DNN tends to evolve from chain topology to directed acyclic graph (DAG) topology. However, most of the existing works do not study partitioning and offloading DAG-topology DNNs (DDNNs) for streaming tasks. Moreover, when partitioning computationally expensive DNN models, multipartitioning probably outperforms the bi-partitioning method, and most of the works do not study multipartitioning DAG-topology DNNs. In this article, we propose a more general multipartitioning and offloading method for large-scale DDNNs to process streaming tasks, which can adaptively partition DDNNs into multiple parts considering the computing power and bandwidth of all available computing units. Specifically, we first present a transforming method based on topological sorting that can losslessly transform DAG-topology DNNs into CDNNs. Then, based on greedy and dichotomy ideas, a multipartitioning algorithm is designed to partition and offload CDNNs. In this way, we can solve DDNNs’ multipartitioning problem based on the proposed transforming and partitioning algorithms. Experimentshttps://github.com/sreasearcher/PDD-Codeshow that the method proposed in this article significantly outperforms bi-partitioning and nonpartitioning methods when offloading computationally expensive DNN models.

The distributed deep learning architecture can support the front-deployment of deep learning systems in resource constrained IoT devices and is attracting increasing interest. However, most ready-to-use deep models are designed for centralized deployment without considering the transmission loss of the intermediate representation inside the distributed architecture. This oversight significantly affects the inference performance of distributed deployed deep models. To alleviate this problem, a state-of-the-art work chooses to retrain the original model to form an intermediate representation with ordered importance and yields better inference accuracy under constrained transmission bandwidth. This paper first reveals that this solution is essentially a pruning-like solution, where unimportant information is adaptively pruned to fit within the limited bandwidth. With this understanding, a novel scheme named Naturally Aggregated Intermediate Representation (NAIR) has been proposed, which aims to naturally amplify the difference of importance embedded in the intermediate representation from a mature deep model and reassemble the intermediate representation into a hierarchy of importance from high-to-low to accommodate the transmission loss. As a result, this method shows further improved performance in various scenarios, avoids compromising the overall inference performance of the system, and saves astronomical retraining and storage costs. The effectiveness of NAIR has been validated through extensive experiments, achieving a 112% improvement in performance compared to the state-of-the-art work.

Federated Learning (FL) has emerged as a fundamental learning paradigm to harness massive data scattered at geo-distributed edge devices in a privacy-preserving way. Given the heterogeneous deployment of edge devices, however, their data are usually Non-IID, introducing significant challenges to FL including degraded training accuracy, intensive communication costs, and high computing complexity. Towards that, traditional approaches typically utilize adaptive mechanisms, which may suffer from scalability issues, increased computational overhead, and limited adaptability to diverse edge environments. To address that, this paper instead leverages the observation that the computation offloading involves inherent functionalities such as node matching and service correlation to achieve data reshaping and proposesFederatedlearning basedoncomputingOffloading (FlocOff) framework, to address data heterogeneity and resource-constrained challenges. Specifically, FlocOff formulates the FL process with Non-IID data in edge scenarios and derives rigorous analysis on the impact of imbalanced data distribution. Based on this, FlocOff decouples the optimization in two steps, namely: 1) Minimizes the Kullback-Leibler (KL) divergence via Computation Offloading scheduling (MKL-CO); 2) Minimizes the Communication Cost through Resource Allocation (MCC-RA). Extensive experimental results demonstrate that the proposed FlocOff effectively improves model convergence and accuracy by 14.3%-32.7% while reducing data heterogeneity under various data distributions.

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.

Synthetic Aperture Radar (SAR) is a type of active microwave remote sensing imaging radar that can generate two-dimensional high-resolution images. Its ability to operate in all weather conditions and at all times has led to its widespread use. As a multi-parameter and multi-channel extension of SAR, polarimetric SAR (PolSAR) provides a wealth of scattering information for various applications, including topographic mapping, ocean exploration, polar observation, and target identification. Compared with single-polarization SAR, multi-polarization SAR enhances the information potential of the data by expanding its one-dimensional information, however, this potential cannot be fully realized without a clean SAR echo signal. The electromagnetic environment is becoming increasingly congested with radio frequency interference (RFI) signals, presenting a significant challenge for the subsequent tasks of PolSAR. Although there have been many related studies based on polarization information to carry out the aforementioned applications, there is a lack of research on the joint suppression of interference by using multi-polarization information, and single-polarization data alone is insufficient in effectively mitigating interference. To address these challenges, this paper presents a framework combining multi-polarization data to improve performance of low-rank based methods. Based on the proposed framework, experiments are conducted on real PolSAR data to assess the feasibility of the proposed framework in interference suppression. The results demonstrate that the framework significantly enhances the suppression performance of various low-rank based methods with clearer scene details being recovered.

Image distortion during wireless transmission presents a significant challenge for real-world artificial intelligence (AI) applications. Recent methods have attempted to address this issue by integrating neural networks into the wireless transmission system. However, these approaches often require a large volume of labeled training data, which can be expensive and time-consuming to collect. To address this issue, we propose a novel approach,Self-SupervisedMulti-AgentFeedbackEnabledNeuralNetworks (S2MAFENN). S2MAFENN is designed to improve the efficiency of labeled data in wireless image transmission. It incorporates a Feedbacker agent that emulates the error correction mechanisms observed in primate brains and employs self-supervised contrastive learning to extract representations from unlabeled distorted images independently. From a theoretical perspective, we model the training process of S2MAFENN as a three-player Stackelberg game and provide evidence that S2MAFENN can achieve exponential convergence rates. We then empirically validate our approach by assessing the representations learned through S2MAFENN. We use varied labeled CIFAR10 and CIFAR100 data to simulate real image transmissions over the Rayleigh fading and 5G channels. Our results show that S2MAFENN matches or even surpasses the performance of state-of-the-art self-supervised training methods, even when only 50% of labels are used. Moreover, S2MAFENN yields average accuracy gains of 5.11%, 5.8%, and 4.58% with only 0.1, 0.2, and 0.5 of the labels transmitted over the 5G channel, respectively. For the downstream task of semantic segmentation over the 5G channel, S2MAFENN exhibits significant advancements on the ADE20K dataset. It achieves enhancements of approximately 7% and 8.7% in Mean IoU and DICE metrics, respectively, surpassing the performance of current state-of-the-art methods.

Emoji prediction has been widely adopted in most mobile keyboards to improve the quality of user experience. Considering the resource constraints of smartphones, it is promising to deploy well-trained prediction models on edge servers, with which smartphones can carry out emoji prediction in an online fashion. However, a key issue in such a scenario lies in how the smartphone should select a subset of models to achieve high-accuracy and real-time emoji prediction with energy efficiency (a.k.a.themodel selectionproblem). Moreover, part of the system dynamics such as the prediction accuracy and the inference latency of each model are usually unknowna prioriin practice, further complicating the problem. In this paper, with an effective integration of history-aware online learning and online control, we propose the first green edge intelligence scheme to solve the model selection problem for mobile keyboard emoji prediction. Our theoretical analysis and simulation results verify the effectiveness of our proposed scheme in achieving a sub-linear round-averaged regret bound and energy efficiency with a high prediction accuracy and a low latency.

With the widespread application of 5G and the Internet of things (IoT), edge computing and cloud computing have been collaboratively utilized for task offloading and processing. However, though the massive devices (e.g., smartphones) are organized into multi-cells, most of the existing works do not explore the computation offloading for edge-cloud computing under inter-cell interference. Thus, the offloading decisions may be inappropriate as the transmission rate is overestimated. To address this issue, we propose COMEC, a novel Computation Offloading scheme in Multi-cell networks with Edge-Cloud collaboration, which could minimize the total cost in terms of delay and energy consumption. Specifically, we first formulate COMEC as an optimization problem taking into account inter-cell interference. Then, considering the offloading decisions of all users are coupled, a non-cooperative game is formulated to minimize the total cost of each user in a distributed manner. We prove that this game is a general (ordinal) potential game and possesses a pure strategy Nash equilibrium (NE). Based on the finite improvement property of the potential game, we develop the corresponding computation offloading algorithm to achieve the NE. Finally, simulation results show that the proposed scheme can achieve superior performance in overall system cost compared with other baselines.

The collaboration between unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) presents an innovative approach for delay-tolerant data harvesting in distributed Internet of Things (IoT) networks. However, existing research mostly overlooks the dynamic changes in communication links caused by the real-time UAV movement and the realistic geographical features. In this paper, we address these challenges by considering a practical three-dimensional (3D) urban scenario with a centralized IRS. Our aim is to minimize the completion time of data harvesting missions by jointly optimizing the 3D trajectory of the UAV and the phase shift of the IRS. Specifically, the formulated problem is decoupled into two subproblems. First, for the 3D continuous trajectory design, we propose a robust memory-based softmax deep double deterministic policy gradients (MSD3) approach, which enables the UAV to adaptively collect delay-tolerant data from randomly distributed ground devices starting from any arbitrary point. Second, we present a comprehensive theoretical analysis for the continuous IRS phase control, which provides a practical and intuitive numerical solution. Simulation results demonstrate that the proposed MSD3-IRS algorithm outperforms other mainstream baselines based on deep reinforcement learning.

With the application and development of the fifth-generation (5G) communications, it is essential to gain insight understanding of the multi-antenna wireless channel characterizations. In particular, their relevant Channel Impulse Response (CIR) so to ensure the effective design of algorithms and systems. However, both the traditional channel measurement and modelling are essentially based on assessment at discretely sampled spatial points without the capability to obtain the relevant channel information over a given surrounding area. To overcome this limitation, this paper proposes to re-assemble the discretely sampled CIRs into equalized video streams. With this basis, a deep learning based video super-resolution method, namely, the Time Decomposition Video Super-Resolution (TDVSR), has been proposed to restore the area channel information for the first time. Moreover, a time decomposition module based on Bidirectional Long Short-Term Memory (BiLSTM) has been designed to decompose the re-assembled CIRs into video form in the time dimension. A retrained video super-resolution model will then process the composited data and output high-resolution frames, which will be reversed to the CIRs at the dense density target area. A data set with various typical fading scenarios has been constructed by Ray Tracing (RT) method. Extensive experiments demonstrate that the proposed TDVSR model successfully learned the nonlinear propagation laws through the data-driven method, which shows satisfied restoration accuracy with significantly increased computation efficiency.

Over-The-Air (OTA) measurement is considered the preferred method for measuring the antenna system and end-to-end performance of Multiple-Input-Multiple-Output (MIMO) devices under test. Spatial correlation has been widely utilized as a key metric for evaluating the accuracy of MIMO OTA measurements. However, there is no guarantee that the standard signal streams convoluted with specified impulse responses will be ideally independent of each other in the implementation of the MIMO OTA testing system. Thus, it is envisaged that the spatial correlation in practical MIMO OTA testing systems may not be exactly equivalent to the expected value of the ideal theoretical model. In this paper, we propose a new evaluation framework for evaluating the spatial correlation performance of MIMO OTA testing system. This evaluation framework provides a novel observation method for spatial correlation, which reflects the non-ideal configuration of the MIMO OTA testing system and can be utilized to predict or cross-validate spatial correlation errors that deviate from the theoretical model. The experimental and simulation results have been verified against the theoretical model, demonstrating good consistency between the theoretical model and the proposed evaluation framework. Furthermore, several test scenarios have been verified with the different varying factors.

Traditional low-rank methods overlook residuals as corruptions, but we discovered that low-rank residuals actually keep image edges together with corrupt components. Therefore, filtering out such structural information could hamper the discriminative details in images, especially in heavy corruptions. In order to address this limitation, this paper proposes a novel method named ESL-LRR, which preserves image edges by finding image projections from low-rank residuals. Specifically, our approach is built in a manifold learning framework where residuals are regarded as another view of image data. Edge preserved image projections are then pursued using a dynamic affinity graph regularization to capture the more accurate similarity between residuals while suppressing the influence of corrupt ones. With this adaptive approach, the proposed method can also find image intrinsic low-rank representation, and much discriminative edge preserved projections. As a result, a new classification strategy is introduced, aligning both modalities to enhance accuracy. Experiments are conducted on several benchmark image datasets, including MNIST, LFW, and COIL100. The results show that the proposed method has clear advantages over compared state-of-the-art (SOTA) methods, such as Low-Rank Embedding (LRE), Low-Rank Preserving Projection via Graph Regularized Reconstruction (LRPP_GRR), and Feature Selective Projection (FSP) with more than 2% improvement, particularly in corrupted cases.

Gun violence and misuse pose great threat to the public safety. Real-time monitoring of gun usage and gunshot events are very promising for effective gun control. However, most available monitoring systems are installed in a fixed location instead of the guns, which greatly limits the flexibility and coverage. In this study, we propose a wireless gun monitoring and gunshot recognition system based on a low-cost triaxial acceleration sensor, which can monitor the gun in real time and accurately recognize gunshot events. Addressing the limited resources of the embedded systems, we further propose an efficient gunshot recognition algorithm EfficientNetTime that combines the lightweight neural network and knowledge distillation, so as to enable the deployment on embedded devices. First, a novel lightweight deep learning model is proposed as the basic model, which combines the advantages of 1-D convolution and depthwise separable convolution to effectively characterize the gunshot signal while decreasing the computing cost of convolution. Second, using the knowledge distillation, EfficientNetTime is used as the teacher model to generate a compressed student model that maintains accuracy and greatly reducing model size. Finally, the EfficientNetTime student model can be deployed on resource-limited embedded systems. The proposed method can automatically extract features for end-to-end recognition and is robust to temporal transformations of input signals. Using a publicly available gunshot data set, the proposed EfficientNetTime model is verified and compared against the state-of-the-art models. Experimental results demonstrate that the EfficientNetTime model surpasses other gunshot recognition methods in terms of the accuracy and model size.

With the rapid development of edge data intelligence, task offloading (TO) and resource allocation (RA) optimization in multiaccess edge computing networks can significantly improve the Quality of Service (QoS). However, for the online scenario, traditional methods (e.g., game theory and numerical methods) cannot adapt to dynamic environments. Deep reinforcement learning (DRL) is applied to adjust the policy to get long-term rewards. Nevertheless, since the joint problem of TO and RA is nonconvex and NP-hard, existing DRL methods cannot guarantee high efficiency because of the large action space. To solve the above problem, we propose a fast learning with integrated reward and reduced action space-based DRL framework (FLIRRAS), which adopts a low-complexity approach to jointly optimize TO and RA strategies. The FLIRRAS framework combines DRL with numerical methods to iteratively pursues the discrete TO and continuous RA. Specifically, a deep neural network (DNN) is used to learn environmental information, which can get prior knowledge of the offloading decision. Furthermore, a novel reward integrating the utility of TO and RA is designed to motivate the agent to find the optimal policy. To solve the dilemma that the action space is too large, low-complexity convex optimization methods, i.e., subgradient projection and KKT condition, are used to supplement and adjust the decision, which reduces the network parameters and the decision space. In addition, given the dynamic online environment, we introduce the experience replay mechanism, where policy is updated regularly to reflect the best mapping between states. The experiment results show that the performance of FLIRRAS is better than greedy and other DRL approaches, and it outperforms the latest DRL method by over 18.0% in terms of execution time.

To face the challenges in emergency scenarios, a multitask and multiobjective optimization algorithm for computation offloading and relay communication is investigated for the air-ground integrated networks, composed of unmanned aerial vehicles (UAVs), emergency vehicle users (EVUs) and ground sensor nodes (GSNs). We propose an HFL-DDQN algorithm, which combines horizontal federated learning (HFL) with double deep$Q$-network (DDQN). First, UAVs are separated into two clusters according to the services they provide, i.e., edge computing or relay communication. Next, the optimization problems are formulated for two types of services, respectively. For the computation offloading tasks of EVUs, the optimization objective is to minimize the weighted sum of delay and energy consumption. For the sensor data transmission of GSNs, the optimization objective is to maximize the minimum rate of relay links. We define the total cost of the system as the sum of two types of services. Then, federated aggregation is used to joint training the global neural networks model without sharing raw data. Furthermore, the DDQN is improved by adopting prioritized experience replay to achieve better convergence. The simulation results show that the proposed HFL-DDQN algorithm not only outperforms the state-of-the-art baselines in terms of the system cost but also promotes the generalization in execution process, which is especially applicable to the rescue scene under accidents.

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in detail. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks.

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. This Special Issue aims to provide a forum for the latest advances in multi-tier computing for next-generation wireless network research, innovations, and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

As a wideband radar system, synthetic aperture radar (SAR) may conflict with several electromagnetic systems. These signals may severely interfere with SAR image quality. Numerous previous researches focused on the interference suppression problem, among which semiparametric methods, such as low-rank recovery methods, have been verified to have state-of-the-art (SOTA) performance. However, semiparametric methods are restricted by extremely strong interferences when the signal-to-interference-and-noise ratio (SINR) exceeds the ability upper bound of semiparametric methods. In recent years, multichannel SAR (MC-SAR) systems have been widely used for more applications, where multiple antennas are mounted along the azimuth or in elevation. Adaptive digital beamforming (DBF) is a classic spatial filtering method to focus energy in the expected direction and suppress unexpected interferences. Its performance is determined by the array manifold and the interference-impinging angle. In this article, we propose a novel space-time-combined method that takes advantage of both low-rank recovery methods in the 2-D time domain and the adaptive DBF method in the spatial domain. Specifically, we construct a single optimization problem to unify both kinds of methods. The alternating direction of the multiple multiplier (ADMM) framework is leveraged with a closed-form solution for each step. Multiple experiments are provided to demonstrate the effectiveness of the proposed method.

In satellite-terrestrial integrated networks, it is a common practice to schedule real-time tasks from low Earth orbit (LEO) satellites to ground stations (GSs) for data processing. However, the joint task scheduling and resource allocation under unknown environment dynamics (e.g., transmission latency) remains to be a challenging problem. First, the tradeoff between task latencies and energy consumption should be carefully considered when making decisions to minimize task latencies under time-averaged energy consumption constraints. Second, to learn the environment uncertainties and minimize the system performance loss (i.e., regret) in terms of task latencies, both online feedback and offline history should be leveraged efficiently, and the accompanying exploration-exploitation tradeoff should be dealt with in a proper way. In this article, we formulate the joint task scheduling and resource allocation problem as a constrained combinatorial multi-armed bandit (CMAB) problem. To solve the problem, by integrating online learning, online control, and offline historical information, we propose aTask scheduling and Resource allocation scheme with Data-driven Bandit LearningcalledTRDBL. Our theoretical and numerical results show that TRDBL achieves a sublinear time-averaged regret while satisfying the time-averaged energy consumption constraints.

Compressed sensing (CS) has been identified as a good candidate for user detection in grant-free non-orthogonal multiple access (NOMA) by exploiting the inherent sparsity of user activity. However, most of the existing CS-based user detection schemes do not fully utilize the temporal correlation of user activity in NOMA and rely heavily on the unrealistic assumption that the number of active users is known in advance. To address these issues, we propose a temporal correlation enhanced multiuser detection scheme to achieve efficient and pragmatic multiuser detection. First, using 1-bit memory to piggyback the information on whether the active users still have data to transmit, the base station can realize that the active users in the current time slot will turn to be silent or remain active. Then, to make explicit use of the temporal correlation of active user sets, a cross validation based adaptive subspace pursuit (CVASP) algorithm is developed by utilizing the reported information on prior active users. The proposed CVASP is a highly practical algorithm that does not require any prior knowledge of the number of active users or the noise level, as the cross validation technique could properly determine the stopping condition. Extensive simulation results demonstrate that the proposed mechanism could achieve almost the same performance as compared to the existing state of art CS-based multiuser detection algorithms while eliminating the need for any prior knowledge.

Federated learning (FL) leverages the private data and computing power of multiple clients to collaboratively train a global model. Many existing FL algorithms over wireless networks adopting synchronous model aggregation suffer from the straggler issue, due to the heterogeneity of local computing power and channel conditions. To address this issue, we in this paper advocate an asynchronous FL framework with adaptive client selection for training latency minimization, taking into account the client availability and long-term fairness. We consider a practical scenario, where the channel conditions and the locally available computing power are not known in prior. This makes the client selection problem challenging, as the training latency consists of the uplink/downlink transmission time and the local training time. To this end, we tackle the asynchronous client selection problem in an online manner by converting the latency minimization problem into a multi-armed bandit problem, and leverage the upper confidence bound policy and virtual queue technique in Lyapunov optimization to solve the problem. We theoretically show that the proposed algorithm achieves sub-linear regret performance, ensures long-term fairness, and guarantees training convergence. Results show that the proposed algorithm can reduce the training time by up to 50% when compared to the baseline algorithms.

Yang Li, Cheng Yu, Guangzhi Sun, Hua Jiang, Fanglei Sun, Weiqin Zu, Ying Wen, Yang Yang, Jun Wang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

DNN offloading has become an important supporting technology for edge intelligence. However, most of the existing works do not consider thread scheduling, which can achieve the parallelism of multiple threads in the practical distributed DNN inference system. To address this issue, we discuss the thread scheduling of the computing units participating in offloading in this paper, considering a single-core Central Processing Unit (CPU) and the Round Robin Scheduling (RRS). We deduce the relationship between the blocking of DNN inference-related threads and the Average Task Delay (ATD) and prove that an appropriate buffer setting can reduce blocking times. Theoretical analysis verifies that the buffering mechanism (DBM) can reduce the ATD significantly, and experimental results demonstrate that the DBM-improved DNN offloading can achieve a delay reduction of 14%-71%.

With the massive use of GPU, task scheduling under CPU-GPU clusters has become an indispensable research topic. Unlike existing models, we propose an innovative framework that users offload their tasks in CPU-GPU heterogeneous Edge Clusters (ECs) instead of general-purpose CPU clusters. The framework takes full advantage of the GPU's powerful parallel computing capabilities. Specifically, we decompose each user task into sequential segments and parallel segments, which can be offloaded to CPUs and GPUs of the ECs, respectively. By dis-cretizing the GPU's computing capability, we formulate a Mixed Integer Nonlinear Programming (MINLP), which involves jointly optimizing the task offloading decision, the uplink transmission power of users, and computing resource allocation. To tackle this challenging problem, we propose a Joint Simulated Annealing and Convex Optimization (JSAC) based algorithm to minimize the total overhead consisting of delay and energy consumption. Our experimental simulation results demonstrate that the JSAC algorithm can make full use of GPU's powerful parallel computing capability via allocating GPU resources effectively. In particular, the JSAC algorithm achieves optimal performance in terms of system overhead, number of beneficial UEs, and speedup.

The increasingly stringent requirement on quality-of-experience in 5G/B5G communication systems has led to the emerging neural speech enhancement techniques, which however have been developed in isolation from the existing expert-rule based models of speech pronunciation and distortion, such as the classic Linear Predictive Coding (LPC) speech model because it is difficult to integrate the models with auto-differentiable machine learning frameworks. In this paper, to improve the efficiency of neural speech enhancement, we introduce an LPC-based speech enhancement (LPCSE) architecture, which leverages the strong inductive biases in the LPC speech model in conjunction with the expressive power of neural networks. Differentiable end-to-end learning is achieved in LPCSE via two novel blocks: a block that utilizes the expert rules to reduce the computational overhead when integrating the LPC speech model into neural networks, and a block that ensures the stability of the model and avoids exploding gradients in end-to-end training by mapping the Linear prediction coefficients to the filter poles. The experimental results show that LPCSE successfully restores the formants of the speeches distorted by transmission loss, and outperforms two existing neural speech enhancement methods of comparable neural network sizes in terms of the Perceptual evaluation of speech quality (PESQ) and Short-Time Objective Intelligibility (STOI) on the LJ Speech corpus.

To cope with the high communication overhead caused by frequent aggregation of Federated Learning (FL) in Multi-access Edge Computing (MEC) scenarios, Hierarchical Federated Edge Learning (HFEL) is proposed as an evolving framework. HFEL offloads tasks to edge servers for partial model aggregation to reduce network traffic. However, most of the existing research focuses on resource optimization for HFEL without considering the impact of data characteristics and cannot guarantee the quality of FL training. To this end, we propose a task offloading approach based on data and resource heterogeneity under HFEL to improve training performance and reduce system cost. Specifically, we leverage information entropy to incorporate data statistical features into the cost function to reshape edge datasets. In addition, we applied Multi-Agent Deep Deterministic Policy Gradient (MADDPG) with a resource allocation module to generate distributed offloading policy more efficiently. Our algorithm not only adopts local observations to obtain the optimal action but also takes into account device heterogeneity, which can adapt to the unstable edge environment. Extensive experiments under multiple datasets and baselines are carried out, which demonstrate that our algorithm can effectively improve the accuracy of aggregated models while reducing system cost.

The scheme design of switch-controller association is an essential problem for software-defined networking (SDN) systems. A natural idea is to address the problem from the perspective of stable matching, since each switch (controller) often prefers to be associated with those controllers (switches) of lower communication costs and control traffic overhead. However, in practice, such system dynamics are usually unknown a priori, making it a challenging open problem. In this paper, we study such a problem of stable matching between switches and controllers with unknown communication costs from the perspective of multi-agent multi-armed bandit (MAMAB) learning. By integrating stable matching with online learning, we propose an effective Learning-aided Switch-controller Stable Matching (LS2M) scheme. Our theoretical analysis shows that LS2M effectively achieves a switch-optimal stable matching with a sublinear regret bound over time slots. Moreover, we conduct numerical simulations to verify the outperformance of LS2M over various baseline schemes.

In intelligent Internet of Things systems, data-hungry services are empowered by data collection, which is jointly accomplished by edge servers and data-collecting sensors. In this paper, we aim to achieve efficient data collection, i.e., maximize data rates from sensors to servers while mitigating the impact of data heterogeneity for data collected from sensors. Considering geographically distributed servers and sensors, we study the problem from the perspective of multi-agent multi-armed bandits. The key ideas of our approach are to 1) establish associations between servers and sensors under unknown wireless dynamics (i.e., channel state information) and selection fraction constraints; 2) utilize shared information via pairwise communication between servers to mitigate biased observations for data rates. To this end, we propose a scheme that leverages online learning to reduce uncertainties in wireless dynamics and online control to mitigate the impact of data heterogeneity. Based on an effective integration of bandit learning methods under pairwise communication and Lyapunov optimization techniques, we present a novel Decentralized sErver-Sensor association scheme with Multi-Agent learning under pairwise communication (DESMA). Our theoretical analysis demonstrates that DESMA achieves a tunable trade-off between maximizing data rate and mitigating the impact of data heterogeneity.

Running computer vision applications, such as 3-D simultaneous localization and mapping (SLAM), on mobile devices requires low-latency responses and a massive amount of computation. Edge computing has been introduced to move Cloud features closer to end users, providing necessary computing and network resources for end devices. The heterogeneous edge devices, with different hardware architectures (e.g., CPUs and GPUs) and runtime environments, provide diverse resources to support processing tasks from end devices, resulting in different costs and quality of services. How to partition these computing tasks and distribute them over these heterogeneous hardware nodes is still an open research question. Considering these inherently heterogeneous hardware architectures, new approaches for service orchestration and task scheduling are required to meet the service-level agreement and reduce the overall cost of the system (e.g., facility utilization cost). This article presents a system framework, EDGE VISION, for computer vision applications partitioning and orchestration on heterogeneous edge computing platforms considering both CPUs and GPUs. EDGE VISION abstracts the heterogeneous hardware resources and the task runtime environments and divides the application into separate tasks to be orchestrated and deployed into the heterogeneous edge nodes. We also propose two scheduling algorithms in our framework, minimum latency task scheduling and minimum cost task scheduling, aiming to minimize the processing latency and the overall system cost. We evaluate our framework by implementing the edge-based 3-D SLAM application in our real testbed with ten heterogeneous edge devices. Evaluations show that EdgeVision can efficiently minimize the processing latency and the system overall cost and achieve up to 30% decrease in task processing latency and 15% more cost saving compared to the State-of-the-Art baselines.

The wireless-enabled Industrial Internet of Things (IIoT) system is promising due to its flexibility and cable-free deployment. The varying fading channel will lead to the random transmission delays and jitters, which are the major challenges hindering the adoption of wireless communication in mission-critical industrial systems. The performance or even the stability of closed-loop feedback control system will degrade severely with such delays and jitters. As a result, the maximum delay margin should be met to guarantee the performance of the IIoT system. Aiming to better satisfy this requirement, a novel concept to shape the delay distribution under the industrial multipath fading channel is proposed in this article. Consequently, a two-layer closed feedback control algorithm, referred as TACAN in this article, is designed through the decoupling of original optimization function, by which the variance of the delay distribution is minimized to improve the reliability and stability of the IIoT systems. The performance of proposed delay shape control method is verified by both the classical Rician channel model and the field measured industrial fading channel responses.

A fundamental issue in multiaccess edge computing (MEC) is efficiently offloading multiple tasks to multiple helper nodes (MTMH), i.e., MEC servers. However, most of the existing decentralized schemes do not consider interuser interference or merely adopt time division multiple access (TDMA) as the multiple access scheme for MTMH in the heterogeneous scenario, leading to a large latency. To address these issues, we propose DOT, a novel Decentralized Offloading of Tasks scheme in orthogonal frequency division multiple access (OFDMA)-based heterogeneous MEC, to minimize the sum cost in terms of energy consumption and delay. Specifically, we first formulate DOT as an optimization problem considering the interuser interference and dynamics in communication and computation resource allocation. Then, considering the huge dimension of potential offloading decisions and conflicting objectives of different users, the total cost of each user is minimized in a distributed manner by modeling the offloading problem as a potential game. The formulated potential game is proved to be an ordinal potential game and thus admits a Nash equilibrium (NE). Further, we develop an offloading algorithm to achieve the NE by exploiting the finite improvement property. Finally, simulation results demonstrate that DOT can achieve a lower cost compared with other baselines.

Most online service providers deploy their own data stream processing systems in the cloud to conduct large-scale and real-time data analytics. However, such systems, e.g., Apache Heron, often adopt naive scheduling schemes to distribute data streams (in the units of tuples) among processing instances, which may result in workload imbalance and system disruption. Hence, there still exists a mismatch between the temporal variations of data streams and such inflexible scheduling scheme designs. Besides, the fundamental limits of benefits of predictive scheduling to data stream processing systems remain unexplored. In this article, we focus on the problem of tuple scheduling with predictive service in Apache Heron. With a careful choice in the granularity of system modeling and decision making, we formulate the problem as a stochastic network optimization problem and proposePOTUS, an online predictive scheduling scheme that aims to minimize the response time of data stream processing by steering data streams in a distributed fashion. Theoretical analysis and simulation results show that POTUS achieves an ultra-low response time with a stability guarantee. Moreover, POTUS only requires mild-value of future information to effectively reduce the response time, even with mis-prediction.

In this paper, a massive multiple-input multiple-output (MIMO) relay assisted multi-tier computing (MC) system is employed to enhance the task computation. We investigate the joint design of the task scheduling, service caching and power allocation to minimize the total task scheduling delay. To this end, we formulate a robust non-convex optimization problem taking into account the impact of imperfect channel state information (CSI). In particular, multiple task nodes (TNs) offload their computational tasks either to computing and caching nodes (CCN) constituted by nearby massive MIMO-aided relay nodes (MRN) or alternatively to the cloud constituted by nearby fog access nodes (FAN). To address the non-convexity of the optimization problem, an efficient alternating optimization algorithm is developed. First, we solve the non-convex power allocation optimization problem by transforming it into a linear optimization problem for a given task offloading and service caching result. Then, we use the classic Lagrange partial relaxation for relaxing the binary task offloading as well as caching constraints and formulate the dual problem to obtain the task allocation and software caching results. Given both the power allocation, as well as the task offloading and caching result, we propose an iterative optimization algorithm for finding the jointly optimized results. The simulation results demonstrate that the proposed scheme outperforms the benchmark schemes, where the power allocation may be controlled by the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requiresa prioriaiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.

For wireless caching networks, the scheme design for content delivery is non-trivial in the face of the following tradeoff. On one hand, to optimize overall throughput, users can associate their nearby APs with great channel capacities; however, this may lead to unstable queue backlogs on APs and prolong request delays. On the other hand, to ensure queue stability, some users may have to associate APs with inferior channel states, which would incur throughput loss. Moreover, for such systems, how to conduct predictive scheduling to reduce delays and the fundamental limits of its benefits remain unexplored. In this paper, we formulate the problem of online user-AP association and resource allocation for content delivery with predictive scheduling under a fixed content placement as a stochastic network optimization problem. By exploiting its unique structure, we transform the problem into a series of modular maximization sub-problems with matroid constraints. Then we devisePUARA, a Predictive User-AP Association and Resource Allocation scheme which achieves a provably near-optimal throughput with queue stability. Our theoretical analysis and simulation results show that PUARA can not only perform a tunable control between throughput maximization and queue stability, but also incur a notable delay reduction with predicted information.

In the intelligent communication field, deep learning (DL) has attracted much attention due to its strong fitting ability and data-driven learning capability. Compared with the typical DL feedforward network structures, an enhancement structure with direct data feedback have been studied and proved to have better performance than the feedfoward networks. However, due to the above simple feedback methods lack sufficient analysis and learning ability on the feedback data, it is inadequate to deal with more complicated nonlinear systems and therefore the performance is limited for further improvement. In this paper, a novel multi-agent feedback enabled neural network (MAFENN) framework is proposed, consisting of three fully cooperative intelligent agents, which make the framework have stronger feedback learning capabilities and more intelligence on feature abstraction, denoising or generation, etc. Furthermore, the MAFENN frame work is theoretically formulated into a three-player Feedback Stackelberg game, and the game is proved to converge to the Feedback Stackelberg equilibrium. The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback. To verify the MAFENN framework’s feasibility in wireless communications, a multi-agent MAFENN based equalizer (MAFENN-E) is developed for wireless fading channels with inter-symbol interference (ISI). Experimental results show that when the quadrature phase-shift keying (QPSK) modulation scheme is adopted, the SER performance of our proposed method outperforms that of the traditional equalizers by about 2 dB in linear channels. When in nonlinear channels, the SER performance of our proposed method outperforms that of either traditional or DL based equalizers more significantly, which shows the effectiveness and robustness of our proposal in the complex channel environment.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the sequential rank-one constraint relaxation (SROCR) algorithm and semidefinite relaxation (SDR) algorithm for a given power- and computational resource allocation. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision that minimizes the total energy consumption. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes, and the energy efficient offloading strategy for the proposed fog computing system can be chosen according to the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

In 5G massive multiple input multiple output (MIMO) system, the main challenges to mitigate inter-cell interference (ICI) are overhead of information exchange and computational complexity. In this paper, we propose an interference approximation method based on spatial features, which can cover the major channel information by low overhead. And based on this method, a novel distributed low-complexity interference coordination algorithm called SFDIC is proposed, which is based on the idea of leader-follower game to avoid strong ICI. The experimental results show that the proposed interference approximation method is strongly consistent with the traditional channel matrix based interference calculation method on the trend, whose correlation coefficient is 0.9098 and Kullback-Leibler (KL) divergence is close to 0. In low and medium-speed scenarios, the SFDIC increases system and edge throughput by more than 20% and 107% than joint space division multiplexing (JSDM) respectively. And these scenarios reduce the sharing overhead by more than 50% simultaneously. In addition, the new channel predicted module based on Koopman operator is incorporated to improve practical feasibility and system performance loss causing by delay for the first time.

Considering the contradiction between limited re-sources in small devices and the high complexity of deep neural networks (DNNs), DNNs can hardly run on small devices such as smartphones and wearable devices. Therefore, offloading DNNs to computing units (fog/edge servers), where each unit executes a part of a DNN collaboratively, has gained increasing popularity. Notably, DNNs are generally in chained structure, while streaming tasks are the central part of artificial-intelligent applications. Thus it is crucial to reduce chained DNNs' delay for streaming tasks. Although existing works have advanced DNN offloading largely, the discussion about chained DNNs and streaming tasks is negligible. To address this issue, in this paper, we propose a layer-level offloading model called OCDST based on the analysis about them. After chained DNNs are offloaded to computing units, the involved units will handle streaming tasks as a pipeline. Consequently, the model significantly reduces the average task delay by paralleling each step in the pipeline. Moreover, the global optimal model solution is drawn by an improved depth-first search (DFS) algorithm, which utilizes DFS to achieve path establishment, calculation and record stages. Based on multi-threading programming and producer-consumer pattern, a program parallelization scheme is also devised to ensure the feasibility of the obtained optimum. Experimental results show that OCDST significantly outperforms recent works with higher inferring speed and faster response.

Feedback mechanism has been widely used in wireless communication such as channel equalization and resource allocation. In recent years, deep learning (DL) has made great progress in the field of wireless communication. There is now some work that attempts to introduce plain feedback mechanisms into DL algorithm to solve wireless communication problems. However, the improvement of plain feedback DL methods is limited in complex situations due to those methods lack sufficient learning ability on feedback information. In this paper, we propose a Multi-Agent Feedback Enabled Neural Network (MAFENN) equalizer, which consists of a specific learnable feedback agent and two feed-forward agents. Three fully cooperative intelligent agents help the system improve the ability to remove wireless inter-symbol interference (ISI) in receiving ends. We further formulate it into a three-player Stackelberg Game, which helps us to optimize and train this model more efficiently. To verify the feasibility of our proposed MAFENN system and the Stackelberg Game optimization, we conduct a series of experiments to compare the symbol error rate (SER) performance of the MAFENN equalizer and the other methods which utilizes quadrature phase-shift keying (QPSK) modulation scheme. Our performance outperforms that of the other equalizers at different signal-to-noise ratio (SNR) settings for both linear and nonlinear channels.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the semidefinite relaxation (SDR) algorithm. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

High overhead of sharing and feedback and high computational complexity are common problems in multi-cell processing. In this paper, a novel framework for bidirectional signal transformation between space and frequency domains of massive MIMO channels is proposed to reduce system processing overhead and complexity. We design new space and frequency features and build the framework by two off-line trained neural networks (NN). Moreover, the uniqueness of spatial features is proved. Average errors of uni- and bi-directional transformation are 7.6% and 7.3%. When applying the framework to inter-cell interference coordination (ICIC), the system and edge throughput are both increased compared to the traditional scheme with low information sharing overhead.

Emoji prediction has been widely adopted in most mobile keyboards to improve the quality of user experience. Considering the energy limitations of smartphones, it is promising to consider deploying pre-trained prediction models on edge servers, with which smartphones can carry out emoji prediction in an online fashion. However, given a limited connection capacity, a key issue under such a scheme lies in how each smartphone should select a subset of models to achieve high-accuracy and real-time emoji prediction with energy efficiency (a.k.a. the model selection problem). Moreover, part of the system dynamics such as the accuracy and the latency of individual models are usually unknown a priori in practice, further complicating the problem. In this paper, with an effective integration of history-aware online learning and online control, we propose the first green edge intelligence scheme to solve the model selection problem for edge-assisted mobile keyboard emoji prediction. Our theoretical analysis and simulation results verify the effectiveness of our proposed scheme in achieving a sublinear regret bound and energy efficiency with high accuracy and low latency.

Benefiting from the breakthrough development of the fifth generation (5G), beyond 5G (B5G) wireless communication networks and Artificial Intelligence (AI) in recent years, the artificial intelligence of things (AIoT) is a new trend in the future. AIoT devices often have high-quality wireless video transmission requirements. However, the propagating signals at millimeter wave suffer from high propagation loss and sensitivity to blockage, resulting in the received video is vulnerable to be interfered. Due to the ability of Deep Learning (DL) to discover and learn good representations, some DL methods have achieved breakthrough performance in video recovery. However, most of these methods cannot exploit information from the higher to lower level to refine themselves. In this paper, we propose a novel retrospective thinking based multi-agent (ReTMA) system to solve the interference problem experienced on wireless channels. Compared with other plain feedback models, we add a retrospective agent on the feedback loop, which makes the entire system have stronger capabilities to learn good representative features. We further formulate it as a Stackelberg game to analyze the dependency relationship between the agents and facilitate the complex training issue of the multiple agents. To verify the feasibility of ReTMA system, we randomly add masks to simulate the severe interference received by the video frames in wireless transmissions. Experimental results show that the performances of similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and classification accuracy all achieve significant gains compared with those of other plain feedback models at different mask ratios.

In satellite-terrestrial integrated networks, it is a common practice to distribute real-time tasks from low Earth orbit (LEO) satellites to ground stations (GSs) for data processing. However, it remains an open problem how to match tasks with proper GSs in an online fashion with unknown dynamics, e.g., transmission latency. Moreover, such a problem is further complicated by the non-trivial interaction between the decision-making procedure and long-term constraints on time-averaged energy consumptions. In this paper, by formulating the energy-constrained online matching problem with unknown transmission latency as a constrained Combinatorial Multi-Armed Bandit (CMAB) problem, we adopt bandit learning methods and virtual queue techniques to deal with the exploration-exploitation tradeoff and long-term constraints, respectively. With an effective integration of online learning and online control, we propose a Task-matching and Resource-allocation with Data-driven Bandit Learning (TRDBL) scheme. Our theoretical analysis shows that TRDBL achieves a sublinear regret bound with a time-averaged energy constraints guarantee in the long run. Through simulation results we not only verify our theoretical analysis but also demonstrate the outperformance of TRDBL in terms of both task latency reduction and energy efficiency.

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user.

Due to the vulnerability of the Internet of Things (IoT), it is indispensable to provide adequate security services. These services are generally implemented through security protocols or algorithms and running on energy-sensitive IoT devices. In order to design energy-efficient algorithms to prolong the lifetime of IoT devices, the energy characteristics of those algorithms should be analyzed primarily. In this paper, we conduct an integrated static analysis method with dynamic tracing to provide a quantitative energy profile for popular security algorithms such as AES128, RSA, and SHA256. Specifically, binary instructions executed in the invoked function are measured and counted through remotely debugging each program on the Arm development board. Then, the fine-grained energy consumption inside a program is revealed by combining the instruction statistics and instruction-level energy model. The experimental results show that the energy consumption of a program is mainly consumed by a few primary functions and CPU-memory interaction instructions, and hence the functions can be implemented in different ways to reduce energy consumption. This meaningful energy consumption evaluation method for security algorithms is able to guide to optimizing existing algorithms for embedded security.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In fog-assisted Internet-of-Things systems, it is a common practice to cache popular content at the network edge to achieve high quality of service. Due to uncertainties, in practice, such as unknown file popularities, the cache placement scheme design is still an open problem with unresolved challenges: 1) how to maintain time-averaged storage costs under budgets; 2) how to incorporate online learning to aid cache placement to minimize performance loss [also known as (a.k.a.) regret]; and 3) how to exploit offline historical information to further reduce regret. In this article, we formulate the cache placement problem with unknown file popularities as a constrained combinatorial multiarmed bandit problem. To solve the problem, we employ virtual queue techniques to manage time-averaged storage cost constraints, and adopt history-aware bandit learning methods to integrate offline historical information into the online learning procedure to handle the exploration–exploitation tradeoff. With an effective combination of online control and history-aware online learning, we devise a cache placement scheme with history-aware bandit learning calledCPHBL. Our theoretical analysis and simulations show that CPHBL achieves a sublinear time-averaged regret bound. Moreover, the simulation results verify CPHBL’s advantage over the deep reinforcement learning-based approach.

Fog computing is promising to solve the challenge caused by an extremely large amount of data on cloud computing. In this study, an efficient binary convolutional neural network with numerous skip connections (BNSC-Net) is proposed for fog computing to enable real-time smart industrial applications. This network features decomposition convolution kernels and concatenated feature maps. Moreover, the network performance is further improved through expanding the update interval of the straight-through estimator. To verify the performance, BNSC-Net is tested on two broadly used public data sets: 1) ImageNet and 2) CIFAR-10. An ablation study is first conducted to verify the effectiveness of the proposed improved operations, and results demonstrate that BNSC-Net can obviously increase the classification accuracy for both data sets. ImageNet-based classification results indicate that BNSC-Net can achieve 59.9% TOP-1 accuracy that is 2.6% higher than the state-of-the-art binary neural networks, such as projection convolutional neural networks (PCNNs). Finally, a subset with ten classes is selected from ImageNet to simulate the data collected in the smart industry with limited categories, based on which BNSC-Net also demonstrates an impressive classification performance with friendly memory and calculation requirements. Particularly, the receiver operating characteristic curves of BNSC-Net surpass that of the state-of-the-art algorithm DeepIns. Therefore, the proposed BNSC-Net is effective and efficient for building deep learning-enabled industrial applications on fog nodes.

Exploiting the sparse nature of user activity, compressed sensing (CS) has been a powerful technique to realize efficient user detection in grant-free nonorthogonal multiple access (NOMA). However, most of the existing CS-based multiuser detection schemes merely independently incorporate the temporal correlation in frame-based transmission or spatial correlation induced by multiantenna reception, leading to unsatisfactory user detection performance. Driven by the observation in the CS theory that the signal recovery performance could be enhanced by an increased number of sparse vectors with a common support set, in this article, we propose a novel joint user activity identification and channel estimation (JUICE) framework by integrating the temporal correlation of active user sets with multiantenna reception, which could achieve superior user detection performance. Specifically, we first formulate the JUICE as a Kronecker CS (KCS) problem to model the CS measurement process, by fully extracting the spatial-temporal structure of user activity. Then, based on the mined spatial-temporal structure of user activity, an adaptive subspace pursuit algorithm is developed, i.e., spatial-temporal structure enhanced adaptive subspace pursuit (STS-ASP), which could realize efficient multiuser detection. A distinct advantage of the proposed algorithm is that it does not require any prior knowledge (e.g., the number of active users and the noise level), by adaptively acquiring the number of active users and employing the cross-validation technique to appropriately terminate the iterative procedures. Extensive experimental evaluation is conducted, and the results corroborate the superiority of the proposed framework compared with the existing CS-based multiuser detection methods.

In fog computing systems, each fog node often maintains multiple interfaces to achieve simultaneous communications with end devices. To maximize the utilization of network capacities and avoid interference, a critical mission for each fog node is to allocate distinct channels to its interfaces, also known as multi-interface channel allocation, to maximize the total throughput by successful transmissions. However, the effective allocation scheme design is challenging because the full knowledge of channel state dynamics is often hard to attain in practice. Faced with such uncertainties, online learning is needed to cooperate with online decision making. In this article, we devise an integrated design to conduct such multi-interface channel allocation in fog computing systems. Specifically, by formulating the channel allocation problem in the settings of multiarmed bandit with multiple plays and leveraging Thompson sampling techniques, we propose a multi-interface channel allocation with binary feedback (MICA-B) scheme, which makes online channel allocation decisions through effective learning from binary transmission feedback. Our theoretical analysis shows that MICA-B achieves a sublinear O(logT) regret bound on the performance loss (also known as regret) over a finite time horizon T. Based on MICA-B, we further exploit structure information of channel characteristics and design constrained MICA-B (CoMICA-B) to improve learning efficiency. Further, we propose multi-interface channel allocation with multilevel feedback (MICA-M) which extends MICA to handle more general cases with multilevel feedback information. Our simulation results verify the effectiveness and robustness of MICA-B, CoMICA-B, and MICA-M in terms of regret reduction.

In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements. The background of a video usually lies in a low dimensional space and the foreground is usually sparse. More importantly, each video frame is a natural image that has textural patterns. By exploiting these properties, we develop a message passing algorithm termed offline denoising-based turbo message passing (DTMP). We show that these structural properties can be efficiently handled by the existing denoising techniques under the turbo message passing framework. We further extend the DTMP algorithm to the online scenario where the video data is collected in an online manner. The extension is based on the similarity/continuity between adjacent video frames. We adopt the optical flow method to refine the estimation of the foreground. We also adopt the sliding window based background estimation to reduce complexity. By exploiting the Gaussianity of messages, we develop the state evolution to characterize the per-iteration performance of offline and online DTMP. Comparing to the existing algorithms, DTMP can work at much lower compression rates, and can subtract the background successfully with a lower mean squared error and better visual quality for both offline and online compressed video background subtraction.

For systems that are based on network function virtualization (NFV), it remains a key challenge to conduct effective service chain composition with the lowest request latency and the minimum network congestion. In such an NFV system, users are usually non-cooperative, i.e., they compete with each other to optimize their own benefits. However, existing solutions often ignore such non-cooperative behaviors of users. What is more, they may fall short in the face of unexpected resource failures such as breakdown of virtual machines and loss of connections to users. In this article, we formulate the service chain composition problem with resource failures in NFV systems as a non-cooperative game, and show that such a game is a weighted potential game, aiming to search for the optimal Nash equilibrium (NE). By adopting Markov approximation techniques, we devise a distributed scheme called MH-SCCA, which achieves a provably near-optimal NE and adapts to resource failures in a timely manner. For comparison, we also propose two baseline schemes (DRL-SCCA and MCTS-SCCA) for centralized service chain composition that are based on deep reinforcement learning (DRL) and Monte Carlo tree search (MCTS) techniques, respectively. Our simulation results demonstrate the effectiveness of the three proposed schemes in terms of both latency reduction and congestion mitigation, as well as the adaptivity of MH-SCCA when faced with resource failures.

In software-defined networking (SDN) systems, it is a common practice to adopt a multi-controller design and control devolution techniques to improve the performance of the control plane. However, in such systems the decision-making for joint switch-controller association and control devolution often involves various uncertainties, e.g., the temporal variations of controller accessibility, and computation and communication costs of switches. In practice, statistics of such uncertainties are unattainable and need to be learned in an online fashion, calling for an integrated design of learning and control. In this article, we formulate a stochastic network optimization problem that aims to minimize time-average system costs and ensure queue stability. By transforming the problem into a combinatorial multi-armed bandit problem with long-term stability constraints, we adopt bandit learning methods and optimal control techniques to handle the exploration-exploitation tradeoff and long-term stability constraints, respectively. Through an integrated design of online learning and online control, we propose an effective Learning-Aided Switch-Controller Association and Control Devolution (LASAC) scheme. Our theoretical analysis and simulation results show that LASAC achieves a tunable tradeoff between queue stability and system cost reduction with a sublinear time-averaged regret bound over a finite time horizon.

For NFV systems, the key design space includes the function chaining for network requests and the resource scheduling for servers. The problem is challenging since NFV systems usually require multiple (often conflicting) design objectives and the computational efficiency of real-time decision making with limited information. Furthermore, the benefits of predictive scheduling to NFV systems still remain unexplored. In this article, we propose POSCARS, an efficient predictive and online service chaining and resource scheduling scheme that achieves tunable trade-offs among various system metrics with stability guarantee. Through a careful choice of granularity in system modeling, we acquire a better understanding of the trade-offs in our design space. By a non-trivial transformation, we decouple the complex optimization problem into a series of online sub-problems to achieve the optimality with only limited information. By employing randomized load balancing techniques, we propose three variants of POSCARS to reduce the overheads of decision making. Theoretical analysis and simulations show that POSCARS and its variants require only mild-value of future information to achieve near-optimal system cost with an ultra-low request response time.

Recent years have seen a widespread deployment of NB-IoT networks for massive machine-to-machine communication in the emerging 5G era. Unfortunately, the key aspects of NB-IoT networks, such as radio access performance and power consumption have not been well-understood due to lack of effective tools and closed nature of operational cellular infrastructure. In this paper, we develop NB-Scope - the first hardware NB-IoT diagnostic tool that supports fine-grained fusion of power and protocol traces. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 4 regions during a period of three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. Such a high performance variance can be attributed to several key factors including diverse network coverage levels, long tail power profile, and excessive control message repetitions. We then explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed, and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets.

In fog-assisted IoT systems, it is a common practice to offload tasks from IoT devices to their nearby fog nodes to reduce task processing latencies and energy consumptions. However, the design of online energy-efficient scheme is still an open problem because of various uncertainties in system dynamics such as processing capacities and transmission rates. Moreover, the decision-making process is constrained by resource limits on fog nodes and IoT devices, making the design even more complicated. In this paper, we formulate such a task offloading problem with unknown system dynamics as a combinatorial multi-armed bandit (CMAB) problem with long-term constraints on time-average energy consumptions. Through an effective integration of online learning and online control, we propose a Learning-Aided Green Offloading (LAGO) scheme. In LAGO, we employ bandit learning methods to handle the exploitation-exploration tradeoff and utilize virtual queue techniques to deal with the long-term constraints. Our theoretical analysis shows that LAGO can reduce the average task latency with an O(1/V + √(log T)/T) regret bound over time horizon T and satisfy the long-term time-average energy constraints, where V is a tunable positive parameter. We conduct extensive simulations to verify such theoretical results.

In fog-assisted IoT systems, it is a common practice to cache popular content at the network edge to achieve high quality of service. Due to various uncertainties such as unknown file popularities in practice, the design of effective cache placement scheme is still an open problem with two key challenges: 1) how to incorporate online learning into the cache placement process to minimize performance loss (a.k.a. regret), and 2) how to maintain caching costs under budgets in the long run. In this paper, we formulate the content cache placement problem with unknown file popularities as a combinatorial multi-armed bandit (CMAB) problem with long-term time-average constraints. We adopt bandit learning methods and virtual queue technique to deal with the exploration-exploitation tradeoff and long-term time-average constraints, respectively. With an effective integration of online learning and online control, we devise a learning-aided cache placement scheme called CPB (Cache Placement with Bandit Learning). Our theoretical analysis and simulation results show that CPB achieves a tunable sublinear regret over a finite time horizon and keeps caching costs within budgets in the long run.

In fog computing systems, each fog node often maintains multiple interfaces to achieve simultaneous communication with end devices. To maximize the utilization of network capacities and avoid interference, a critical mission for each fog node is to allocate distinct channels to its interfaces, a.k.a. multi-interface channel allocation, to maximize the total throughput by successful transmissions over time. However, the effective allocation scheme design is challenging because the full knowledge of channel state dynamics is often hard to attain in practice. Faced with such uncertainties, online learning is needed to cooperate with online decision making. In this paper, we devise an integrated design to conduct such multi-interface channel allocation in fog computing systems. Specifically, by formulating the channel allocation problem in the settings of multi-armed bandit with multiple plays and leveraging Thompson sampling techniques, we propose a Multi-Interface Channel Allocation with Binary feedback (MICAB) scheme, which makes online channel allocation decisions through effective learning from binary transmission feedback. Our theoretical analysis shows that MICA-B achieves a sublinear $O(\log T)$ regret bound over the performance loss (a.k.a regret) over a finite time horizon T. Further, we propose MICA-M which extends MICA to handle more general multi-level feedback information. Our simulation results verify the effectiveness and robustness of both MICA-B and MICA-M in terms of regret reduction.

Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately. In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings. To be specific, the side information includes question difficulty and three kinds of relations contained in a bipartite graph between questions and skills. To pre-train the question embeddings, we propose to use product-based neural networks to recover the side information. As a result, adopting the pre-trained embeddings in existing deep KT models significantly outperforms state-of-the-art baselines on three common KT datasets.

In software-defined networking (SDN) systems, it is a common practice to adopt a multi-controller design and control devolution techniques to improve the performance of the control plane. However, in such systems the decision making for joint switch-controller association and control devolution often involves various uncertainties, e.g., the temporal variations of controller accessibility, and computation and communication costs of switches. In practice, statistics of such uncertainties are unattainable and need to be learned in an online fashion, calling for an integrated design of learning and control. In this paper, we formulate a stochastic network optimization problem that aims to minimize time-average system costs and ensure queue stability. By transforming the problem into a combinatorial multi-armed bandit problem with long-term stability constraints, we adopt bandit learning methods and optimal control techniques to handle the exploration-exploitation tradeoff and long-term stability constraints, respectively. Through an integrated design of online learning and online control, we propose an effective Learning-Aided Switch-Controller Association and Control Devolution (LASAC) scheme. Our theoretical analysis and simulation results show that LASAC achieves a tunable tradeoff between queue stability and system cost reduction with a sublinear regret bound over a finite time horizon.

Spaced repetition technique aims at improving long-term memory retention for human students by exploiting repeated, spaced reviews of learning contents. The study of spaced repetition focuses on designing an optimal policy to schedule the learning contents. To the best of our knowledge, none of the existing methods based on reinforcement learning take into account the varying time intervals between two adjacent learning events of the student, which, however, are essential to determine real-world schedule. In this paper, we aim to learn a scheduling policy that fully exploits the varying time interval information with high sample efficiency. We propose the Time-Aware scheduler with Dyna-Style planning (TADS) approach: a sample-efficient reinforcement learning framework for realistic spaced repetition. TADS learns a Time-LSTM policy to select an optimal content according to the student's whole learning history and the time interval since the last learning event. Besides, Dyna-style planning is integrated into TADS to further improve the sample efficiency. We evaluate our approach on three environments built from synthetic data and real-world data based on well-recognized cognitive models. Empirical results demonstrate that TADS achieves superior performance against state-of-the-art algorithms.

Caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. In this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.

Fog computing is a promising solution to enable delay-sensitive applications in the Internet of Things (IoT). In this article, based on the simultaneous wireless information and power transfer (SWIPT) technology, we investigate the joint offloading of tasks and energy (JOTE) in fog-enabled IoT networks. Specifically, the task node is allowed to offload energy and tasks to multiple neighboring helper nodes in a time-division multiple access (TDMA) manner. When there are no task queues in the nodes, the offloading decision for each task is independent. We first find the offloading strategy to minimize the task execution delay as well as the energy consumption for a specific task and then, analyze the condition under which the JOTE is beneficial. We show that it becomes more and more desirable to offload both the tasks and the energy from the task node as the number of helper nodes gets large. When there are task queues in the nodes, the offloading decision for each task becomes temporally correlated. We then characterize the optimal strategies to offload the tasks and energy jointly over multiple time slots. An online offloading policy based on the Lyapunov optimization is then proposed to minimize the time average expected delay while stabilizing the system operation. Comprehensive numerical results corroborate our theoretical results and demonstrate the superior performance of the proposed JOTE algorithms.

In multitiered fog computing systems, to accelerate the processing of computation-intensive tasks for real-time Internet of Things (IoT) applications, resource-limited IoT devices can offload part of their workloads to nearby fog nodes, whereafter such workloads may be offloaded to upper-tier fog nodes with greater computation capacities. Such hierarchical offloading, though promising to shorten processing latencies, may also induce excessive power consumptions and latencies for wireless transmissions. With the temporal variation of various system dynamics, such a tradeoff makes it rather challenging to conduct effective and online offloading decision making. Meanwhile, the fundamental benefits of predictive offloading to fog computing systems still remain unexplored. In this article, we focus on the problem of dynamic offloading and resource allocation with traffic prediction in multitiered fog computing systems. By formulating the problem as a stochastic network optimization problem, we aim to minimize the time-average power consumptions with stability guarantee for all queues in the system. We exploit unique problem structures and propose predictive offloading and resource allocation (PORA), an efficient and distributed PORA scheme for multitiered fog computing systems. Our theoretical analysis and simulation results show that PORA incurs near-optimal power consumptions with queue stability guarantee. Furthermore, PORA requires only mild value of predictive information to achieve a notable latency reduction, even with the prediction errors.

Fog computing has been promoted to support delay-sensitive applications in future Internet of Things (IoT). For a general heterogeneous fog network consisting of many dispersive fog nodes (FNs), it may well happen that some of them have delay-sensitive tasks to process, i.e., task nodes (TNs), and some have spare resources to help the TNs to process tasks, i.e., helper nodes (HNs). It remains a fundamental challenge to effectively map multiple tasks or TNs into multiple HNs to minimize every task's service delay in a distributed manner, i.e., the multitask multihelper (MTMH) problem. The problem becomes more challenging as tasks are splittable, i.e., tasks can be divided into multiple subtasks and offloaded to multiple HNs to further reduce the service delay via the scheme similar to distributed computing, because it introduces the more complicated task division problem which results in a much larger and more complex solution space. To tackle this challenge, in this article, a generalized Nash equilibrium problem (GNEP), called parallel offloading of splittable tasks (POST), is formulated and studied thoroughly. The structural properties of the problem are characterized and thus the existence of generalized Nash equilibrium (GNE) is proven via the fixed-point theorem. Furthermore, the corresponding distributed task offloading algorithm is developed via the Gauss-Seidel-type method. The simulation results show that the proposed POST algorithm can offer much better performance in terms of the system average delay, individual delay, delay reduction ratio (DRR), and number of beneficial TNs, compared with the existing solution to the counterpart problem for nonsplittable tasks.

Radio-frequency identification (RFID) has pervasive applications in building ultralow-power ubiquitous networks, where backscatter communication during tag identification is neither reliable nor efficient. Inspired by the sparsity that only a few RFID tags communicate with the reader simultaneously, many compressed sensing (CS)-based schemes have been proposed to exploit the colliding tag responses to facilitate efficient tag identification. However, most of them suffer from huge ID search space and signature collision during the CS recovery process. To address these issues, we propose SCRIC, a novel sparsity-controlled RFID identification scheme using a random signature assignment, which achieves a faster and more robust identification performance. Specifically, to tackle identification failure caused by severe signature collision, we assign each active tag an access probability to control the sparsity and reduce signature collision. Theoretical analysis is given to prove that the signature collision probability of the proposed scheme is reduced compared with the existing random signature scheme and an optimal access probability is derived. Moreover, considering that conventional CS recovery algorithm relies heavily on the unpractical assumption that the active tag number is known precisely in advance, we integrate the cross validation (CV) into CS recovery algorithms and propose a greedy algorithm called the CV-based orthogonal matching pursuit (OMP-CV), which can reduce the tag identification false alarm rate without any prior knowledge. Extensive experimental results show that the proposed mechanism significantly outperforms the existing CS-based tag identification methods in terms of identification speed and robustness to noise.

In state-of-the-art deep learning, centralized deep learning forces end devices to pool their data in the cloud in order to train a global model on the joint data, while distributed deep learning requires a parameter server to mediate the training process among multiple end devices. However, none of these architectures scale gracefully to large-scale privacy and time-sensitive IoT applications. Therefore, we are motivated to propose a FOg-based pRivacy prEServing dEep lEarNing framework named FORESEEN, so as to achieve scalable, accurate yet private analytics. In FORESEEN, the intermediate fog nodes and the cloud collaboratively perform noisy training of deep neural networks (DNNs), while each end device and its connected fog node collaboratively perform fast, private yet accurate inference. To enhance robustness and ensure privacy, we put forward a collaborative noisy training algorithm and develop a novel representation perturber to perturb the extracted features by combining random projection, random noise addition and data nullification. To meet the required constraints of accuracy, memory and energy in IoT end devices, we build deep models with mixed-precision. Through these sophisticated designs, FORESEEN is able to not only preserve privacy but also maintain comparable inference performance. Extensive experimental results under different datasets, different inference schemes and different noise addition strategies validate the effectiveness of FORESEEN. Moreover, FORESEEN is capable of reducing the communication cost and providing inherent support for robustness and scalability.

Fog computing (FC) has the potential to process computation-intensive tasks in Industrial Internet of Things (IIoT) systems. In parallel with the development of FC, non-orthogonal multiple access (NOMA) has been recognized as a promising technique to significantly improve the spectrum efficiency. In this paper, a NOMA-based FC framework for IIoT systems is considered, where multiple task nodes offload their tasks via NOMA to multiple nearby helper nodes for execution. We formulate a joint task scheduling and subcarrier allocation problem, with an objective to minimize the total cost in terms of the delay and energy consumption, while taking into account the practical communication and computation constraints. Note that the task scheduling includes task, computation resource, and power allocations. Since the task and subcarrier allocations involve binary variables, it is challenging to obtain an optimal solution for such a combinatorial problem. To this end, we solve the task scheduling and subcarrier allocation problem in an online learning fashion. During the online learning process, we propose an iterative algorithm to jointly optimize the subcarrier allocation and task scheduling in each time episode. Simulation results show that the proposed scheme can significantly reduce the sum cost compared to the baseline schemes.

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

The papers in this special section focus on wireless big data. Big data, which has been following the exponential growth rates in different commercial areas, has profoundly changed the way we live. It has received considerable attention in both academic and industrial communities, in contexts such as mobile communications, distributed computing, e-health, intelligent transportation systems, wireless sensor networks, etc. In the meantime, the Internet of Things (IoT) scenarios considered in the Fifth Generation (5G) wireless communication systems are expected to create many novel applications and services with various requirements [1]. These new directions bring a dramatic increase and change in the amount and types of wireless data, thus driving wireless communications into a new era. Therefore, an in-depth analysis and understanding of wireless big data can greatly facilitate better system design and performance optimization, which will certainly benefit equipment vendors, network operators and service providers.

High-speed train (HST) communications in tunnels have attracted more and more research interests recently, especially within the framework of the fifth generation (5G) wireless networks. In this paper, based on cuboid-shape, three-dimensional (3D) non-stationary wideband geometry-based stochastic models (GBSMs) for HST tunnel scenarios are proposed. By considering the influence of the tunnel walls, a theoretical channel model is first established, which assumes clusters with an infinite number of scatterers randomly distributed on the tunnel walls. The corresponding simulation model is then developed and the method of equal areas is employed to obtain the discrete parameters, such as the azimuth and elevation angles. We derive and investigate the most important channel statistical properties of the proposed 3D GBSMs, including the time-variant autocorrelation function, spatial cross-correlation function, and Doppler power spectrum density. It is indicated that all statistical properties of the simulation model, verified by simulation results, can match very well with those of the theoretical model. Furthermore, a validation is presented by comparing the stationary regions of our proposed tunnel channel model to those of relevant measurement data.

Fog computing has been promoted to support delay-sensitive applications in future Internet of Things (IoT) and wireless networks. For a general heterogeneous fog network consisting of many dispersive Fog Nodes (FNs) with diverse resources and capabilities, some of them have delay-sensitive tasks to process, i.e., Task Nodes (TNs), while some have spare resources to help their neighboring TNs to process tasks, i.e., Helper Nodes (HNs). How to effectively map multiple tasks or TNs into multiple HNs to minimize every task's service delay in a distributed manner is a fundamental challenge, which is key to reap the full benefits of fog computing. The problem becomes more challenging when tasks can be divided into multiple subtasks to further reduce the service delay via distributed computing. To tackle this challenge, in this paper, a generalized nash equilibrium (NE) game called Parallel Scheduling of Multiple Tasks (PSMT) is formulated and studied. The structure properties of the problem are deduced and thus the existence of NE is proven by the fixed point theorem. Further, the corresponding distributed task scheduling algorithm/mechanism is developed via Gauss-Seidel-type method. Simulation results show that the proposed PSMT algorithm can converge in a fast way and offer much better performance in system average delay and number of beneficial TNs, comparing to the Paired Offloading of Multiple Tasks (POMT) solution to the counterpart problem not supporting distributed computing.

A key challenge in the design space of fog computing systems is online task scheduling, i.e., to allocate multiple types of resources to pending tasks that are constantly generated from end devices. It is challenging because of the online, intensive, and time-varying nature of task arrival, the varieties in the amounts and durations of task resource demands, as well as the unattainability of such priori information due to the online nature of task arrivals. To handle such uncertainties, an online task scheduler design with flexibility to process sequences of task arrivals with variable lengths is highly demanded. Existing works have adopted deep reinforcement learning (DRL) techniques to develop online task schedulers in a data-driven fashion by constructing them as neural networks and training using empirical data. However, hindered by the intrinsic restriction of the underlying neural network design, such schedulers often suffer from poor flexibility that may induce resource under- utilization, or overly fine-grained control that induces considerable overheads. In this paper, we address the above challenges by integrating pointer network architecture with the scheduler design, and proposing Neural Task Scheduling (NTS), an online flexible task scheduling scheme which effectively reduces average task slowdown to facilitate best quality-of-service. Simulation results show that NTS consistently outperforms state-of-the-art schemes under different settings.

Deep neural networks (DNNs) are the key techniques to enable edge/fog intelligence. By far, it remains challenging to conduct distributed deployment of DNN models onto resource-constrained fog nodes with low latency. Existing solutions adopt either model compression techniques to reduce the computation loads on fog nodes, or horizontal model partition techniques, which exploit particular communication and computation patterns to partition different layers of DNNs onto fog nodes. Nonetheless, sometimes even resource demands of particular layers can be unaffordable to fog nodes, which makes horizontal partition inadequate and calls for the joint design of vertical and horizontal model partition. Besides, model partition and compression may lead to degraded inference accuracy, but approaches to compensate such accuracy loss remain unexplored.In this paper, we propose an integrated efficient distributed deep learning (EDDL) framework to address the above challenges. Particularly, we adopt balanced incomplete block design (BIBD) methods to reduce computation loads on fog nodes by removing some data flows in DNNs in a systematic and structured manner. By leveraging grouped convolution techniques, we propose a practical scheme to conduct horizontal and vertical model partition jointly. Moreover, we integrate multi-task learning and ensemble learning techniques to further improve the inference accuracy. Simulation results verify the effectiveness of EDDL framework in achieving notable reduction in computation load and memory footprint with mild loss of inference accuracy.

For NFV systems, the key design space includes the function chaining for network requests and resource scheduling for servers. The problem is challenging since NFV systems usually require multiple (often conflicting) design objectives and the computational efficiency of decision making with limited information. Besides, the limits and benefits of predictive scheduling to NFV systems still remain unexplored. In this paper, we propose POSCARS, an efficient, distributed, and online algorithm that achieves a tunable trade-off between various system metrics with stability guarantee, while exploiting the power of predictive scheduling. Using randomized load balancing techniques, we propose three variants of POSCARS to further reduce sampling overheads. Theoretical analysis and trace-driven simulations show that POSCARS and its variants require only mild-value of future information to achieve a near- optimal average system cost while effectively shortening the average request response time.

For data stream processing systems such as Apache Heron, workload imbalance across processing instances often causes significant system performance degradation. To mitigate such issues, Apache Heron leverages a naive throttling-based back-pressure scheme, which may lead to unexpected system disruption. This calls for a finer-grained control to distribute data stream units (tuples) between successive instances, a.k.a. tuple scheduling, which well adapts to data stream variations and workload discrepancy. Besides, the benefits of predictive scheduling to data stream processing systems still remain unexplored. In this paper, we formulate tuple scheduling problem as a stochastic network optimization problem, with careful choices in the granularity of system modeling and decision making. With non-trivial transformation, we decouple the problem into a series of online subproblems. By exploiting unique subproblem structure, we propose POTUS, an efficient, online, and distributed scheduling scheme that employs the power of predictive scheduling but requires only limited system dynamics to achieve a tunable trade-off between communication cost reduction and system queue stability. Theoretical analysis and simulations show that POTUS effectively shortens response time with mild-value of future information, even in the face of misprediction. Our solution is also applicable to other data stream processing systems.

Fog computing has risen as an evolving architecture to support delay-sensitive applications in Internet of Things (IoT) and next generation mobile networks. For a typical heterogeneous fog network consisting of many fog nodes, some of them have different computation tasks while some have spare computation resources, which forms a multi-task multi-helper (MTMH) network. How to effectively map multiple tasks into multiple helper nodes to reduce the service delay is a key issue to be resolved. To tackle this issue, a computation offloading problem minimizing every task's delay is considered, from the perspective of individuals. This problem is further formulated into a non-cooperative game, i.e., MTMH computation offloading (MTMHCO) game, to model the competition among tasks for helpers. The existence of Nash equilibrium (NE) is guaranteed and an efficient distributed algorithm is developed to achieve an NE for the MTMHCO game. Theoretical analysis and simulation results show that the proposed algorithm can offer the nearoptimal performance in system average delay and achieve more number of beneficial task nodes, at two orders of magnitude lower complexity than a centralized optimal algorithm.

Fog computing (FC) has the potential to enable computation-intensive applications for the next generation wireless networks. In parallel with the development of FC, nonorthogonal multiple access (NOMA) has been recognized as a promising solution to improve the spectrum efficiency. In this paper, a NOMA-based FC system is considered, where multiple task nodes perform task scheduling via NOMA to a helper node, the helper node with abundant computation resource is required to compute the computation task from the task nodes. We formulate a joint task scheduling, computational resource allocation, and power allocation problem with an objective to minimize the sum cost (i.e., delay and energy consumptions for all task nodes) realizing energy-delay tradeoff. It is challenging to obtain an optimal policy for such a combinatorial optimization problem. To this end, we propose an online learning-based optimization framework to tackle this problem. Simulation results show that the proposed scheme significantly reduces the sum cost compared to the baselines.

Network functions virtualization (NFV) initiates a revolution of network service (NS) delivery by forming each NS as a chain of virtual network functions across commodity servers. However, it still remains a key challenge in NFV to decide the chains that induce short latency and low congestion, a.k.a. service chain composition problem. Existing works mainly resort to centralized solutions that require full knowledge of the network state to coordinate different users' traffic and NSs, overlooking privacy issues and the non-cooperative interactions among users. Moreover, handling the possible failures due to user/resource unavailability makes the problem even more challenging. By modeling the service chain composition problem with respect to both user and resource failures as a noncooperative game, we formulate the problem as searching the Nash Equilibrium (NE) with the optimal system performances. By exploiting the unique problem structure, we show that the game is a weighted potential game. We propose DISCCA, a distributed and low-complexity algorithm that guides the system towards the NE with short latency and low congestion, through decision making by individual users with local information. Results from extensive simulations show that DISCCA effectively achieves near-optimal system performances within mild-value of iterations, even in the presence of failures.

Fog computing is a promising paradigm that enables Internet-of-Things (IoT) applications with ultra-low latency and intensive computation. However, it is challenging to make efficient online decisions under varying system dynamics and intertwined power-latency tradeoffs. Moreover, the fundamental limits and benefits of predictive offloading in fog computing systems still remain unknown. In this paper, we study the problem of dynamic workload offloading and resource allocation in multi-tiered fog computing systems. By developing a fine-grained queue model and formulate a stochastic network optimization problem, we propose PORA, an efficient scheme that exploits predictive information to solve the problem. Results from our theoretical analysis and simulations show that PORA achieves a near-optimal power consumption with low latencies. Furthermore, PORA effectively reduces latencies with only mild-value of predictive information and it's robust against prediction errors.

For up-to-date data stream processing systems, e.g., Apache Heron, the distribution of processing units, a.k.a. instance placement, is determined in two stages, i.e., first mapping instances to containers and then mapping containers to servers. The placement, if improperly decided, can induce considerable traffic across servers and inefficient resource allocation. However, it is an open problem to decide the placement effectively, due to the complex interaction among instances, dependency between the decision making in two stages, and the trade-off between traffic reduction and resource utilization improvement. In this paper, we formulate such a problem as two sequential decision making problems. By adopting Monte Carlo Tree Search (MCTS) methods, we propose MIPS, i.e., a MCTS-based Instance Placement Scheme that decides the two-stage placement in a unified manner, achieving a well balance between computational efficiency and optimality. Results from simulations show that, with mild-value of samples, MIPS surpasses baseline schemes with significant improvement in both traffic reduction and utilization. To our best knowledge, this paper is the first to study and solve the two-staged mapping problem in such systems based on Heron.

Fog computing is a promising paradigm to perform low-latency computation for supporting the internet of things (IoT) applications. It enables provisioning resources and services to be closer for end users. Limited by the computing and storage resources, end users offload the computation-intensive tasks to the nearby fog nodes. However, due to mobility feature of the fog nodes, it's challenging to realize efficient task offloading. We rigorously formulate the task offloading problem for dynamic fog networks as an online stochastic optimization problem, and design offloading policies when the network is in stationary status and non-stationary status. When the fog network is in stationary status, we propose task offloading for the stationary status (TOS) algorithm to minimize the long-term average offloading delay. When the fog network is in non-stationary status, we propose two algorithms as task offloading for the non-stationary status using a sliding window (TON-SW) and task offloading for non-stationary status using a discount factor (TON-D) to minimize the average offloading delay. Besides, learning regret bounds of our algorithms are given. Numerical simulations show that our algorithms achieve a significant performance improvement compared to the upper-confidence bound (UCB) algorithm.

Motion tracking technologies have been widely used in mobile interaction applications, such as Virtual Reality (VR), healthy monitoring, and virtual touch control. Compared with dedicated hardware devices, mobile phones use reliable speakers and microphones, and can serve as ubiquitous devices for cheap acoustic-based motion tracking solutions. However, for complex indoor environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter named Multipath Effect Ratio (MER) is defined to indicate the multipath fading effect on received signals at different frequencies. Based on MER, a novel multipath effect mitigating technique is developed to calculate the phase change of acoustic signals and track the corresponding moving distance by using multiple speakers. A Phase-based Acoustic Motion Tracking (PAMT) method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, PAMT can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2mm and 4mm in one-dimensional and two-dimensional scenarios, respectively.

With the rapid growth of terminal equipments, the data traffic in the network has grown exponentially. In order to relieve the pressure of cloud computing on link delay, congestion and energy consumption, the promising fog computing is proposed. The fog network consists of several fog clusters. We consider a fog cluster in which a fog controller (FC) aims to schedule the idle fog nodes (FNs) to serve the task node (TN) while guaranteeing the quality of service (QoS) requirements of the TN. We design an ascending-bid auction mechanism to achieve this goal. In this mechanism, the FC is the auctioneer with the reward prices as its strategy and the FNs play the role of bidders with the task sizes as their strategies. The FC uses the bid prices to motivate the FNs to process more data for the TN. The utility function of FNs is proposed, considering the payment from the FC, the cost of task computational delay and energy consumption. The FNs determine the data sizes to be processed by maximizing their utilities. Numerical simulations indicate the satisfactory performance and verify the theoretical analysis, thereby our proposed mechanism results in a win-win solution under the condition of meeting the QoS.

Fog computing has risen as a promising architecture for future Internet of Things, 5G and embedded artificial intelligence applications with stringent service delay requirements along the cloud to things continuum. For a typical fog network consisting of heterogeneous fog nodes (FNs) with different computing resources and communication capabilities, how to effectively schedule complex computation tasks to multiple FNs in the neighborhood to achieve minimal service delay is a fundamental challenge. To tackle this problem, a new concept named processing efficiency (PE) is first defined to incorporate computing resources and communication capacities. Further, to minimize service delay in heterogeneous fog networks, a scalable, stable, and decentralized algorithm, namely dispersive stable task scheduling (DATS), is proposed and evaluated, which consists of two key components: 1) a PE-based progressive computing resources competition and 2) a QoE-oriented synchronized task scheduling. Theoretical proofs and simulation results show that the proposed DATS algorithm can achieve effective tradeoff between computing resources and communication capabilities, thus significantly reducing service delay in heterogeneous fog networks.

Motion tracking is attractive in what concerns a smart city environment, where citizens have to interact with Internet of Things (IoT) infrastructures spread all around one particular city. Motion tracking is important for smart services and location-based services in smart cities, since it provides natural ways for users to interact with the IoT infrastructures, such as the ability to recognize of a wide range of hand motion in real-time. Compared with dedicated hardware devices, ubiquitous devices with reliable speakers and microphones can be developed to achieve cheap acoustic-based motion tracking, which is appropriate for low-power and low-cost IoT applications. However, for complex urban environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter called multipath dispersion vector (MDV) is proposed to estimate and mitigate the impact of multipath fading on received signals using extreme learning machine. Based on MDV, a robust acoustic motion tracking (RAMTEL) method is proposed to calculate the moving distance based on the phase change of acoustic signals, and track the corresponding motion in 2-D plane by using multiple speakers. The method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, RAMTEL can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2 and 4 mm in 1-D and 2-D scenarios, respectively.

By providing shared and flexible communication, computation, and storage resources along the cloud-to-things continuum, fog computing has become an attractive technology to support delay-sensitive applications in Internet of Things (IoT) and future wireless networks. Consider a typical heterogeneous fog network consisting of different types of fog nodes (FNs), wherein some task nodes (TNs) have computation-intensive and delay-sensitive tasks, while some helper nodes (HNs) have spare computation resources for sharing with their neighboring nodes. In order to minimize the delay of every task, these TNs and HNs should be effectively associated in a distributed manner, which is the fundamental multi-task multi-helper (MTMH) problem. To tackle this challenging problem, a potential game called paired offloading of multiple tasks (POMT) is formulated and studied. Theoretical analysis proves the existence of the Nash equilibrium (NE) for this proposed game. Further, the corresponding POMT algorithm is developed for every TN to achieve the NE of the general game. The analytical and simulation results show that our POMT algorithm can offer the near-optimal performance in system average delay and delay reduction ratio (DRR), and achieve more number of beneficial TNs, at two orders of magnitude lower complexity than a centralized optimal algorithm for computation offloading.

Through offloading the computing tasks of the task nodes (TNs) to the fog nodes (FNs) located at the network edge, the fog network is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based networks. Unlike most existing researches based on the command-mode offloading and full capability report, this paper develops a general analytical model of the task scheduling among voluntary nodes (VNs) in fog networks, wherein the VNs voluntarily contribute their capabilities for serving their neighboring TNs. A novel delay-optimal task scheduling (DOTS) algorithm is proposed to obtain the delay-optimal offloading solution according to the reported capabilities of the VNs. Extensive simulations are carried out in a fog network, and the numerical results indicate that the proposed DOTS algorithm can effectively provide the optimal set of the helper nodes, subtask sizes, and the TN transmission power to minimize the overall task processing delay. Moreover, compared with the command-mode offloading, the voluntary-mode achieves more balanced offloading and a higher fairness level among the FNs.

Future Internet of Things (IoT) networks enabled with fog computing is promising to achieve lower processing delay and lighter link burden, by effectively offloading the computing tasks of the terminal nodes (TNs) to nearby fog nodes (FNs) at the network edge. Existing researches for the energy consumption in fog-enabled networks mostly focused on the minimization of the overall energy consumed by the task offloading services. However, fair offloading among multiple FNs while maintaining a satisfactory energy efficiency is of great significance for the sustainability of the fog-enabled IoT networks, especially in the scenarios with battery-powered FNs. In this paper, we propose a fair and energy-minimized task offloading (FEMTO) algorithm based on a fairness scheduling metric, taking three important characteristics into consideration, which include the task offloading energy consumption, the FN's historical average energy and the FN priority. The analytical results of the optimal target FN, the optimal TN transmission power, and the optimal subtask size are obtained in a fair and energy-minimized manner. Extensive simulations are carried out for the heterogeneous fog-enabled IoT network, and the numerical results indicate that the proposed FEMTO algorithm effectively determines the FN feasibility and the minimum energy consumption for the task offloading services. Moreover, a high and robust fairness level for the FNs' energy consumptions is obtained by the proposed FEMTO algorithm.

Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects. Such effects are not only caused by massive metal surfaces but also moving operators and logistical vehicles, which will lead to temporal fading effects. A three-layer impulse response framework is proposed to characterize such effects, in which both the specular and scattered components vary with the spacial movement of nearby objects. In this context, a received signal strength indicator will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power. Consequently, an augmented Kalman-filter-based link quality estimator has been designed to track both the specular and scattered power in the distribution parameter space with constant noise covariance matrices. Experiments from industrial sites show significantly increased accuracy.

Task offloading is a promising technology to exploit the available computational resources in spatially distributed fog nodes efficiently in the era of fog computing. In this paper, we look for an online task offloading strategy to minimize the long-term cost, which factors in the latency, the energy consumption, and the switching cost. To this end, we formulate a stochastic programming problem and the expectations of the system parameters are allowed to change abruptly at unknown time instants. Meanwhile, we consider the fact that the queried nodes can only feed back the processing results after finishing the tasks. Then we put forth an effective bandit learning algorithm, i.e., the BLOT, to solve this challenging stochastic programming under the non-stationary bandit model. We also demonstrate that our proposed BLOT algorithm is asymptotically optimal in a non-stationary fog-enabled network. Numerical experiments further verify the superb performance of BLOT.

Demand response (DR) programs are designed to affect the energy consumption behavior of end-users in smart grid. However, most existing pricing designs for DR programs ignore the influence of end-users’s diversity and personal preference. Thus, in this paper, we investigate an incentive pricing design based on the utility maximization rule with consideration of end-users’ preference and appliances’ operational patterns. In particular, the utility company determines the pricing policy by trading off the budget revenue and social obligation, while each end-user aims to maximize their own utility profits with high satisfaction level by scheduling multiclass appliances. We formulate the conflict and cooperative relationship between the utility company and end-users as a Stackelberg game, and the equilibrium points are obtained by the backward induction method, which exists and is unique. At the equilibrium, the utility company adopts real-time pricing (RTP) scheme to coordinate end-users to fulfill the benefit of themselves, i.e., under such price, end-users automatically maximize overall utility profits of the overall system. We propose a distributed algorithm and an adaptive pricing scheme for the utility company and end-users to jointly achieve the best performance of the entire system. Finally, extensive simulation results based on real operation data show the effectiveness of the proposed scheme.

Fog computing is expected to excavate and make full use of the inherent idle communication, cache, computation, and control resources of massive devices, and to relieve the pressure of cloud computing on link congestion, delay, and energy consumption. However, how to accurately sense the resources of all fog nodes (FNs) in real time is vital to efficient resource scheduling in the fog computing networks. Frequent sensing will result in both high sensing accuracy at the fog controller (FC) and cost at the FNs. To this end, we propose a novel incentive framework to motivate the FNs to feed back their resource sensing data frequently to the FC based on Stackelberg game. The FC plays as the leader with the sensing reward prices as its strategy, and the FNs play as the followers with the sensing frequency as their strategies. The utility functions of the FC and the FNs are proposed, considering the payment for resource sensing, the accuracy of sensing and the cost of sensing. The existences of the global optimum of both utilities for the FC and the FNs are proved. Closed-form solutions for the optimal sensing frequencies of the FNs are derived. Numerous simulations are done verifying our theoretical analyses and indicating the importance of our proposed incentive framework for resource sensing in the fog computing network.

In wireless sensor networks (WSN s), energy is always precious for sensor nodes. To save energy, censoring is introduced to cut the total number of transmission by only transmitting informative data. This algorithm, however, ignores the energy consumption during the delivery of parameters, which can be significant comparing to the saved power. In this paper, we consider the adaptive censoring from the energy perspective. A distributed censoring algorithm with energy constraint is developed that allows sensor nodes to make autonomous decisions on whether to transmit the incoming data. We show that with the proposed algorithm, the overall energy consumption of the WSN s is reduced, while the performance loss in terms of the estimation error is negligible. Simulation results validate its effectiveness.

In this paper, we investigate the joint user pairing and resource allocation (UP-RA) problem in uplink multi-user MIMO or virtual MIMO (V-MIMO) systems. Due to the practical transmission constraints, solving such a joint optimization problem is NP-hard. Therefore, we study this problem from a novel perspective, and propose a universal and efficient heuristic algorithm. First, we formulate the UP-RA problem as a special many-to-one matching problem with constraints. Different from most existing researches, we consider a universal scheduling problem, instead of a particular one. Then, we modify the classical deferred acceptance (DA) algorithm which is designed for the common many-to-one matching problem, and develop a lowcomplex heuristic algorithm called adapted deferred acceptance (ADA) algorithm for the UP-RA problem. Numerical results demonstrate that the ADA algorithm achieves robust and better performance compared with other representative algorithms in many scenarios.

Fog computing is seen as a promising approach to perform computation-intensive and latency-critical applications for mobile devices. Existing results mainly focus on power consumption or delay minimization problems which are both from the perspective of end devices. In this work, we further investigate the communication and computation resources minimization problem from the standpoint of the fog server operators instead. In practice, fog server operators must at first decide how many communication resources, e.g., bandwidth, and computation units should be deployed in order to satisfy various requirements from their serving devices. Meanwhile, the operators have to maximize their profits by balancing the cost of the deployed resources and devices' satisfaction. Motivated by such requirements, we formulate the problem as the minimization of the weighted bandwidth and computation resources with per-task delay requirement constraints. We prove that the optimization problem is convex and further derive important properties for the relationship between the required delay and the available resources. It indicates that there is an unachievable region for the completion time, where the task can not be completed within the required delay no matter how large the amount of communication bandwidth and the computation resources at the fog server is. We evaluate the performance of the feasible solutions for the mentioned problem through extensive numerical simulations and the numerical results have verified the analysis and the conclusions.

Fog computing is expected to cope with the long latency and heavy link burden existing in cloud- based networks. Computing tasks of the terminal node can be offloaded to nearby fog nodes thus achieving much lower processing delay than that of cloud-based networks. Existing researches for energy consumption in fog computing networks mainly focus on the total energy consumed by processing a task. However, fair offloading among multiple fog nodes while maintaining a low task delay is of great significance especially for the battery-powered fog nodes. This paper proposes an analytical framework of the fair task offloading for fog computing networks. Task delay and the corresponding energy consumption are formulated. Then, a fairness scheduling metric is constructed for each fog node. A two-step Fair Task Offloading (FTO) scheme is proposed finally, which selects offloading fog nodes according to the fairness metric and then offloads tasks to the selected nodes based on a rule that minimizes the task delay. Numerical simulations and comparisons indicate the satisfactory performance of the proposed task offloading scheme for maintaining a relatively high fairness index for energy consumption and low task delay in the fog computing networks.

Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider customers that operate different appliances and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each customer wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the customers selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme.

The paradigms of Fog and Edge Computing along with Internet of Things (IoT) promise to make everything, especially smart devices, as part of the Internet environment, where highly-centralized computing infrastructures are gradually decentralized into micro data centers deployed at the edges of each network. As a result, access latency has been reduced dramatically because of the decreases in geographical distance. So far, a great deal of research effort has been focused on how to satisfy the ever-increasing demand for Fog and Edge Computing resources. Correspondingly, the traditional centralized power supply system need to be decentralized among other computing and communications resources. However, how to design, operate and manage a fully distributed power supply system to support the same service agreement level as in cloud computing under fog and edge computing largely remains unknown. In this article, we propose and develop a new power supply system for Fog and Edge computing based on our previous work on digital energy systems. Experimental results based on a real-world case study of the proposed system are presented to validate the effectiveness and efficiency of the proposed power supply system.

Motion tracking, as a mechanism of mobile interaction, allows devices to get fine-gained user input by locating the real-time position of target devices (e.g., smart phones, smart watches) in the air. With the proliferation of mobile devices and smart multimedia devices (e.g., smart TV, home audio system), the ubiquitous speakers and microphones in the devices provide more diverse ways of acoustic-based mobile interaction. In this demonstration, we propose a fine-gained motion tracking system, which can be developed on commercial mobile devices and track the devices with millimeter level (mm-level) accuracy. We first compensate the phase offset between receiver and audio source at each frequency. We then use the acoustic phase change at receiver to achieve accurate distance measurement. Finally, we implement our system on off-the-shelf devices, and achieve a fine-gained motion tracking in two-dimensional space. Our experiments show that our system achieves high accuracy as well as high sensitivity: our system could detect the sight and slow movement caused by human breathing for example.

This paper studies the maximization of the weighted sum energy efficiency (WSEE). We derive a first-order optimal algorithm applicable to a wide class of communication scenarios exhibiting very fast convergence. We also discuss how to leverage monotonic optimization and fractional programming to obtain a global optimal solution at the cost of higher computational complexity. The WSEE of interference networks is studied in detail with an application to relay-assisted multi-cell communication. This scenario is modeled as a non-regenerative multi-way relay channel and the achievable rate region is derived. We apply the proposed algorithm to this scenario and compare its performance to the global optimal algorithm. The results indicate that the proposed algorithm often achieves the global optimal solution and is close to it otherwise. Convergence is achieved within 10 iterations, while the global optimal solution may require more than 106iterations.

Virtual multiple‐input multiple‐output (V‐MIMO) is an advanced technology in uplink systems, and a well‐designed user pairing and resource allocation scheme in V‐MIMO systems can effectively exploit multi‐user diversity to yield a significant performance gain. Recently, main attention has been devoted to the crucial user pairing issue with consideration of three key metrics, i.e. spectrum efficiency, energy efficiency and fairness. In this study, the authors investigate the unified tradeoff among all the three metrics for V‐MIMO systems with user pairing and resource allocation. They first formulate the three key metrics in a united form and derive a straightforward unified criterion based on the user‐specific preference to depict system performances. Through discussing several preference‐based cases, they observe that a portion of spectrum efficiency is considered redundantly. Thus, a modified unified criterion is proposed to remove the partial redundancy provided by spectrum efficiency contribution to the objective function. Based on the two unified criteria, they propose a user pairing scheme in flat fading channels and a joint user pairing and resource allocation scheme in frequency‐selective fading channels to balance the tradeoff flexibly. Simulation results show that, compared with existing schemes, the proposed scheme achieves a dynamical tradeoff among spectrum efficiency, energy efficiency and fairness.

A homogeneous fog network is defined as a group of peer nodes with sharable computing and storage resources, as well as spare spectrum for node-to-node/device-to-device communications and task scheduling. It promotes more intelligent applications and services in different Internet of Things (IoT) scenarios, thanks to effective collaborations among neighboring fog nodes via cognitive spectrum access techniques. In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency (EE) in homogeneous fog networks. With this model, the tradeoff relationship between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the EE optimization problem for future intelligent IoT applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy-efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between EE and task scheduling performance in homogeneous fog networks. Compared with traditional task scheduling strategies, the proposed MEETS algorithm can achieve much better EE performance under different network parameters and service conditions.

Vehicular ad hoc networks, wireless sensor networks, Internet of Things, and mobile device-to-device communications can be modeled as different homogeneous fog networks, wherein similar terminals/things/devices/nodes are sharing their computation, communication, and storage resources in the neighborhood for achieving better system performance through effective collaborations. It is very desirable, but quite challenging, to simultaneously reduce service delay and energy consumption in such networks for delay-sensitive and energyconstraint applications, e.g., virtual reality and online 3-D gaming on mobile devices. In this paper, a cross-layer analytical framework is developed to formulate and study the balance between service delay and energy consumption. An effective control parameter V is derived to characterize their tradeoff relationship during dynamic task scheduling processes in fog networks. Combining this analysis with Lyapunov optimization techniques, a novel delay energy balanced tasking scheduling (DEBTS) algorithm is proposed to minimize the overall energy consumption while reducing average service delay and delay jitter. It is proved that DEBTS can achieve the theoretical [O(1/V), O(V)] tradeoff between these two performance metrics. Further, extensive simulation results show that DEBTS can offer much better delay-energy performance in task scheduling challenges. Specifically, for a typical V value of 4 × 104, DEBTS can save 26% and 29% more energy, and at the same time, reduce average service delay by 29% and 32%, than traditional random scheduling and least busy scheduling algorithms, respectively.

Fog computing has recently emerged as a promising technique in content delivery wireless networks to alleviate the heavy bursty traffic burdens on backhaul connections. In order to improve the overall system performance, in terms of network throughput, service delay and fairness, it is very crucial and challenging to jointly optimize node assignments at control tier and resource allocation at access tier under dynamic user requirements and wireless network conditions. To solve this problem, in this paper, a fog-enabled multitier network architecture is proposed to model a typical content delivery wireless network with heterogeneous node capabilities in computing, communication, and storage. Further, based on Lyapunov optimization techniques, a new online low-complexity algorithm, namely fogenabled multitier operations scheduling (FEMOS), is developed to decompose the original complicated problem into two operations across different tiers. Rigorous performance analysis derives the tradeoff relationship between average network throughput and service delay, i.e., [O(1/V), O(V)] with a control parameter V, under FEMOS algorithm in dynamic wireless networks. For different network sizes and traffic loads, extensive simulation results show that FEMOS is a fair and efficient algorithm for all user terminals and, more importantly, it can offer much better performance, in terms of network throughput, service delay, and queue backlog, than traditional node assignment and resource allocation algorithms.

In the era of big data, adaptive censoring (AC) provides us a natural option of trimming data by only keeping the statistical informative data. However, the data chosen by AC may arrive in clusters, which do not relieve the computational resource requirement as expected. In this letter, we exploit queuing theory to model a single sink node with abundant sensor nodes. By adding a buffer to censored distributed wireless sensor networks (WSNs), the uncensored data can be modeled as a queue. With the buffer, the new algorithm entails simple, closed-form updates, and has no loss in terms of estimation accuracy comparing to the original AC method. The proposed model can further reduce the communication cost of distributed WSNs. The proposed model is illustrated in a linear regression setting. Numerical results validate the effectiveness of the proposed model in dealing with data congestion problem.

In this paper, a three-dimensional (3D) non-stationary wideband multiple-input multiple-output (MIMO) channel model based on the WINNER+ channel model is proposed. The angular distributions of clusters in both the horizontal and vertical planes are jointly considered. The receiver and clusters can be moving, which makes the model more general. Parameters, including number of clusters, powers, delays, azimuth angles of departure (AAoDs), azimuth angles of arrival (AAoAs), elevation angles of departure (EAoDs), and elevation angles of arrival (EAoAs) are time-variant. The cluster time evolution is modeled using a birth-death process. Statistical properties, including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), Doppler power spectrum density (PSD), level-crossing rate (LCR), average fading duration (AFD), and stationary interval are investigated and analyzed. The LCR, AFD, and stationary interval of the proposed channel model are validated against the measurement data. Numerical and simulation results show that the proposed channel model has the ability to reproduce the main properties of real non-stationary channels. Furthermore, the proposed channel model can be adapted to various communication scenarios by adjusting different parameter values.

Device-to-device (D2D) communication has become one important part of next-generation mobile networks particularly due to the booming of proximity-based services, e.g. the ProSe standardized in LTE. However, D2D communications may create strong interference to nearby users that are sharing the same spectrum. Thus, interference management in a D2D-enabled cooperative network is critical. In this paper, the time reuse problem and its distributed solution in a D2D-enabled cooperative network are investigated. Even though the total number of possible time reuse patterns increases exponentially with the quantity of the users, the authors first show that the optimal network performance can be achieved by only activating a limited number of time reuse patterns. The effects of the incentive mechanism on the reuse pattern selection are also investigated. Meanwhile, the set of active reuse patterns are determined efficiently with the Frank-Wolfe method. For a specific set of active time reuse patterns, the authors further show that the optimal resource allocation problem can be formulated as one consensus-building problem. Based on the alternating-direction method of multipliers, one low-complexity algorithm is proposed to determine the optimal resource allocations in a distributed fashion. Numerical simulations are carried out to corroborate our designs.

In this paper, we revisit the idea of using deep neural network for channel equalization to account for nonlinear channel distortions as well as temporal variations of radio signals. Our insight is leveraging the the shift-invariant properties of the convolutional neural network (CNN) to learn matched filters analogous to the tap weights of conventional equalizer. Then we feed the learned filters into a subsequent recurrent neural network (RNN) with long-short-term-memory (LSTM) cells for temporal modeling of the channel. We train our proposed CNN-RNN (CRNN) equalizer based on real testbed collected data and enlarge the generalization ability of the learned network model as much as we can to adapt to different channel conditions. Experimental results show that the SER performance for our designated single-input single-output (SISO) system which utilises quadrature phase shift keying (QPSK) modulation scheme with the proposed CRNN-based channel equalizer outperforms that of other equalizers by average 2 to 5 dB at low signal-to-noise ratio (SNR).

Fog computing has emerged as a promising solution for the Internet of Things (IoT) and next generation mobile networks. As an extension to cloud computing, it enables service provisioning along the continuum from the cloud to things for reducing latency and bandwidth demands, and for empowering end users in their vicinity. Specifically, fog computing can pool resources anywhere along this continuum and can deploy its service anywhere in this range, including in the cloud, at the edge or on the things. This new computing model will change the way we deliver services to customers and inspire new business models. It liberates the market from only the large organizations who can afford to build and operate powerful servers and huge data centers to also allow small companies and even an individual person to deploy and operate computing, storage, and control services at different scales in an IoT environment. To achieve the full potential of fog computing, we must address challenges from service providers' perspectives. We propose Fog As A Service Technology (FA2ST) and its architecture - a multi-level fog computing system for end-to-end service enablement in IoT.

Virtual MIMO (V-MIMO) is an advanced technology in uplink systems, and a well-designed user pairing scheme in V-MIMO systems can effectively exploit multi-user diversity to yield a significant performance gain. Recently, main attention has been devoted to the crucial user pairing issue with consideration of three key metrics, i.e., spectrum efficiency, energy efficiency and fairness. In this paper, we investigate the unified tradeoff among all the three metrics for V-MIMO systems with user pairing. We first formulate the three key metrics in a united form and derive a straightforward unified criterion (SUC) based on the user-specific preference to depict system performances. Through discussing several preference-based cases of the SUC, we observe that a portion of spectrum efficiency is considered redundantly. Thus, a modified unified criterion is proposed to remove the partial redundancy provided by spectrum efficiency contribution to the objective function. Based on the two unified criteria, we propose a user pairing scheme to balance the tradeoff flexibly. Simulation results show that, compared with existing user pairing schemes, the proposed scheme achieves a dynamical tradeoff among spectrum efficiency, energy efficiency and fairness.

The D2D-Relay mechanism, which integrates device-to- device (D2D) communications and relay technology, is promising in system performance improvement. This paper focuses on studying what kind of benefits the D2D-Relay mechanism could bring into a wireless communication system during the uplink transmission. To comprehensively analyze the system performance, we use a metric, which consists of the spectral efficiency (SE) and the energy efficiency (EE), as an indicator. With this criteria in mind, we theoretically describe the influence from users' preferences to the SE and the EE in detail. Noting that user pairing is an important part of this mechanism, we evaluate the performance of the D2D- Relay mechanism with three typical pairing algorithms based on different criteria. The simulation results conform to our derivations and reveal the performance gain brought by applying the D2D-Relay mechanism. In addition, with different configurations of the number of the users and their preferences to the SE and the EE, the most appropriate algorithm is suggested for the practical application.

Caching is a promising technique to alleviate the capacity bottleneck of content-centric wireless networks (CCWNs). Most existing work of wireless caching networks focuses on the content placement policy, while some other interesting problems including dynamic user-AP association and predictive scheduling were rarely investigated. In this paper, we make the first attempt to investigate the benefit of such new degrees of freedom in wireless caching networks. Based on predictive service model, we formulate an average network throughput maximization problem with request queue stability constraints. We design the predictive user-AP association and resource allocation (P-UARA) algorithm, which is an online algorithm with theoretically guaranteed performance and does not require any statistical information of the system dynamics. Given the NP-hard user-AP association and bandwidth allocation optimization problem in P-UARA, we reformulate it and show the equivalence to the problem of modular function maximization subject to two matroid constraints. We propose an efficient greedy algorithm that guarantees a low bound 1/2 of the optimal value. Simulation results validate the theoretical analysis of our proposed algorithm and demonstrate the benefit of dynamic user-AP association and the predictive scheduling.

Massive multiple-input multiple-output (MIMO) technology and millimeter wave (mmWave) communication are key technologies for the fifth generation (5G) wireless communications. The combination of mmWave and massive MIMO has the potential to dramatically improve wireless access and throughput performance. Such systems benefit from large available signal bandwidths and small antenna form factor. In the literature, most of the massive MIMO channel measurements are carried out at sub-6 GHz frequency bands, while the effects caused by large antenna arrays at mmWave bands have not been studied yet. In this paper, we conduct channel measurements at 11, 16, 28, and 38 GHz frequency bands combined with large antenna arrays in an indoor office environment. The space-alternating generalized expectation-maximization (SAGE) algorithm is applied to obtain the multipath component (MPC) parameters. New propagation characteristics like spherical wavefront, cluster birth-death, and non-stationarity over antenna array axis are validated for the four mmWave bands by investigating the temporal-spatial channel characteristics like power delay profile (PDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS). The results indicate that massive MIMO effects should be fully considered for mmWave channel models under systems with large antenna arrays.

Existing researches on the theoretical analysis for Proportional Fair (PF) scheduling are mostly based on the full buffer assumption while the user always has a bursty traffic in reality. In this paper, an analytical framework for PF scheduling in an Orthogonal Frequency Division Multiple Access (OFDMA) wireless network is proposed, and the user traffic is set to follow a poisson arrival process. Detailed analysis for each user's Resource Block (RB) competition status is carried out, and an M/M/1 queuing model is adopted for the packet transmission process of each user, with which the analytical expressions for user throughput and packet latency are obtained. Simulation in a single-cell OFDMA network is carried out and the comparision between simulation and analysis results confirms the accuracy of our analysis.

Compared with the traditional power grid, Energy Internet is motivated by the concept of intelligent energy sharing and coordination, achieving higher penetration of renewable energy and economic saving. In this paper, we propose a new framework for the time-slotted Peer-to-Peer (P2P) energy sharing and coordination in Energy Internet, which aims to achieve flexible and efficient distributed energy management and control. In this framework, users are equipped with distributed generators (DGs), distributed energy storage systems (DESs) and smart meters; the P2P energy sharing fashion is supported, where users can buy/sell electric from/to utility company and their neighboring users. The energy sharing and coordination problem is formulated as a convex optimization problem with the objective to minimize the economic cost of users. Then, a distributed algorithm is proposed, in combination with alternating direction method of multipliers (ADMM). On the basis of a real-world dataset of renewable energy and real-time electricity price, both analytical and numerical results show the effectiveness of the proposed framework and algorithm in terms of not only fast convergence in a time slot but also economic saving prominently for a long time application.

As more than 2.5 quintillion bytes of data are generated every day, the era of big data is undoubtedly upon us. Running analysis on extensive datasets is a challenge. Fortunately, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference in many cases. Censoring provides us a natural option for data reduction. However, the data chosen by censoring occur non-uniformly, which may not relieve the computational resource requirement. In this paper, we propose a dynamic, queuing method to smooth out the data processing without sacrificing the convergence performance of censoring. The proposed method entails simple, closed-form updates, and has no loss in terms of accuracy comparing to the original adaptive censoring method. Simulation results validate its effectiveness.

Millimeter wave (mmWave) communication has been a key technology for the fifth generation (5G) wireless communications. There have been various mmWave channel measurements. However, many measurements in the literature are conducted with different configurations, which may have large impacts on the propagation channel characteristics, and make the comparison of propagation channel characteristics for different mmWave bands impossible. In this paper, we carry out channel measurements at 11, 16, 28, and 38 GHz bands in an indoor environment using a vector network analyzer (VNA). The space-alternating generalized expectation-maximization (SAGE) algorithm is used to obtain the multipath component (MPC) parameters including three dimensional (3D) angular domain information. The propagation characteristics like average power delay profile (APDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS) are shown and compared for the four frequency bands. The results show similar properties for different bands and indicate the possibility of the derivation of a unified channel model framework for 10-40 GHz bands.

Capacity-coverage tradeoff balancing is a classical but critical problem for practical wireless multi-cell network optimization. Increasing the average capacity is often at the expense of decreasing the cell coverage and vice versa. It becomes more complex in massive multiple-input/multiple-output (MIMO) networks to balance the above two performance indicators since capacity is much highly improved while the inter-cell interference is also increased greatly under massive antenna scenarios. In this work, a novel system-level optimization parameter is proposed, namely minimum user signal to interference plus noise ratio (user-SINR) threshold, to solve the above balancing problem. By utilizing this new parameter, the corresponding user scheduling and inter-cell interference coordination scheme are further provided for cooperative massive MIMO networks. Performance analysis and numerical results show that the proposed minimum user-SINR threshold is a very effective optimization parameter to achieve the capacity-coverage tradeoff performance with lower optimization complexity, combined with the proposed scheduling and interference coordination schemes.

Most millimeter wave (mmWave) channel measurements are conducted with different configurations, which may have large impacts on propagation channel characteristics. In addition, the comparison of different mmWave bands is scarce. Moreover, mmWave massive multiple-input multiple-output (MIMO) channel measurements are absent, and new propagation properties caused by large antenna arrays have rarely been studied yet. In this paper, we carry out mmWave massive MIMO channel measurements at 11-, 16-, 28-, and 38-GHz bands in indoor environments. The space-alternating generalized expectation-maximization algorithm is applied to process the measurement data. Important statistical properties, such as average power delay profile, power azimuth profile, power elevation profile, root mean square delay spread, azimuth angular spread, elevation angular spread, and their cumulative distribution functions and correlation properties, are obtained and compared for different bands. New massive MIMO propagation properties, such as spherical wavefront, cluster birth-death, and non-stationarity over the antenna array, are validated for the four mmWave bands by investigating the variations of channel parameters. Two channel models are used to verify the measurements. The results indicate that massive MIMO effects should be fully characterized for mmWave massive MIMO systems.

In modern mobile communication networks, such as 3G and 4G networks, sectorized antennas have been widely used to divide each cell into multiple sectors in order to improve coverage, spectrum efficiency, and quality of service. Large-scale path loss from a transmitting antenna to a receiving antenna should include: 1) propagation attenuation that depends on transmission distance; 2) shadowing that depends on surrounding environment; and 3) antenna loss that depends on a sectorized antenna pattern and transmission angle. An in-depth analysis of statistical characteristics of large-scale path losses involving with these three factors is crucial for the design, operation, evaluation, and optimization of modern sectorized wireless networks. In this paper, a sectorized antenna pattern is, for the first time, considered in the derivation of a closed-form expression of a probability density function (pdf) of large-scale path losses. Specifically, we first discover that the normalized pdf of propagation attenuation plus shadowing, which can be approximated by the Gaussian mixture model (GMM) with all system parameters, is fully determined by our newly defined metric 10/ln 10β/σs, namely, the attenuation exponent β to standard deviation of shadowing σsratio (ASR). The convolution of GMM and antenna loss statistics is elaborately transformed to a series of differential equations. A closed-form pdf of large-scale path losses with sectorized antenna pattern can be obtained by solving these differential equations. To reduce the computational complexity, we further prove that the exciting sources of these differential equations can be tightly approximated by weighted Gaussian functions, and thus, the final solutions (i.e., pdf of path losses) can be derived in the form of Gaussian and Dawson functions. Our analytical results are verified by extensive numerical computation and Monte Carlo simulation results, e.g., the impact of ASR on the shape of pdf of propagation attenuation plus shadowing. Compared with traditional Gaussian-fitting approach, our newly derived pdf of large-scale path losses with sectorized antenna patterns is at least two orders of magnitude more accurate in terms of Kullback-Leibler divergence under typical propagation attenuation and shadowing conditions.

Due to the scarcity of the licensed spectrum, Licensed-Assisted Access Long-Term Evolution (LAA- LTE) network can be deployed in unlicensed spectrum, which is currently occupied by different Wi-Fi systems. It is a very challenging problem to ensure fair coexistence between LAA-LTE and Wi-Fi networks, in terms of spectrum sharing and traffic management. To solve this problem, a Fair Downlink Traffic Management (FDTM) scheme is proposed in this paper for hybrid LAA-LTE/Wi-Fi networks. By using the genetic algorithm, FDTM tunes the minimum Contention Window (CW min ) values and assigns feasible weights for the LAA eNBs with different traffic loads, thus to achieve (1) fair spectrum sharing with the existing Wi-Fi networks in unlicensed bands, and (2) fair service differentiation for downlink LAA-LTE traffic. Numerical results show our FDTM scheme can guarantee the throughput of WiFi networks in shared unlicensed spectrum, while supporting proportional fairness for the LAA eNBs with different weights and CW min values.

Wireless caching at users' devices in mobile social network is considered to be a promising solution to alleviate backhaul overload in future wireless networks. However, most of the current works propose caching schemes based on heuristic reasoning and intuition with poor performance or high complexity which are impractical due to individual devices' computing capacity restriction. In this paper, we design a cooperative caching scheme aimed at maximizing hit ratio, incorporating probabilistic modeling of mobility and user interests patterns from mobile social networks. Furthermore, we reformulate this optimization problem into a submodular function maximization and propose a semigradient-based cooperative caching scheme, while this scheme's efficiency is shown to significantly outperform the greedy caching by 99.6%.

This paper first proposes a novel three dimensional (3-D) wideband multi-confocal ellipsoid model for wireless multiple-input multiple-output (MIMO) communication channels. The proposed 3-D geometry-based stochastic model (GBSM) describes the channel in both the azimuth direction and elevation direction, including delay, Doppler frequency, angle of departure (AoD), and angle of arrive (AoA). It can be shown that the ellipsoid model can capture the effect of the clusters more accurately than the previous elliptic-cylinder model. Using the proposed theoretical model as the reference model, the corresponding simulation model is also derived. Numerical results demonstrate that their statistical properties can match well.

Frequency band propagation characteristics measurement and modeling is a prerequisite for cellular network plan and infrastructure deployment. However, the generally assumed Okumura-Hata, and COST 231 large-scale channel model are based on transmission data collected in some specifical areas, which has application limitations for the other different cities. With the rapid development of 4G and study on 5G cellular systems, there are multiple radio access networks covering the same areas, and thus the transmission characteristics are more complicated and still unknown to all. Different from prior work focusing on channel characteristics for only one specifical frequency band in one specifical scenario, in this work, we investigate and compare Radio Frequency (RF) transmission characteristics for eleven different frequency bands varying from 700 MHz to 3500 MHz under various terrains and landscapes, including high-density area, urban city area and rural area. Moreover, three different fitting and modeling methods on testing data are compared, and under high-density scenario, the effects of different antenna height on the transmission characteristics is studied. It is found that the standard deviation of path loss indicates in the range of 9dB which is available for network requirement. With the increase of frequency, the standard deviation decreases. Moreover, the transmit antenna height is defined as above 30m to obtain best coverage. Our work will facilitate networks planners and researchers on future 5G networks.

With the increasing development of cloud computing, how to securely outsource prohibitively expensive computation to unfaithful clouds has caught more and more attention. Large-scale systems of linear matrix equations (LME) are com-monly deployed in scientific and engineering fields, which is a computationally complex task. Thus, it is necessary to design a protocol for practically outsourcing large-scale systems of LME to a malicious cloud. For this purpose, we propose a protocol called OutLME in this paper. In OutLME, we adopt a special permutation technique for the client to transform the original lin-ear matrix equation into a randomized one and decrypt the result returned from cloud into the right one of original problem. As to robust cheating resistance, we propose an effective verification algorithm by utilizing the algebraic property of matrix-vector operations. In addition, both the chosen permutation technique and result verification mechanism incur close-to-zero additional cost on both the cloud and the client, so our proposed protocol is efficient. In the end, the theoretical analysis and the experimental evaluation are provided to demonstrate the validity of OutLME.

High performance of visible light communication (VLC) systems requires overcoming the limitations imposed by the optical wireless channel distortions resulting from path loss and temporal dispersion. In order to design techniques to combat the effects of channel distortions, an accurate VLC channel model is needed. In this paper, we propose a new regular-shaped geometry-based multiple bounce model (RS-GBMB) for VLC channels. The proposed model employs a combined two-ring model and ellipse model, where the received signal is constructed as a sum of the line-of-sight (LoS), single-, double-, and triple bounced rays of different powers. This makes the model sufficiently generic and adaptable to a variety of indoor scenarios. Based on the proposed RS-GBMB model, statistical properties are then investigated, such as the channel DC gain, mean excess delay, root mean square (RMS) delay spread, and Rician factor.

Due to the scarcity of spectrum resources, spectrum sensing techniques are vital for future wireless communication systems. Compared with energy detection methods (ED), feature detection (FD) methods exploit statistical periodicity of transmission signals and have better performance under low signal-to-noise ratio (SNR) conditions. However, for fast time-varying fading channels, the detection accuracy of FD methods will be severely affected when the channel coherence time is shorter than the sensing observation window. In this paper, we analyze the negative impact of fast time-varying channels on the sensing performance of traditional FD sensing methods, and discover the effect of channel-state diversity. Based on this finding, we propose a novel FD sensing algorithm, named “Spectrum Sensing with Channel-state Diversity (SSCD)”. The SSCD algorithm can effectively capture and accumulate the targeted statistical features of primary users from different phases, whereby it is suitable for sensing issues under fast time-varying channels. Simulation results show that for a given probability of false-alarm, the proposed SSCD algorithm significantly outperforms traditional FD with fixed window (FDFW): a detection probability enhancement of 60% is observed under the scenario of sensing window L = 10, coherence time Tc= 2× OFDM symbol length, and SNR= -10 dB. Furthermore, this performance gain is expected to further increase with a faster channel varying rate.

Facebook has recently announced its OpenCellular platform for promoting open-source wireless access technology developments and broader applications. In this demonstration, we would like to show a software defined mobile network that realizes both eNB and EPC key functions according to the 3GPP LTE standard on a general purpose processor (GPP) based computational platform. It is very adaptive and flexible for supporting a variety of internet of things (IoT) applications in vertical industries.

The performance of visible light communication (VLC) system may potentially be degraded by wireless optical channel distortions resulting from path loss and temporal dispersions. In order to devise techniques to combat the effects of channel distortions, an accurate channel model is needed. In this paper, we propose a new field of view (FOV) geometry-based single bounce (GBSB) model for VLC channels. Statistical properties of the proposed GBSB model are then studied, such as the channel gain, mean excess delay, root mean square (RMS) delay spread, Rician factor, and time correlation. Also, the required illuminance for the proposed scenario and the number of light emitting diodes (LEDs) per LED lamp are estimated.

In this paper, we compare the statistical properties of high-speed train (HST) wireless channels in different scenarios using a generic non-stationary HST channel model that has been verified by channel measurements (Ghazal et al., 2015). We mainly focus our comparison and analysis on the three most common HST scenarios, i.e., the rural area, cutting, and viaduct scenarios. Several channel statistical properties such as the temporal autocorrelation function (ACF), space cross-correlation function (CCF), and space- Doppler (SD) power spectrum density (PSD) are investigated. The impacts of different scenario- specific parameters on the channel statistical properties are also studied via numerical analysis.

With small cell networks becoming core parts of the fifth generation (5G) cellular networks, it is an important problem to evaluate the impact of user mobility on 5G small cell networks. However, the tendency and clustering habits in human activities have not been considered in traditional user mobility models. In this paper, human tendency and clustering behaviors are first considered to evaluate the user mobility performance for 5G small cell networks based on individual mobility model (IMM). As key contributions, user pause probability, user arrival, and departure probabilities are derived in this paper for evaluating the user mobility performance in a hotspot-type 5G small cell network. Furthermore, coverage probabilities of small cell and macro cell BSs are derived for all users in 5G small cell networks, respectively. Compared with the traditional random waypoint (RWP) model, IMM provides a different viewpoint to investigate the impact of human tendency and clustering behaviors on the performance of 5G small cell networks.

The statistical characteristics of the signal-to-interference plus noise ratio (SINR) are closely related to many performance metrics of cellular networks. In this paper, the downlink average rate and SINR distribution are studied for orthogonal frequency division multiple access (OFDMA)-based cellular networks subject to the distance-dependent path loss and shadow fading (SF). With the analytical approximate mean and moment generating function (MGF) of the SINR in the logarithmic domain, the closed-form approximation for the lower and upper bounds of the average rate is obtained. Then the distribution of the SINR in the logarithmic domain is proposed to be approximated as the normal inverse Gaussian (NIG) distribution whose parameters are computed explicitly through moment matching. Also, the closed-form expression for the cumulative distribution function (CDF) of the SINR based on the NIG approximation is derived. Simulation results not only verify the tightness of the bounds, but also show that the NIG approximation is up to one order of magnitude more accurate than the Pearson type IV approximation and at least one order of magnitude more accurate than the lognormal approximation when the SF correlation coefficient is small or the standard deviations of the SF are large or different.

In orthogonal frequency division multiple access (OFDMA)-based cellular networks subject to the distancedependent path loss and shadow fading (SF), the downlink inter-cell interference (ICI) for a given user equipment (UE) is essentially a sum of several lognormal random variables (RVs). So far, no method of approximating the lognormal sum distribution is explicitly accurate when the component lognormal RVs with different logarithmic means and logarithmic variances are correlated. In this paper, the Normal Inverse Gaussian (NIG) distribution is proposed to approximate the downlink ICI for a given UE with the correlated SF. First, the downlink ICI is modelled as a sum of several correlated lognormal RVs. Then original moments of the lognormal sum in the logarithmic domain are obtained analytically. Finally the estimated parameters of the NIG distribution are computed explicitly by the mean, variance, skewness and kurtosis of the lognormal sum in the logarithmic domain through moment matching. Numerical results verify the accuracy of the NIG approximation when the correlated component lognormal RVs have different logarithmic means and logarithmic variances, and show that the NIG approximation outperforms the MGF-based lognormal approximation in various scenarios.

Traffic flow modeling is important to the performance analysis/evaluation of services provided by Vehicular Adhoc Networks (VANET), and is also a useful guidance to the deployment of VANET. Different from prior work based on empirical data collected decades ago, in this work, we collect a large amount of empirical traffic flow data from five typical overhead road segments during two different time periods recently in Shanghai. Statistical results in a short time scale (i.e., within half an hour) show that the lane-level traffic volumes/vehicles' velocities in the monitored road segments followed truncated Gaussian distribution (with a match rate of roughly 90%) better than Poisson distribution (with a match rate of roughly 80%) which is normally assumed in the literature. Traffic flow characteristic in a long time scale (i.e., in a day or a week) is also presented, which shows that the traffic density in the night and daytime are very different, however, the traffic flow density in the daytime stays high. With the obtained traffic flow characteristics, we discuss the possibility of the coexistence of WAVE and WLAN in 5.9G band according to the Federal Communications Commission's intention, and point out that for areas in the vicinity of a overhead road inside Outer Ring road in Shanghai, it is not practical for WLAN devices to operate on 5.9G WAVE band.

Network Coding based Epidemic Routing (NECR) has been proposed to improve the data delivery efficiency in Delay Tolerant Networks (DTNs). With NCER, data packets are not only replicated but also encoded while being forwarded to their destination. To better understand how NCER facilitates the transmission, in this paper, we present an analytical framework for modeling the data transmission delay performance of NCER in DTNs. Unlike previous work, our framework can accommodate the inaccuracy of a widely adopted assumption that each node carrying one or more encoded packets is able to transmit an innovative encoded packet to the node it encounters. Numerical results are presented to validate the accuracy of our analytical framework.

In order to meet the explosive increasing demand of user application data in modern wireless networks, a variety of multiservice resource allocation algorithms have been proposed in the literature. Most of them can be modeled as optimization problems of minimizing a summation function indicated as Σi=1Nfi(xi) with additive nonlinear coupling inequality constraints. The existing subgradient methods can only achieve a convergence rate of O(1/√k), which is quite slow for handling big user data generated from modern heterogeneous wireless networks. To develop more efficient multiservice resource allocation algorithms, we consider the regularized Lagrangian function with smoothing accelerated techniques. Specifically, in this paper, we extend the previous research that mainly focuses on linear coupling equality constraints to a challenging scenario with nonlinear coupling inequality constraints. To solve the problem, we propose and analyze a Bregman-based inexact excessive gap (BIEG) algorithm, which, by rigorous mathematical proofs, can asymptotically achieve a faster convergence rate of O(1/k). Furthermore, the BIEG method is applied to develop a novel multiservice resource allocation algorithm, namely, BIEG-RA, which combines the accuracy control mechanism with the Bregman projection technique. Numerical results verify its fast convergence rate in heterogeneous wireless networks.

Heterogeneous network (HetNet) [1] is considered as an energy efficient system structure to alleviate the problem of rapidly increasing power consumption in the wireless communication system. Significant research on HetNet energy efficiency has been conducted. However, most of them only consider power consumption of Base Stations (BSs) while ignoring influence on energy efficiency of Mobile Devices (MDs) brought by new BSs deployment. In this work, we propose a novel power saving metric for HetNet. Under the coexisting scenario of a single macrocell and a single microcell, we analyze the changes in power consumption at both the BSs side and the MDs side with the deployment of a micro BS. Optimum microcell radii for maximum power saving at the MDs sides and for highest network energy efficiency are obtained through analytical studies. It is found that total power saving for microcell MDs is close to 18% with a proper deployment of a microcell. Finally, extensive simulations have been provided to establish the accuracy of our theoretical analyses.

The random distribution of sensors and the irregularity of routing paths lead to unordered sensory data which are difficult to deal with in Wireless Sensor Networks (WSNs). However, for simplicity, most existing researches ignore those characteristics in the designs of Compressive Sensing based Data Aggregation Schemes (CSDAS). Since conventional sparsification bases (e.g., DCT, Wavelets) are inefficient to deal with unordered data, performances of CSDAS with conventional bases are inevitably constrained. In this work, a novel CSDAS which adopts Treelet transform as a sparse transformation tool is proposed. Our CSDAS is capable to exploit both spatial relevance and temporal smoothness of sensory data. Moreover, our CSDAS contains a novel correlation based clustering strategy which is realized with the localized correlation structure of sensory data returned by Treelets and facilitates energy saving of CSDAS in WSNs. Comparative results show the reconstruction error rate with adopting Treelet transform in CSDAS is about 18% lower than that of conventional ones when the normalized energy consumption is 0.3. Even larger performance gain will be obtained at higher energy consumption level. Meanwhile, simulations results further show that our novel correlation based clustering strategy is of great potential. Specially, there is a gain of roughly 35% for total energy savings with our proposed clustering strategy.

In frequency reuse one networks, the inter-cell interference (ICI) is severe especially for cell edge user equipments (UEs). Statistical information of ICI is helpful to design interference mitigation schemes. This paper proposes a statistical model for the uplink ICI in orthogonal frequency division multiple access (OFDMA) networks with power control. The distance-dependent attenuation and correlated shadowing are included in the channel model. Through analyzing the random part related to distribution of user's location and correlated shadowing, we derive the general parametric probability density function (PDF) of uplink ICI in logarithmic domain, and then use the derived model to investigate the distribution of ICI with typical power control schemes. Numerical results derived from theoretical calculations and Monte Carlo simulations verify the statistical model and the analysis.

The power consumption of mobile devices (MDs) has been increasing dramatically since more functions and bigger screens are adopted in the latest MDs. To extend the usage time of MDs and improve customer satisfaction levels, this research study, for the first time, studies the influence of different deployment strategies of a few low‐power base stations (BSs) to reduce the power consumption of a large number of MDs. For both microcell and picocell scenarios, the authors’ analyse the power reductions at the MD side with the deployment of a low‐power BS. The optimum low‐power cell radius is derived for achieving the maximum MDs power reduction. The tradeoff relationships between MD power reduction and the key parameters of low‐power BSs are investigated and discussed. It is shown that authors’ proposed method can achieve about 18% power reduction for microcell MDs.

The 15 papers in Part II of this special issue focus on the spectrum and energy efficient design of wireless communication networks.

ABSTRACT Emerging wireless sensor/actuator network (WSAN) technology has the potential to enable semi‐autonomous airflow control to improve the aerodynamic performance of aircraft. In this paper, a WSAN comprising of multiple linear sensor clusters terminated by actuators is proposed for active airflow control with the objective of minimizing convergecast latency. Here, the convergecast delay is defined as the time required from the beginning of a sampling period to all all sensor's data of this sampling period is received by the actuator. The objective is achieved by minimizing the separation distance of concurrent data transmission so that the number of nodes sending data in the same time slot is maximized. The problem turns into a scheduling problem with a proper selection of interference separation. However, most existing work on the scheduling in linear networks use the minimum separation of two hops to avoid collisions. This paper examines the relationship between the hop separation, signal‐to‐noise ratio, and the latency to make a selection of interference separation. A new interference aware hybrid line scheduling (HLS) algorithm is proposed and its energy consumption is analyzed. Compared with other line scheduling policies, the analysis and simulation results show that, at moderately high node densities, the proposed HLS with carefully selected hop separation is able to reduce both the delay by up to 15% and the energy consumption somehow. Copyright © 2012 John Wiley & Sons, Ltd.

As smart phones usually have large high-resolution screens, many fancy functions, multiple communication modes and interfaces, and powerful processing capabilities, their power consumption is unsurprisingly much higher than traditional voice-centric phones. Unfortunately, the growth of battery capacity is far below the needs of smart phones and new applications, hence it is of great importance and strong interest to mobile users and service providers to prolong the usage time of smart phones without affecting quality of service (QoS) and user experience. To address this challenging issue, we develop a cross-disciplinary approach in this paper by jointly considering battery discharge and recovery characteristics with service/user requirements in energy efficient protocol design. Specifically, based on extensive experiments, we study and model the characteristics of battery recovery effect in a smart phone, under different discharge currents, discharge time and recovery time. A practical power consumption model for smart phone is then derived from real measurement data and empirical formulae. Further, this new model is applied to develop and implement a battery-aware protocol for online continuous music streaming service. Experimental results show the proposed protocol can effectively exploit the battery recovery effect in a smart phone and achieve a 10% gain in usage time while satisfying standard service and user requirements.

The fifteen papers in this special issue are devoted to the topic of spectrum and energy efficiency of wireless and mobile communications networks.

Extensive research has been done on studying the capacity of wireless multi-hop networks. These efforts have led to many sophisticated and customized analytical studies on the capacity of particular networks. While most of the analyses are intellectually challenging, they lack universal properties that can be extended to study the capacity of a different network. In this paper, we sift through various capacity-impacting parameters and present a simple relationship that can be used to estimate the capacity of both static and mobile networks. Specifically, we show that the network capacity is determined by the average number of simultaneous transmissions, the link capacity and the average number of transmissions required to deliver a packet to its destination. Our result is valid for both finite networks and asymptotically infinite networks. We then use this result to explain and better understand the insights of some existing results on the capacity of static networks, mobile networks and hybrid networks and the multicast capacity. The capacity analysis using the aforementioned relationship often becomes simpler. The relationship can be used as a powerful tool to estimate the capacity of different networks. Our work makes important contributions towards developing a generic methodology for network capacity analysis that is applicable to a variety of different scenarios.

Soft Frequency Reuse (SFR) has been widely used to deal with the severe ICI (Inter-Cell Interference) especially for cell edge users. This paper presents an analytical method to investigate the statistics of inter-cell interference in uplink when SFR scheme is adopted in OFDM (Orthogonal Frequency Division Multiplexing) systems. PDF (Probability Density Function) of ICI from one adjacent interfering cell is derived and then used to deduce the expectation and variance of ICI from multiple interfering cells. The derived results are validated through comparison with Monte Carlo simulations. In addition, the relationships between system parameters and statistics of ICI in different frequency bands are investigated. These contributions will give inspect into important guidelines to system optimization.

Traffic flow models are essential to performance analysis/evaluation for applications/services provided by Intelligent Transportation Systems (ITS)/Vehicular Ad-hoc networks (VANET). They are also useful guidance for the deployment of ITS/VANET. Many macroscopic traffic flow models have been proposed in the past few decades on the basis of empirical data collected in the US, Canada, Turkey and etc. However these models may not be accurate for traffic flows in cities in China due to the differences in population, transportation infrastructure, and driving culture. In this paper, we collected a large amount of empirical traffic flow data in Shanghai overhead road during three different time periods. Statistical results showed that the lane-level traffic volumes in Shanghai followed Gaussian distribution rather than Poisson distribution which was normally assumed in literature. Regarding lane-level vehicles' velocities, they matched well with Gaussian distribution. The empirical probability mass functions (PMF) for both the traffic volumes and vehicles' velocities were presented. In addition, how these models would impact performance analysis in VANETs was discussed.

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

Tail-biting convolutional codes (TBCC) provide an efficient method to eliminate the rate loss caused by the known-tail encoding. To simplify the decoder design, circular Viterbi algorithm (CVA) has been proposed by recording and repeating the received block of (soft) symbols beyond the block boundary and continuing Viterbi decoding. However, CVA does not converge in the presence of circular trap. A checking rule is proposed for detecting the circular trap in existing CVA. Based on this rule, an efficient CVA-based decoding algorithm is obtained for tail-biting codes, which exhibits near-optimal performance for both short and long tail-biting codes. This new scheme provides faster convergence speed than the conventional CVA without increasing in complexity and storage space.

The stable lights data in the Version 4 Nighttime Lights Time Series Dataset gained by the Defense Meteorological Satellite Program's Operational Line-scan System can't be used to map urban areas directly because the data has no on board calibration and many unstable lights are also included in the data. So the systematic correction was performed before the urban areas extraction according to the characteristic of stable lights data in China from 1992 to 2008 with the 1 km spatial resolution. It was found that, with systematic correction, the stable lights data could be strictly comparable from one year to the next, in which the remarkable differences of digital number values and the unstable lights were removed effectively. Besides, the urban areas extracted from the stable lights data could be used to describe the real process of urban expansion in China from 1992 to 2008.

IEEE 802.11p and 1609 standards are currently under development to support Vehicle-to-Vehicle and Vehicle-to-Infrastructure communications in vehicular networks. For infrastructure-based vehicular relay networks, access probability is an important measure which indicates how well an arbitrary vehicle can access the infrastructure, i.e. a base station (BS). On the other hand, connectivity probability, i.e. the probability that all the vehicles are connected to the infrastructure, indicates the service coverage performance of a vehicular relay network. In this paper, we develop an analytical model with a generic radio channel model to fully characterize the access probability and connectivity probability performance in a vehicular relay network considering both one-hop (direct access) and two-hop (via a relay) communications between a vehicle and the infrastructure. Specifically, we derive close-form equations for calculating these two probabilities. Our analytical results, validated by simulations, reveal the tradeoffs between key system parameters, such as inter-BS distance, vehicle density, transmission ranges of a BS and a vehicle, and their collective impact on access probability and connectivity probability under different communication channel models. These results and new knowledge about vehicular relay networks will enable network designers and operators to effectively improve network planning, deployment and resource management.

Wireless multi-hop architectures are increasingly used in many wireless networks. However, it is very difficult to analyse and guarantee end-to-end delay performance over multi-hop wireless paths. In this paper, we develop a cross-layer analytical model to characterise the multi-hop delay performance and derive new mathematical formulae of (DBVP), delay mean and jitter of end-to-end communications. The analytical model is verified by computer simulations, and the results show that the mathematical formulae and simulations are in good agreement.

Cyclostationarity based sensing methods are appealing for OFDM signals under low SNR areas. However, such methods are sensitive to doubly-selective fading channels. In this paper we develop a new sensing method by exploiting the cyclostationarity incurred by the spread of autocorrelation due to the Doppler effect in doubly-selective fading channels. First, we model the doubly-selective fading channel with basis expansion model (BEM). Then we analyze the relationship between cyclostationary statistics of transmitted and received OFDM signals, hence derive the cyclostationary statistics of the received signal. Based on the new cyclostationary signatures of the received OFDM signal spread by the Doppler effect, more suitable cyclic frequencies can be chosen to detect cyclostationary features when signal experiences doubly-selective fading. Simulation results demonstrate that the proposed sensing method provides substantial improvement on detection performance.

In cognitive radio networks, the statistical duty cycles (traffic loads) of primary channels have a decisive effect on the throughput and link maintenance of secondary users (SUs). It is very difficult and time-consuming for the SUs to distinguish between primary channels in terms of duty cycles. This paper proposes a multi-user cooperation scheme where SUs share status information about the primary channels and the corresponding cooperation information processing scheme based on Bayesian inference to differentiate the primary channels in terms of statistical duty cycles within a short period of time. The cooperation information processing scheme helps the SUs quickly make accurate estimates of the statistical duty cycles of primary channels and theire uncertainties. Specifically, with the aid and information from its cooperating neighbors, a SU will continuously update the likelihood function and the priori distribution for each primary channel. Analytical and simulation results show that the proposed multi-user cooperation scheme, which is based on a Bayesian updating rule, can significantly save a SU's time in channel detection and selection, and therefore greatly improve its data transmission time and throughput.

Coverage is an important problem in wireless networks. Together with the access probability, which measures how well an arbitrary user can access a wireless network, in particular VANET, they are often used as major indicators of the quality of the network. In this paper, we investigate the coverage and access probability of the vehicular networks with roadside infrastructure, i.e. base stations. Specifically, we analyze the relation between these key parameters, i.e. the coverage range of base stations, coverage range of vehicles, vehicle density and distance between adjacent base stations, and how these parameters interact with each other to collectively determine the coverage and the access probability. We use the connectivity probability, the probability that all nodes in the network are connected to at least one base station within a designated number of hops, as a measure of the coverage. We derived close-form formulas for the connectivity probability and the access probability for a 1D vehicular network bounded by two adjacent base stations. The analytical results have been validated by simulations. The results in the paper can be used by network operators to design networks with specific service coverage guarantees.

Emerging wireless sensor and actuator network (WSAN) technology has the potential to enable semi-autonomous air-flow control to improve the aerodynamic performance of aircraft. In this paper, a WSAN topology comprising of multiple linear sensor clusters terminated by actuators is proposed for active flow control. Two interference aware convergecast scheduling strategies are presented and analyzed, with the objective of jointly minimizing latency and energy consumption. The proposed schemes are required to coordinate local convergecast communications in each cluster, on the one hand, and to arbitrate between mutually interfering nodes of different clusters contending for the wireless channel, on the other. Initial results show that a 19s and 11s convergecast delay moderate to high energy consumption can be achieved.

High energy consumption at mobile devices is a critical issue for end users to access and enjoy high data-rate multimedia applications and services in Wireless Local Area Networks (WLANs). This paper develops an analytical framework for analyzing and comparing the energy efficiency performance of IEEE 802.11n Medium Access Control (MAC) protocol and a Busy Tone Based Cooperative MAC Protocol(namely BTAC) in multi-rate WLANs. Our proposed analytical model considers the impact of dynamic radio channel conditions and multi-rate transmission scenarios. Analytical and simulation results show that BTAC can achieve up to 50% energy saving, comparing to the IEEE 802.11n MAC protocol, under different radio channel conditions, network sizes and traffic loads.

Many applications of wireless sensor networks rely on batteries. But most batteries are not simple energy reservoirs, and can exhibit battery recovery effect. That is, the deliverable energy in a battery can be self-replenished, if left idling for sufficient time. As a viable approach for energy optimisation, we made several contributions towards harnessing battery recovery effect in sensor networks. 1) We empirically examine the gain of battery runtime of sensor devices due to battery recovery effect, and affirm its significant benefit in sensor networks. We also observe a saturation threshold, beyond which more idle time will contribute only little to battery recovery. 2) Based on our experiments, we propose a Markov chain model to capture battery recovery considering saturation threshold and random sensing activities, by which we can study the effectiveness of duty cycling and buffering. 3) We devise a simple distributed duty cycle scheme to take advantage of battery recovery using pseudo-random sequences, and analyse its trade-off between the induced latency of data delivery and duty cycle rates.

Abstract In this paper, we analyze the impacts of different frame types on the self‐similarity and burstiness characteristics of the aggregated frame traffic in a real 802.11 wireless local area network (WLAN). We find that the impacts of different frame types are related to the mean frame sizes and the proportions of specified frame types in the aggregated frame traffic. Furthermore, we propose an analytical model to capture the relationship of self‐similarity characteristics between the aggregated frame traffic and different frame types. These new results provide an insight of frame traffic characteristics and some practical guidelines for developing new efficient algorithms to improve the common medium utilization and system throughput performance. Copyright © 2009 John Wiley & Sons, Ltd.

The man-induced cyclostationary signatures can provide a robust mechanism for the self-coordination of cognitive radio networks. However, such artificial signatures incur signaling overhead and come at the bandwidth cost. In this paper, we show intrinsic cyclostationary signatures in the Orthogonal Frequency Division Multiplexing (OFDM) system with Cyclic Delay Diversity (CDD). The standard conformable CDD technique is initially motivated by the objective for exploiting spatial diversity. Significantly, the underlying periodicity of CDD can simultaneously induce advantageous cyclostationary signatures without any signaling overhead. The lag-indices of the CDD- induced signatures are uniquely determined by the assigned amount of cyclic delay. Consequently each CDD-OFDM system can be identified by a pre-assigned cyclic delay. The signed system can be easily and robustly recognized through cyclostationary detection. Furthermore, the CDD-OFDM systems still preserve the cyclic-prefix induced cyclostationarity as primitive OFDM. By exploiting the overall cyclostationarity, we present a desirable cyclostationarity detector with asymptotical constant false alarm rate for spectrum sensing. Comprehensive simulations are also given to show the performance improvement.

The carrier frequency offset (CFO) could destroy the orthogonality of OFDM subcarriers and induce the inter-carrier interference (ICI) spread. However, the generalized multi-carrier (GMC) system reflects different interference characters in the presence of CFO. In this paper, we exactly analyze the CFO effect on GMC, which multiplexes several single band single carrier based frequency division multiple access (SC-FDMA) streams in frequency domain, under the scenario of additive white Gaussian noise (AWGN) channels. A closed-form expression of signal to interference plus noise ratio (SINR) is derived for the GMC systems in the presence of CFO. Furthermore, the bit error rate (BER) performance analysis is also performed in comparison with the traditional OFDM system for both uncoded and coded environments. Analysis and simulation results show that the feature procedure of discrete Fourier transform (DFT) spread may condense the CFO-induced interference in the first symbol of each subband, resulting in less interference in other symbols. Such condensing procedure can provide robustness against the CFO effect, which also lies on the subband bandwidth for GMC systems. In contrast to the conventional OFDM system, GMC exhibits more robustness when each subband width is designed for 4 times larger than the subcarrier spacing.

In this paper, we propose a new medium access control (MAC) protocol, called cooperative access with relay's data (CARD) for multi-rate wireless local area networks (WLANs). CARD allows remote nodes to transmit their information at a higher data rate to access point (AP) by using intermediate nodes as relays. Particularly, under CARD, a relay node sends its own data packet after forwarding a packet from the original source node, thus to improve system throughput. Analytical and simulation results show that the CARD protocol can effectively improve system throughput and reduce service delay.

Throughput and energy efficiency are two important parameters to evaluate the performance of a Wireless Sensor Network (WSN). For WSNs involved in varying channel conditions, packet transmission reliability can be affected. This results in increased number of retransmissions and therefore energy consumption, with low throughput. Making optimal choices for robust packet transmission in this scenario is vital. For the purpose of this study, we propose a genetic adaptive fuzzy scheme that uses current network conditions in hop node selection. Signal to noise ratio (SNR) and outage probability (Pout) are chosen as input parameters for the proposed scheme, to decide in a distributed manner, the best hop for reliable packet forwarding. Simulation results show the proposed scheme does indeed provide advantages in improving on transmission reliability by 20% and energy efficiency performance by 15%, under different channel conditions.

The concept of cooperative communication has been proposed to improve link capacity, transmission reliability and network coverage in multiuser wireless communication networks. Different from conventional point-to-point and point-to-multipoint communications, cooperative communication allows multiple users or nodes in a wireless network to coordinate their packet transmissions and share each other's resources, thus achieving cooperative diversity. In this paper, we analyze the throughput performance of a cooperative MAC protocol called Cooperative Access with Relay's Data (CARD) under unsaturated traffic conditions in wireless local area networks (WLANs). A Markov chain model is developed to derive the analytical results which are verified by extensive computer simulations.

The aim of this paper is to provide an energy efficiency analysis of the Cooperative Access with Relay's Data for Multi-rate wireless local area networks (WLANs), named CARD protocol at the Medium Access Control (MAC) layer taking into account the impact of both transmission channel states and multi-rate transmission. Simulation results closely match the analytical results confirming the effectiveness of the proposed model.

In cognitive radio (CR) networks, the ability to capture a frequency slot for transmission in a vacant channel has a significant impact on the spectrum efficiency and quality of service (QoS) of a CR user. Important factors include spectrum handoff, transmission rate and delay. This paper proposes a framework for sensing and selecting channels that have the highest probability of appearing idle while reducing the corresponding sensing time. In contrast to previous work, we consider the practical issues encountered by a CR user in a wireless environment, such as discontinuous target frequency bands and limited spectrum sensing ability. We examine the spectrum sensing scheme in terms of packet loss ratio (PLR). The simulation results show that the proposed spectrum sensing strategy can decrease the probability of packet losses in the discontinuous spectrum environment and improve spectrum efficiency.

This paper presents a new joint frame synchronization and frequency offset estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems as a modification to Zhang's method (Zhang et al., 2005). By designing a new training preamble weighted by PN sequence, the timing estimator is improved (at least 3dB with low SNR). By estimating time offset first, the computational load is greatly reduced with no loss in frequency offset estimation accuracy. The performance of the proposed method is evaluated by computer simulations in terms of timing error rate (TER) together with computational complexity.

Dynamic channel allocation (DCA) and transmit power control (TPC) are known as efficient tools to accommodate the explosive demand for broadband wireless access services, by utilizing the limited radio resources while satisfying predefined quality of service (QoS). However, the high complexity of the interactions between discrete DCA and continuous TPC renders a close-form solution to the joint optimization problem intractable. In this paper, a detailed convergence analysis is addressed and reveals that the optimal channel assignment can strengthen the stability condition of TPC. A distributed algorithm is proposed to interactively perform DCA and TPC in a real time manner, with the ability to adjust power and channel according to network dynamics. A real WLAN is employed to evaluate the performance of the integration. It shows that the joint design approach can offer a substantial improvement in terms of user throughput, power saving and interference mitigation compared with conventional fixed-power random channel allocation algorithm.

In this paper, we analyze the throughput performance of a new access protocol, called Cooperative Access with Relay's Data (CARD) for multi-rate wireless local area networks (WLANs). CARD allows remote source nodes to transmit their information at a higher data rate to Access Point (AP) by using intermediate nodes as relays, particularly, a relay node sends its own data packet after forwarding a packet from the original source node, thus to improve system throughput. We also develop a Markov chain model to capture channel conditions. Our analysis considers both ideal and error-prone channel conditions. Analytical results are verified by computer simulations and show significance improvement in the system throughput.

Many applications of sensor networks require batteries as the energy source, and hence critically rely on energy optimisation of sensor batteries. But as often neglected by the networking community, most batteries are non-ideal energy reservoirs and can exhibit battery recovery effect — the deliverable energy in batteries can be replenished per se, if left idling for sufficient duration. We made several contributions towards harnessing battery recovery effect in sensor networks. First, we empirically examine the gain of battery runtime due to battery recovery effect, and found this effect significant and duration-dependent. Second, based on our findings, we model the battery recovery effect in the presence of random sensing activities by a Markov chain model, and study the effect of duty cycling and buffering to harness battery recovery effect. Third, we propose a more energy-efficient duty cycling scheme that is aware of battery recovery effect, and analyse its performance with respect to the latency of data delivery.

The primary difficulty with frequency reuse in scalable WLANs is interference mitigation. When deploying 802.11 devices in a close proximity, appropriate channel selection for each AP becomes one of the most challenging issues provided by limited usable frequencies. In this paper we introduce a distributed version of simulated annealing to solve the dynamic channel allocation problem in high-density WLANs. The approach is generic and applicable to any access system where channels are not allocated in a prefixed or centralized manner. The simulation results show that the proposed algorithm scales well and approximates the optimal solutions under a wide range of different network topologies.

In this paper, we propose a new medium access control (MAC) protocol, called relay with integrated data (RID) for multi-rate wireless networks. RID allows remote nodes to transmit their information at a higher data rate to destination node by using intermediate nodes as relays. In RID protocol, a relay node encapsulates its own data packet and the data packet from a source node and sends both of them into one data packet to destination node. This scheme removes contention period for the relay node. Analytical and simulation results show that RID can significantly increase system throughput and reduce access delay.

In wireless mesh access networks, ad hoc and infrastructure modes are both used to support multi-hop data transmission from mesh clients to a mesh router. Traffic will accumulate along the paths towards the mesh router. So the mesh clients close to the router will have more data to transmit and these packets are more likely to collide with each other. In [3], a random access MAC protocol, RSMA (receiver sense multiple access), is proposed dedicated for the final hop client to router communication to deal with the packet collision problem. In this paper, a rigorous mathematical model is developed for cross-layer performance analysis of RSMA. MAC layer random access model, physical layer radio channel model and the power capture model are combined for comprehensive analysis.

In this paper, we analyze a real 802.11 wireless network frame traffic trace, collected from the ACM SIGCOMM 2004 Conference, by calculating its autocorrelation function and Hurst exponents at different time scales. We find this frame traffic trace has second-order self-similar characteristic. This in-depth knowledge of 802.11 wireless network frame traffic can effectively improve the efficiency and performance of network planning, resource management and MAC-layer algorithms.

In wireless mesh access networks, ad hoc and infrastructure modes are both used to support multi-hop data transmission from mesh clients to a mesh router. Traffic will accumulate along the paths towards the mesh router. So the mesh clients close to the router will have more data to transmit and these packets are more likely to collide with each other. In this paper, a contention-based medium access control (MAC) protocol, called receiver sense multiple access (RSMA), is proposed for the last-hop client-to-router communications. RSMA can minimize control packet collisions and achieve collision-free data packet transmissions. A rigorous mathematical model is developed for performance analysis in a scenario with hidden terminals. Our analytical results of throughput and delay performance are verified by computer simulations.

A complete complementary codes based direct sequence code division multiple access (CC/DS-CDMA) system was recently proposed to offer isotropic interference free transmissions. Instead of being restricted by interference, the capacity of CC/DS-CDMA, however, is constrained by the number of code flocks due to the small family size of current CC codes. In this article, an channel-dependent code family extension (COFE) scheme is proposed to increase the system capacity. The performance of the COFE scheme is investigated through extensive computer simulations. It is concluded that the COFE scheme not only tends to hold the interference-free property of CC/DS-CDMA, but also offers significant spectrum efficiency and system capability in terms of supporting applications with diverse QoS requirements.

In wireless mesh access networks, ad hoc and infrastructure modes are both used to support multi-hop data transmission for mesh clients. The traffic will accumulate when it get close to the mesh router and makes collision more likely to happen. This phenomenon severely degrades system throughput and increases the access delay. At the same time, huge amount of energy is wasted on both sides of sender and receiver in transmitting and receiving the collision (overlapping) packets. In this paper, a contention-based medium access control protocol, double sense multiple access - single channel (DSMA-S) is designed to alleviate the packet collision problem, so as to guarantee the throughput performance and reduce the corresponding energy wastage. A rigorous mathematical model is developed for performance analysis with simulation results perfectly match.

In this paper the impact of capture effect on the IEEE 802.11 networks has been investigated. In order to have insight into capture effect in the MAC mechanism, a new Markov chain model with capture effect has been built for describing the backoff scheme in the IEEE 802.11 networks. Based on the new iterative algorithm used for calculating the transmission probability, a new throughput model considering the impact of capture effect on the back-off scheme has been proposed. The numerical simulation results show that the capture effect has more impact on the basic access mechanism than that on the RTS/CTS access mechanism.

In this paper, we propose a link adaptation scheme to adapt the packet size as a crucial system parameter for quality of service (QoS) provisioning in multi-rate wireless networks. The link adaptation scheme combines adaptive modulation and coding (AMC) at the physical layer with type-II hybrid automatic repeat request (HARQ) at the data link layer in a cross-layer fashion. We then study the goodput performance of the proposed link adaptation scheme at the medium access control (MAC) layer. We show that there exists an optimal packet size to achieve the maximum goodput in transmitting messages over transport layer sessions. Based on this observation, we provide an efficient and effective algorithm to search the optimal packet size. Finally, we propose a link adaptation architecture, through which the proposed link adaptation scheme can adapt packet sizes to instantaneous channel conditions to fulfill committed QoS in higher layers. Numerical results show that the system adopting the proposed optimal packet sizes has superior goodput performance over the systems using fixed packet sizes.

Multi-hop wireless mesh networks (WMNs) have been actively developed as a flexible and reliable platform to support a wide variety of real-time and best-effort multimedia services and applications. For delay sensitive applications over multi-hop wireless connections, end-to-end delay performance is a crucial performance metric and hence the focus of this paper. We extend the link layer effective capacity (EC) model to derive a lower bound of delay-bound violation probability, or complementary cumulative distribution function over multi-hop wireless communication paths. A fluid traffic model with cross traffic and a Rayleigh fading channel with Doppler spectrum are considered in our study. End-to-end average delay and delay jitter bounds are also obtained. Analytical results are verified by extensive computer simulations under different traffic conditions.

This paper begins with a review of CDMA based 2-3G systems from a network perspective. In particular, the challenges faced by current CDMA technologies are addressed. We will show that many advanced operational requirements in future 4G networks, such as high-speed burst traffic, cross layer network design, seamless integration of different networks with and without infrastructure, will make the current CDMA technologies unsuitable due to their rigid and voice-centric physical (PHY) layer architecture. To respond to the call for CDMA innovation, a new physical layer architecture, namely Orthogonal Complementary Coded CDMA with Offset Stacking (OCC-CDMA/OS) spreading scheme, is proposed in this paper for its possible applications in futuristic wireless networks. One of the most important features of OCC-CDMA/ OS PHY architecture is its unique elasticity, which facilitates implementation of a fully-adaptive CDMA transceiver to work harmonically with various advanced upper-layer designs in 4G networks. Many other technical issues on CDMA based 4G networks will also be discussed. It is concluded that OCC-CDMA/OS has a great potential for its applications in future 4G wireless.

For time-sensitive applications requiring frequent data collections from a remote wireless sensor network, it is a challenging task to design an efficient routing scheme that can minimize delay and also offer good performance in energy efficiency, network lifetime and throughput. In this paper, we propose a new routing scheme, called Shortest Hop Routing Tree (SHORT), to achieve those design objectives through effectively generating simultaneous communication pairs and identifying the shortest hop (closest neighbor) for packet relay. Compared with the existing popular schemes such as PEGASIS, BINARY and PEDAP-PA, SHORT offers the best "energy x delay" performance and has the capability to achieve a very good balance among different performance metrics.

In wireless ad hoc networks, the major quality of service (QoS) concern and challenge in the design and analysis of contention-based medium access control (MAC) protocols is to achieve good throughput and access delay performance in the presence of hidden terminals, which are defined as the terminals out of the radio coverage area of an intended transmitter but within that of the receiver. We propose and analyze in this paper a new dual-channel random access protocol, called "Double Sense Multiple Access" (DSMA), for improving QoS support in wireless ad hoc networks. By separating the transmissions of ready-to-send (RTS) and data packets into two time-slotted channels and by introducing a novel double sense mechanism, DSMA completely solves the hidden terminal problem and can guarantee the success transmission of data packets. By taking into account the most complex network scenario in which all transmitters are hidden terminals with respect to each other, key QoS metrics such as throughput, blocking probability and access delay are derived mathematically for the proposed DSMA protocol. These analytical results are verified by extensive computer simulations.

For applications requiring frequent data collections from a remote wireless sensor network, it is a challenging problem to design an efficient routing scheme for comprehensively, accurately and timely delivering data packets in each round of data collection over a long period of time. Unlike previous work targeting at maximizing energy efficiency and network lifetime, we propose and analyze in this paper a new routing scheme, called Minimum Energy Spanning Tree for Efficient Routing (MESTER), which is developed under the design objective of maintaining a high quality in data collection for as long as possible. Compared with the existing Minimum Spanning Tree (MST) based schemes like PEDAP and PEDAP-PA, MESTER can achieve comparable but more balanced performance at a much lower complexity. In addition, we define "throughput efficiency" to characterize our quality-oriented design objective. As a new concept with low granularity (packet level), throughput efficiency is found a fair and stable performance metric to different network sizes, node densities and routing schemes. It provides us an additional insight into the network behavior under different resource and capability constraints.

This paper introduces an approach to generate generalized pairwise complementary (GPC) codes, which offer a uniform interference free windows (IFWs) across the entire code set. The GPC codes work in pairs and can fit extremely power efficient quadrature carrier modems. The characteristic features of the GPC codes include: the set size is 2K, the processing gain is 4NK, and the IFW's width is 8N identically for all codes in a set, where K is the times to perform Walsh-Hadamard expansions and N is element code length of seed complementary codes. Therefore, by using different N, the IFW width of a GPC code set can be adjusted with its set size unchanged. Each GPC code set consists of two code groups, with each having K codes, and they have sparsely and uniformly distributed autocorrelation side lobes and cross-correlation levels outside the IFWs.

In this paper, a new non-linear companding technique, called "exponential companding", is proposed to reduce the high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. Unlike the /spl mu/-law companding scheme, which enlarges only small signals so that increases the average power, the schemes based on exponential companding technique adjust both large and small signals and can keep the average power at the same level. By transforming the original OFDM signals into uniformly distributed signals (with a specific degree), the exponential companding schemes can effectively reduce PAPR for different modulation formats and sub-carrier sizes. Moreover, many PAPR reduction schemes, such as /spl mu/-law companding scheme, cause spectrum side-lobes generation, but the exponential companding schemes cause less spectrum side-lobes. Computer simulations, which consider a baseband OFDM system with additive white Gaussian noise (AWGN) channels and a solid state power amplifier (SSPA), show that the proposed exponential companding schemes can offer better PAPR reduction, bit error rate (BER), and phase error performance than the /spl mu/-law companding scheme.

Complete complementary code based direct sequence code division multiple access (CC/DS-CDMA) system has been proposed recently as a potential candidate for beyond the third generation (B3G) mobile communications. This paper addresses the issue on variable capacity property (VCP) of a CC/DS-CDMA system with multiple time slots, pertaining to the multi-rate channel assignment schemes of a CC/DS-CDMA system. In particular, three traffic classes and two dynamic code flock assignment schemes, namely random assignment (RA) and compact assignment (CA), are considered in our study. Simulation results of blocking probability, mean and variance of system capacity, and throughput under different schemes are derived and compared. It is concluded that the VCP of CC/DS-CDMA systems is sensitively affected by different code flock assignment schemes. In general, CA can offer lower blocking probability and more stable system capacity; whereas RA can offer larger mean system capacity and higher throughput when offered traffic is heavy.

The random access channel (RACH) in a universal terrestrial radio access-frequency division duplex (UTRA-FDD) system is a contention-based channel mainly used to carry control information from mobile stations (MS) to base stations (BS). The transmission of a random access request contains two steps: preamble transmission and message transmission. In preamble transmission, the power ramping technique is used to favor the delayed preambles by stepping up the transmission power after each unsuccessful access. In doing so, the success of transmitting a long-delayed preamble is increased due to the power capture effect. This paper analyzes the blocking, throughput, and delay performance of preamble transmission under three power ramping schemes with fixed, linear, and geometric step sizes. The interference caused by different power ramping schemes is also compared.

The random access channel (RACH) in a universal terrestrial radio access frequency division duplex (UTRA-FDD) system is a contention-based channel mainly used to carry control information from mobile stations to base stations. The transmission of a random access request contains two steps, preamble transmission and message transmission. In preamble transmission, a power ramping technique is used to favor the delayed preambles by stepping up the transmission power after each unsuccessful access. In doing so, the success of transmitting a long-delayed preamble is increased due to the power capture effect. We analyze the blocking and throughput performance of preamble transmission under three power ramping schemes with fixed, linear and geometric step sizes. Also, we compare the interference caused by different power ramping schemes.

In universal terrestrial radio access (UTRA) systems, orthogonal variable spreading factor (OVSF) codes are used to support different transmission rates for different users. In this paper, we first define the flexibility index to measure the capability of an assignable code set in supporting multirate traffic classes. Based on this index, two single-code assignment schemes, nonrearrangeable and rearrangeable compact assignments, are proposed. Both schemes can offer maximal flexibility for the resulting code tree after each code assignment. We then present an analytical model and derive the call blocking probability, system throughput and fairness index. Analytical and simulation results show that the proposed schemes are efficient, stable and fair.

We derive the closed-form delay distributions of slotted ALOHA and nonpersistent carrier sense multiple access (CSMA) protocols under steady state. Three retransmission policies are analyzed. We find that under a binary exponential backoff retransmission policy, finite average delay and finite delay variance can be guaranteed for G<2S and G<4S/3, respectively, where G is the channel traffic and S is the channel throughput. As an example, in slotted ALOHA, S<(ln2)/2 and S<3(ln4-ln3)/4 are the operating ranges for finite first and second delay moments. In addition, the blocking probability and delay performance as a function of r/sub max/ (maximum number of retransmissions allowed) is also derived.

In UTRA systems, orthogonal variable-spreading-factor (OVSF) codes are used to support different transmission rates for different users. In this paper, we first define an index for measuring the flexibility of an assignable code set. Based on this flexibility index, a single-code assignment scheme, namely rearrangeable compact assignment (RCA), is proposed for accommodating multi-rate traffic. RCA can offer maximal flexibility to the resulting assignable code set after each code assignment. Analytical and simulation results show that RCA is efficient, stable and fair.

The random access channel (RACH) in UTRA-TDD is defined as an uplink contention-based transport channel that is mainly used to carry control information from mobile stations to base stations. We study the throughput performance of RACH on an additive white Gaussian noise (AWGN) channel whereby successful transmission of a burst requires the spreading code chosen to be collision-free and the burst error-free after convolutional decoding. Based on this model, the code-collision probability, the data bit error probability and the RACH channel capacity are derived. For spreading factor Q equal to 8 or 16 as specified in the standard, the maximum throughput obtained is 1.74 and 3.67, respectively.

In UTRA systems, orthogonal variable-spreading-factor (OVSF) codes are used to support different transmission rates for different users. In this paper, we first define an index for measuring the flexibility of an assignable code set. Based on this flexibility index, a single-code assignment scheme, namely nonrearrangeable compact assignment (NCA), is proposed for accommodating multi-rate traffic. NCA can offer maximal flexibility to the resulting assignable code set after each code assignment. As a result, it gives better blocking, throughput and fairness performance when compared to random assignment (RA) scheme.