We consider spectrum sharing in a system with multiple network operators serving many users in the same coverage area. Each operator selects a frequency band from a pool. Due to interference, the aggregate throughput of an operator would likely deteriorate as the number of operators using the band increases. The objective is to devise a decentralized method for the operators to select the bands, assuming that the networks and user populations of the operators are similar. For this, we consider a decentralized multi-armed bandit algorithm operating in two phases, namely Estimation and Allocation. In the estimation phase, operators learn load-dependent reward distributions; and in the subsequent allocation phase, these estimates are used to determine an allocation of bands across operators. We extend the framework to multi-state systems, where the reward distribution depends on additional observable state variables capturing load variations and environmental factors. Analyzing the system with a variance-based approach using Bernstein’s Inequality, we show that the decentralized algorithm achieves a regret which is sublinear in the time horizon as compared to an optimal centralized decision. In multi-state systems, we demonstrate a trade-off between increasing estimation overhead and decreasing regret during allocation phases.

With the rapid development of 5G and beyond-5G (B5G) networks, adaptive network slicing has become essential to meet the diverse requirements of various communication services, such as ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). Conventional optimization methods often face challenges such as poor adaptability, limited scalability, and low sample efficiency. To address these issues, this paper introduces SliceWM, a world-model-based reinforcement learning (RL) framework for network slicing. SliceWM implements the world model as a Recurrent State Space Model, and effectively learns network dynamics in latent representation spaces, enabling more accurate look-ahead predictions of network states. The policy is trained through interactions with the learned world model, to generate efficient resource allocation across network slices. Extensive experiments demonstrate that our proposed world-model-based approach for network slicing outperforms baseline methods in both latency and throughput. Additionally, our method adapts robustly to dynamic network conditions, highlighting its potential to handle complex network systems. The code is accessible on our GitHub page: https://github.com/ucd-dare/SliceWM.

World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning policy. To speed up the learning process, the pretrain-finetune paradigm is often used, where online RL is initialized by a pretrained model and a policy learned offline. However, naively performing such initialization in RL may result in dramatic performance degradation during the online interactions in the new task. To tackle this challenge, we first analyze the performance degradation and identify two primary root causes therein: the mismatch of the planning policy and the mismatch of the dynamics model, due to distribution shift. We further analyze the effects of these factors on performance degradation during finetuning, and our findings reveal that the choice of finetuning strategies plays a pivotal role in mitigating these effects. We then introduce AdaWM, an Adaptive World Model based planning method, featuring two key steps: (a) mismatch identification, which quantifies the mismatches and informs the finetuning strategy, and (b) alignment-driven finetuning, which selectively updates either the policy or the model as needed using efficient low-rank updates. Extensive experiments on the challenging CARLA driving tasks demonstrate that AdaWM significantly improves the finetuning process, resulting in more robust and efficient performance in autonomous driving systems.

We address radio resource scheduling in a network of multiple in-X subnetworks providing wireless Ultra-Reliable Low-Latency Communication (URLLC) service. Each subnetwork is controlled by an agent responsible for scheduling resources to its devices. Agents rely solely on interference measurements for information about other agents, with no explicit coordination. Subnetwork mobility and fast-fading effects create a non-stationary environment, adding to the complexity of the scheduling problem. This scenario is modeled as a multi-agent Markov Decision Process (MDP). To address the problem, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach under URLLC constraints, which integrates Long Short-Term Memory (LSTM) with the Deep Deterministic Policy Gradient (DDPG) algorithm to manage non-stationarity and high-dimensional action spaces. We apply an asynchronous update strategy, where one agent is updating at a time. This reduces learning variability, resolves policy conflicts, and improves the interpretability of the MADRL approach. Simulation results demonstrate that the asynchronous update mechanism outperforms synchronous updates and baseline methods, achieving superior reliability, resource utilization, and explainability.

To safely navigate intricate real-world scenarios, autonomous vehicles (AVs) must be able to adapt to diverse road conditions and anticipate future events. World model (WM)-based reinforcement learning (RL) has emerged as a promising approach by learning and predicting the complex dynamics of various environments. Nevertheless, to the best of our knowledge, there does not exist an open-source platform for training and testing such algorithms in complicated driving environments. To fill this void, we introduce CarDreamer, the first open-source learning platform designed specifically for developing and evaluating WM-based autonomous driving algorithms. It comprises a few key components, including 1) WM Backbone: CarDreamer has integrated some state-of-the-art WMs, which simplifies the reproduction of RL algorithms; 2) Built-In Tasks: CarDreamer offers a comprehensive set of highly configurable driving tasks which are compatible with gym interfaces and are equipped with empirically optimized reward functions; and 3) Task Development Suite: CarDreamer integrates a flexible task development suite to streamline the creation of driving tasks. This suite enables easy definition of traffic flows and vehicle routes, along with automatic collection of multimodal observation data. Furthermore, we conduct extensive experiments using built-in tasks to evaluate the performance and potential of WMs in autonomous driving. Thanks to the richness and flexibility of CarDreamer, we also systematically study the impact of observation modality, observability, and sharing of vehicle intentions on AV safety and efficiency. All code and documents are accessible on our GitHub pagehttps://github.com/ucd-dare/CarDreamer.

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce edge intelligent drive (EI-Drive), an Edge-AI-based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page:https://ucd-dare.github.io/eidrive.github.io/.

Federated Learning (FL) has garnered widespread interest in recent years. However, owing to strict privacy policies or limited storage capacities of training participants such as IoT devices, its effective deployment is often impeded by the scarcity of training data in practical decentralized learning environments. In this paper, we study enhancing FL with the aid of (large) pre-trained models (PMs), that encapsulate wealthy general/domain-agnostic knowledge, to alleviate the data requirement in conducting FL from scratch. Specifically, we consider a networked FL system formed by a central server and distributed clients. First, we formulate the PM-aided personalized FL as a regularization-based federated meta-learning problem, where clients join forces to learn a meta-model with knowledge transferred from a private PM stored at the server. Then, we develop an inexact-ADMM-based algorithm, AugFL, to optimize the problem with no need to expose the PM or incur additional computational costs to local clients. Further, we establish theoretical guarantees for AugFL in terms of communication complexity, adaptation performance, and the benefit of knowledge transfer in general non-convex cases. Extensive experiments corroborate the efficacy and superiority of AugFL over existing baselines.

Decentralized Multi-agent Learning (DML) enables collaborative model training while preserving data privacy. How-ever, inherent heterogeneity in agents' resources (computation, communication, and task size) may lead to substantial variations in training time. This heterogeneity creates a bottleneck, lengthening the overall training time due to straggler effects and potentially wasting spare resources of faster agents. To minimize training time in heterogeneous environments, we present a Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning (ComDML), which balances the workload among agents through a decentralized approach. Leveraging local-loss split training, ComDML enables parallel updates, where slower agents offload part of their workload to faster agents. To minimize the overall training time, ComDML optimizes the workload balancing by jointly considering the communication and computation capacities of agents, which hinges upon integer programming. A dynamic decentralized pairing scheduler is developed to efficiently pair agents and determine optimal offloading amounts. We prove that in ComDML, both slower and faster agents' models converge, for convex and non-convex functions. Furthermore, extensive experimental results on popular datasets (CIFAR-10, CIFAR-100, and CINIC-10) and their non-I.I.D. variants, with large models such as ResNet-56 and ResNet-110, demonstrate that ComDML can significantly reduce the overall training time while maintaining model accuracy, compared to state-of-the-art methods.ComDML demonstrates robustness in heterogeneous environments, and privacy measures can be seamlessly integrated for enhanced data protection.

Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is *how to extract positive behaviors from noisy data*. In general, current approaches to the problem select data building on state-action similarity to given expert demonstrations, neglecting precious information in (potentially abundant) *diverse* state-actions that deviate from expert ones. In this paper, we introduce a simple yet effective data selection method that identifies positive behaviors based on their *resultant states* - a more informative criterion enabling explicit utilization of dynamics information and effective extraction of both expert and beneficial diverse behaviors. Further, we devise a lightweight behavior cloning algorithm capable of leveraging the expert and selected data correctly. In the experiments, we evaluate our method on a suite of complex and high-dimensional offline IL benchmarks, including continuous-control and vision-based tasks. The results demonstrate that our method achieves state-of-the-art performance, outperforming existing methods on **20/21** benchmarks, typically by **2-5x**, while maintaining a comparable runtime to Behavior Cloning (BC).

In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the naive combination of existing offline IL and online IL methods tends to behave poorly in this context, because the initial discriminator (often used in online IL) operates randomly and discordantly against the policy initialization, leading to misguided policy optimization and *unlearning* of pretraining knowledge. To overcome this challenge, we propose a principled offline-to-online IL method, named OLLIE, that simultaneously learns a near-expert policy initialization along with an *aligned discriminator initialization*, which can be seamlessly integrated into online IL, achieving smooth and fast finetuning. Empirically, OLLIE consistently and significantly outperforms the baseline methods in **20** challenging tasks, from continuous control to vision-based domains, in terms of performance, demonstration efficiency, and convergence speed. This work may serve as a foundation for further exploration of pretraining and finetuning in the context of IL.

Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others. In this study, we propose a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, is developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines.

A central question in edge intelligence is “how can an edge device learn its local model with limited data and constrained computing capacity?” In this study, we explore the approach where a global model initialization is first obtained by running federated learning (FL) across multiple edge devices, based on which a semi-supervised algorithm is devised for a single edge device to carry out quick adaptation with its local data. Specifically, to account for device heterogeneity and resource constraints, a global model is first trained via FL, where each device conducts multiple local updates only for its customized subnet. A subset of devices can be selected to upload updates for aggregation during each training round. Further, device scheduling is optimized to minimize the training loss of FL, subject to resource constraints, based on the carefully crafted reward function defined as the one-round progress of FL each device can provide. We examine the convergence behavior of FL for the general non-convex case. For semi-supervised model personalization, we use the FL-based model initialization as a teacher network to impute soft labels on unlabeled data, thereby addressing the insufficiency of labeled data. Experiments are conducted to evaluate the performance of the proposed algorithms.

Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share a model similarity, it is plausible to leverage pretrained generative models from other edge nodes. Appealing to optimal transport theory tailored toward Wasserstein-1 generative adversarial networks (WGANs), this study aims to develop a framework that systematically optimizes continual learning of generative models using local data at the edge node while exploiting adaptive coalescence of pretrained generative models. Specifically, by treating the knowledge transfer from other nodes as Wasserstein balls centered around their pretrained models, continual learning of generative models is cast as a constrained optimization problem, which is further reduced to a Wasserstein-1 barycenter problem. A two-stage approach is devised accordingly: 1) the barycenters among the pretrained models are computed offline, where displacement interpolation is used as the theoretic foundation for finding adaptive barycenters via a "recursive" WGAN configuration and 2) the barycenter computed offline is used as metamodel initialization for continual learning, and then, fast adaptation is carried out to find the generative model using the local samples at the target edge node. Finally, a weight ternarization method, based on joint optimization of weights and threshold for quantization, is developed to compress the generative model further. Extensive experimental studies corroborate the effectiveness of the proposed framework.

Warm-Start reinforcement learning (RL), aided by a prior policy obtained from offline training, is emerging as a promising RL approach for practical applications. Recent empirical studies have demonstrated that the performance of Warm-Start RL can be improved *quickly* in some cases but become *stagnant* in other cases, especially when the function approximation is used. To this end, the primary objective of this work is to build a fundamental understanding on ''whether and when online learning can be significantly accelerated by a warm-start policy from offline RL?''. Specifically, we consider the widely used Actor-Critic (A-C) method with a prior policy. We first quantify the approximation errors in the Actor update and the Critic update, respectively. Next, we cast the Warm-Start A-C algorithm as Newton's method with perturbation, and study the impact of the approximation errors on the finite-time learning performance with inaccurate Actor/Critic updates. Under some general technical conditions, we derive the upper bounds, which shed light on achieving the desired finite-learning performance in the Warm-Start A-C algorithm. In particular, our findings reveal that it is essential to reduce the algorithm bias in online learning. We also obtain lower bounds on the sub-optimality gap of the Warm-Start A-C algorithm to quantify the impact of the bias and error propagation.

Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inverse semantic communications as a new paradigm to achieve lightweight wireless sensing using the reconfigurable intelligent surface (RIS). Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message. Specifically, we first develop a novel RIS hardware for encoding several signal spectrums into one MetaSpectrum. We then propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from the real world, we show that our framework can reduce the data volume by 90% compared to that before encoding, without affecting the execution of various sensing tasks. Experiment results also demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.

This article studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find that it is challenging to directly use this data set for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: 1) data preprocessing; 2) fine-grained event data extraction; and 3) feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world data set from the Western Interconnection of the U.S. power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

Real-time traffic has stringent requirements in terms of latency, and deadline guarantees on packet delivery play a vital role in real-time IoT applications. Deadline-aware wireless scheduling of real-time traffic has been a long-standing open problem, despite significant efforts using analytical methods. Departing from the conventional approaches, this work studies deadline-aware traffic scheduling by taking an offline reinforcement learning (RL) approach to train scheduling algorithms, ready to be used for online scheduling. To address the challenges therein, we propose a network-aided offline RL (NA-ORL) framework for deadline-aware scheduling, by making use of the fact that the network dynamics follows a well-defined physics model. Specifically, in NA-ORL the initialization of the scheduling policy is obtained through behavior cloning with a good model-based scheduling algorithm, and the network-aided actor–critic (A–C) method is utilized to train a better scheduling policy with carefully designed states and reward function, thanks to its nature of policy improvement. Building on NA-ORL, we further devise a network-aided offline meta-RL (NA-MRL) algorithm to deal with the nonstationary network dynamics. Extensive experimental results demonstrate that the proposed NA-ORL and NA-MRL algorithms can achieve better performance over adaptive mixing over nondominated links (AMIX-ND) and largest-deficit-first (LDF), in various scenarios for the deadline-aware wireless scheduling.

Distributed machine learning is envisioned as the bedrock of future intelligent networks, where agents exchange information with each other to train models collaboratively without uploading data to a central processor. Despite its broad applicability, a downside of distributed learning is the need for iterative information exchange between agents, which may lead to high communication overhead unaffordable in many practical systems with limited communication resources. To resolve this communication bottleneck, we need to devise communication-efficient distributed learning algorithms and protocols that can reduce the communication cost and simultaneously achieve satisfactory learning/optimization performance. Accomplishing this goal necessitates synergistic techniques from a diverse set of fields, including optimization, machine learning, wireless communications, game theory, and network/graph theory. This Special Issue is dedicated to communication-efficient distributed learning from multiple perspectives, including fundamental theories, algorithm design and analysis, and practical considerations.

Distributed learning is envisioned as the bedrock of next-generation intelligent networks, where intelligent agents, such as mobile devices, robots, and sensors, exchange information with each other or a parameter server to train machine learning models collaboratively without uploading raw data to a central entity for centralized processing. By utilizing the computation/communication capability of individual agents, the distributed learning paradigm can mitigate the burden at central processors and help preserve data privacy of users. Despite its promising applications, a downside of distributed learning is its need for iterative information exchange over wireless channels, which may lead to high communication overhead unaffordable in many practical systems with limited radio resources such as energy and bandwidth. To overcome this communication bottleneck, there is an urgent need for the development of communication-efficient distributed learning algorithms capable of reducing the communication cost and achieving satisfactory learning/optimization performance simultaneously. In this paper, we present a comprehensive survey of prevailing methodologies for communication-efficient distributed learning, including reduction of the number of communications, compression and quantization of the exchanged information, radio resource management for efficient learning, and game-theoretic mechanisms incentivizing user participation. We also point out potential directions for future research to further enhance the communication efficiency of distributed learning in various scenarios.

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users’ QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme. The code for this paper is available athttps://github.com/HongyangDu/AttentionQoE.

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data. The code for this paper is available athttps://github.com/HongyangDu/SemSensing.

Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that has attracted significant research attention is the design of incentive mechanism to stimulate user collaboration in FL. The majority of works adopt a broker-centric approach to help the central operator to attract participants and further obtain a well-trained model. Few works consider forging participant-centric collaboration among participants to pursue an FL model for their common interests, which induces dramatic differences in incentive mechanism design from the broker-centric FL. To coordinate the selfish and heterogeneous participants, we propose a novel analytic framework for incentivizing effective and efficient collaborations for participant-centric FL. Specifically, we respectively propose two novel game models for contribution-oblivious FL (COFL) and contribution-aware FL (CAFL), where the latter one implements a minimum contribution threshold mechanism. We further analyze the uniqueness and existence for Nash equilibrium of both COFL and CAFL games and design efficient algorithms to achieve equilibrium solutions. Extensive performance evaluations show that there exists free-riding phenomenon in COFL, which can be greatly alleviated through the adoption of CAFL model with the optimized minimum threshold.

Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for many existing FL systems, clients need to frequently exchange model parameters of large data size with the remote cloud server directly via wide-area networks (WAN), leading to significant communication overhead and long transmission time. To mitigate the communication bottleneck, we resort to the hierarchical federated learning paradigm of HiFL, which reaps the benefits of mobile edge computing and combines synchronous client-edge model aggregation and asynchronous edge-cloud model aggregation together to greatly reduce the traffic volumes of WAN transmissions. Specifically, we first analyze the convergence bound of HiFL theoretically and identify the key controllable factors for model performance improvement. We then advocate an enhanced design of HiFlash by innovatively integrating deep reinforcement learning based adaptive staleness control and heterogeneity-aware client-edge association strategy to boost the system efficiency and mitigate the staleness effect without compromising model accuracy. Extensive experiments corroborate the superior performance of HiFlash in model accuracy, communication reduction, and system efficiency.

We consider a distributed learning setting where strategic users are incentivized by a fusion center, to train a learning model based on local data. The users are not obliged to provide their true gradient updates and the fusion center is not capable of validating the authenticity of reported updates. Thus motivated, we formulate the interactions between the fusion center and the users as repeated games, manifesting an under-explored interplay between machine learning and game theory. We then develop an incentive mechanism for the fusion center based on a joint gradient estimation and user action classification scheme, and study its impact on the convergence performance of distributed learning. Further, we devise adaptive zero-determinant (ZD) strategies, thereby generalizing the classical ZD strategies to the repeated games with time-varying stochastic errors. Theoretical and empirical analysis show that the fusion center can incentivize the strategic users to cooperate and report informative gradient updates, thus ensuring the convergence.

Though electric vehicles (EVs) are efficient in power consumption, EV charging is time consuming and hence EV users may experience long delay for charging during peak hours in urban areas. Reshaping of heterogeneous EV charging demand enhances user experience and charging stations' profit. This study proposes a demand reshaping framework, in which each charging station announces different hourly charging prices ahead of time and EV users can freely select their charging destinations. The optimal charging prices should minimize the waiting duration for charging and maximize charging stations' profit. To this end, charging stations train a deep neural network model to predict hourly charging demand at distinct charging stations. Subsequently, the optimal prices are numerically computed by leveraging the trained neural network. We show that peak demand for EV charging is smoothed out both spatially and tem-porarily for improved quality of service via monetary incentives. Consequently, EV users benefit from decreased charging duration and charging stations obtain profit from increased service quality.

Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency structure between the spatial and temporal domains, as well as the contextual information. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods do not work well when applied to LSTF due to several issues: the low level of generality, insufficient use of contextual information, and the imbalanced graph fusion approach. To address these issues, we construct new graph models to represent the contextual information of each node and exploit the long-term spatio-temporal data dependency structure. To aggregate the information across multiple graphs, we propose a new dynamic multi-graph fusion module to characterize the correlations of nodes within a graph and the nodes across graphs via the spatial attention and graph attention mechanisms. Furthermore, we introduce a trainable weight tensor to indicate the importance of each node in different graphs. Extensive experiments on two large-scale datasets demonstrate that our proposed approaches significantly improve the performance of existing graph neural network models in LSTF prediction tasks.

By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. However, most existing CL methods focus on addressing catastrophic forgetting in neural networks by minimizing the modification of the learnt model for old tasks. This inevitably limits the backward knowledge transfer from the new task to the old tasks, because judicious model updates could possibly improve the learning performance of the old tasks as well. To tackle this problem, we first theoretically analyze the conditions under which updating the learnt model of old tasks could be beneficial for CL and also lead to backward knowledge transfer, based on the gradient projection onto the input subspaces of old tasks. Building on the theoretical analysis, we next develop a ContinUal learning method with Backward knowlEdge tRansfer (CUBER), for a fixed capacity neural network without data replay. In particular, CUBER first characterizes the task correlation to identify the positively correlated old tasks in a layer-wise manner, and then selectively modifies the learnt model of the old tasks when learning the new task. Experimental studies show that CUBER can even achieve positive backward knowledge transfer on several existing CL benchmarks for the first time without data replay, where the related baselines still suffer from catastrophic forgetting (negative backward knowledge transfer). The superior performance of CUBER on the backward knowledge transfer also leads to higher accuracy accordingly.

Energy demand and supply vary from area to area, where an unbalanced load may occur and endanger the system security constraints and cause significant differences in the locational marginal price (LMP) in the power system. With the increasing proportion of local renewable energy (RE) sources in microgrids that are connected to the power grid and the growing number of electric vehicle (EV) charging loads, the imbalance will be further magnified. In this article, we first model the EV charging network as a cyber–physical system (CPS) that is coupled with both the transportation networks and the smart grids. Then, we propose an EV charging station recommendation algorithm. With a proper charging scheduling algorithm deployed, the synergy between the transportation network and the smart grid can be created. The EV charging activity will no longer be a burden for power grids, but a load-balancing tool that can transfer energy between the unbalanced distribution grids. The proposed system model is validated via simulations. The results show that the proposed algorithms can optimize the EV charging behaviors, reduce charging costs, and effectively balance the regional load profiles of the grids.

The electric vehicle (EV) fleet is gradually growing into a major part of public transportation. Proper planning and operation of EV supply equipment (EVSE) is essential to ensure the efficient and economic operations of the EV fleets. Charging stations (CS) have gained market attention due to their lower cost and versatility. Battery swapping stations (BSS) have also received considerable attention because of their promise to provide fast and sustainable battery replacements. However, their commercial viability is unclear due to their requirement for large capital and infrastructure deployment. In this paper, we develop a stochastic model for interactions between CS/BSS and taxi/bus fleets. The model is based on a realistic abstraction of users’ behavior defined by various stochastic processes. It also considers the dynamic impacts of the road congestion. Analytical revenue boundaries are derived and verified by simulations. These simulation results may prove valuable for future studies of public transit.

In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user’s monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user’s personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.

Precise citywide mobile traffic prediction is of great significance for intelligent network planning and proactive service provisioning. Current traffic prediction approaches mainly focus on training a well-performed model for the cities with a large amount of mobile traffic data. However, for the cities with scarce data, the prediction performance will be greatly limited. To tackle this problem, in this paper we propose a novel cross-city deep transfer learning framework named CCTP for citywide mobile traffic prediction in cities with data scarcity. Specifically, we first present a novel spatial-temporal learning model and pre-train the model by abundant data of a source city to obtain prior knowledge of mobile traffic dynamics. We then devise an efficient generative adversarial network (GAN) based cross-domain adapter for distribution alignment between target data and source data. To deal with data scarcity issue in some clusters of target city, we further design an inter-cluster transfer learning strategy for performance enhancement. Extensive experiments conducted on real-world mobile traffic datasets demonstrate that our proposed CCTP framework can achieve superior performance in citywide mobile traffic prediction with data scarcity.

Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, which is known as catastrophic forgetting. To learn new task without forgetting, recently, the mask-based learning method (e.g. piggyback [10]) is proposed to address this issue by learning only a binary element-wise mask, while keeping the backbone model fixed. However, the binary mask has limited modeling capacity for new tasks. A more recent work [5] proposes a compress-grow-based method (CPG) to achieve better accuracy for new tasks by partially training backbone model, but with order-higher training cost, which makes it infeasible to be deployed into popular state-of-the-art edge-/mobile-learning. The primary goal of this work is to simultaneously achieve fast and high-accuracy multi task adaption in continual learning setting. Thus motivated, we propose a new training method called Kernelwise Soft Mask (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task. Such a hybrid mask can be viewed as a superposition of a binary mask and a properly scaled real-value tensor, which offers a richer representation capability without low-level kernel support to meet the objective of low hardware overhead. We validate KSM on multiple benchmark datasets against recent state-of-the-art methods (e.g. Piggyback, Packnet, CPG, etc.), which shows good improvement in both accuracy and training cost.

We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data. The unreliable nature of wireless connectivity, together with constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Thus motivated, we propose SGD-based bandlimited coordinate descent algorithms for such settings. Specifically, for the wireless edge employing over-the-air computing, a common subset of k-coordinates of the gradient updates across edge devices are selected by the receiver in each iteration, and then transmitted simultaneously over k sub-carriers, each experiencing time-varying channel conditions. We characterize the impact of communication error and compression, in terms of the resulting gradient bias and mean squared error, on the convergence of the proposed algorithms. We then study learning-driven communication error minimization via joint optimization of power allocation and learning rates. Our findings reveal that optimal power allocation across different sub-carriers should take into account both the gradient values and channel conditions, thus generalizing the widely used water-filling policy. We also develop sub-optimal distributed solutions amenable to implementation.

There has recently been an increasing interest in computationally-efficient learning methods for resource-constrained applications, e.g., pruning, quantization and channel gating. In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which can speed up subnet selection for a new task at the resource-limited node. In particular, we develop a federated meta-learning algorithm to jointly train good meta-initializations for both the backbone networks and gating modules, by leveraging the model similarity across learning tasks on different nodes. In this way, the learnt meta-gating module effectively captures the important filters of a good meta-backbone network, and a task-specific conditional channel gated network can be quickly adapted from the meta-initializations using data samples of the new task. The convergence of the proposed federated meta-learning algorithm is established under mild conditions. Experimental results corroborate the effectiveness of our method in comparison to related work.

Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adaptation, a single agent alone has to learn over many tasks, and this is the so-called 'cold-start' problem. Observing that in a multi-agent network the learning tasks across different agents often share some model similarity, we ask the following fundamental question: "Is it possible to accelerate the online meta-learning across agents via limited communication and if yes how much benefit can be achieved?" To answer this question, we propose a multi-agent online meta-learning framework and cast it as an equivalent two-level nested online convex optimization (OCO) problem. By characterizing the upper bound of the agent-task-averaged regret, we show that the performance of multi-agent online meta-learning depends heavily on how much an agent can benefit from the distributed network-level OCO for meta-model updates via limited communication, which however is not well understood. To tackle this challenge, we devise a distributed online gradient descent algorithm with gradient tracking where each agent tracks the global gradient using only one communication step with its neighbors per iteration, and it results in an average regret O(T/N) per agent, indicating that a factor of 1/N speedup over the optimal single-agent regret O(T) after T iterations, where N is the number of agents. Building on this sharp performance speedup, we next develop a multi-agent online meta-learning algorithm and show that it can achieve the optimal task-average regret at a faster rate of O(1 N/T) via limited communication, compared to single-agent online meta-learning. Extensive experiments corroborate the theoretic results.

In order to meet the requirements for performance, safety, and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge. However, the constrained resources and limited local data amount pose significant challenges to the development of edge AI. To overcome these challenges, we explore continual edge learning capable of leveraging the knowledge transfer from previous tasks. Aiming to achieve fast and continual edge learning, we propose a platform-aided federated meta-learning architecture where edge nodes collaboratively learn a meta-model, aided by the knowledge transfer from prior tasks. The edge learning problem is cast as a regularized optimization problem, where the valuable knowledge learned from previous tasks is extracted as regularization. Then, we devise an ADMM based federated meta-learning algorithm, namely ADMM-FedMeta, where ADMM offers a natural mechanism to decompose the original problem into many subproblems which can be solved in parallel across edge nodes and the platform. Further, a variant of inexact-ADMM method is employed where the subproblems are 'solved' via linear approximation as well as Hessian estimation to reduce the computational cost per round to O(n). We provide a comprehensive analysis of ADMM-FedMeta, in terms of the convergence properties, the rapid adaptation performance, and the forgetting effect of prior knowledge transfer, for the general non-convex case. Extensive experimental studies demonstrate the effectiveness and efficiency of ADMM-FedMeta, and showcase that it substantially outperforms the existing baselines.

The ensemble method is a promising way to mitigate the overestimation issue in Q-learning, where multiple function approximators are used to estimate the action values. It is known that the estimation bias hinges heavily on the ensemble size (i.e., the number of Q-function approximators used in the target), and that determining the 'right' ensemble size is highly nontrivial, because of the time-varying nature of the function approximation errors during the learning process. To tackle this challenge, we first derive an upper bound and a lower bound on the estimation bias, based on which the ensemble size is adapted to drive the bias to be nearly zero, thereby coping with the impact of the time-varying approximation errors accordingly. Motivated by the theoretic findings, we advocate that the ensemble method can be combined with Model Identification Adaptive Control (MIAC) for effective ensemble size adaptation. Specifically, we devise Adaptive Ensemble Q-learning (AdaEQ), a generalized ensemble method with two key steps: (a) approximation error characterization which serves as the feedback for flexibly controlling the ensemble size, and (b) ensemble size adaptation tailored towards minimizing the estimation bias. Extensive experiments are carried out to show that AdaEQ can improve the learning performance than the existing methods for the MuJoCo benchmark.

We consider a reinforcement learning (RL) based joint cache placement and delivery (CPD) policy for cellular networks with limited caching capacity at both Base Stations (BSs) and User Equipments (UEs). The dynamics of file preferences of users is modeled by a Markov process. User requests are based on current preferences, and on the content of the user’s cache. We assume probabilistic models for the cache placement at both the UEs and the BSs. When the network receives a request for an un-cached file, it fetches the file from the core network via a backhaul link. File delivery is based on network-level orthogonal multipoint multicasting transmissions. For this, all BSs caching a specific file transmit collaboratively in a dedicated resource. File reception depends on the state of the wireless channels. We design the CPD policy while taking into account the user Quality of Service and the backhaul load, and using an Actor-Critic RL framework with two neural networks. Simulation results are used to show the merits of the devised CPD policy.

The pool-hopping attack casts down the expected profits of both the mining pool and honest miners in Blockchain. The mainstream countermeasures, namely PPS (pay-per-share) and PPLNS (pay-per-last-N-share), can hedge pool hopping but need to charge miners some fees when they join in a pool. Obviously, the higher fee charged, the higher cost of joining the pool, the less motivation of a miner to mine in the pool. In this article, we apply the zero-determinant (ZD) theory to design a novel pooled mining which offers an incentive mechanism for motivating miners not to switch in pools strategically by economic means without fee charged. In short, the proposed pooled mining has three unique features: 1) fee-free. No fee is charged if the miner does not hop, 2) wide applicability. It can be employed in both prepaid and postpaid mechanisms, and 3) fairness. Even can dominate the game with any miner, a pool has to cooperate when a miner does not hop among pools, implying that the pool cannot squeeze the honest miners financially. The fairness of our scheme makes it have long-term sustainability. Both theoretical analyses and numerical simulations demonstrate the effectiveness of our scheme.

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.

Electric vehicles (EVs) are becoming increasingly popular, but the frequent charging and large charging latency remain major obstacles to the EV industry. This article focuses on the charging scheduling of on-the-move EVs in a transportation network to minimize EVs' charging latency, including driving time to charging stations (CSs), wait time and charging time. We formulate this charging scheduling problem as a graphical game to characterize the strong couplings of charging latency among neighboring EV players. Specially, we investigate correlated equilibrium (CE) to describe the joint strategies of EV players, which is expected to further reduce the charging latency of EVs compared with Nash equilibrium (NE). It is shown that CE always exists in a finite game, and can be found by linear programming tools. In addition, we propose a method of wait time prediction, which can improve the prediction accuracy by combining the data of deterministic EV arrivals and the stochastic property of potential EV arrivals. Simulation studies are used to examine the performance of the proposed game-based approach, the efficiency of CE, the preciseness of our proposed wait time prediction method, the impacts of CS deployment on EVs' charging latency, etc. We can draw a conclusion that our method has apparent advantages in situations where the locations of EV players are in dense manners.

A cost-efficient deal that can achieve high sensing quality with a low reward is the permanent goal of the requestor in mobile crowdsensing, which heavily depends on the quantity and quality of the workers. However, the spatial diversity and temporal dynamics lead to heterogeneous worker supplies, making it hard for the requestor to utilize a homogeneous pricing strategy to realize a cost-efficient deal from a systematic point of view. Therefore, a cost-efficient deal calls for a cost-efficient pricing strategy, boosting the whole sensing quality with less operation (computation) cost. However, the state-of-the-art studies ignore the dual cost-efficient demands of large-scale sensing tasks. Hence, we propose a combinatorial pinning zero-determinant (ZD) strategy, which empowers the requestor to utilize a single strategy within its feasible range to minimize the total expected utilities of the workers throughout all sensing regions for each time interval, without being affected by the strategies of the workers. Through turning the worker-customized strategy to an interval-customized one, the proposed combinatorial pinning ZD strategy reduces the number of pricing strategies required by the requestor from O(n3) to O(n). Besides, it extends the application scenarios of the classical ZD strategy from two-player simultaneous-move games to multiple-heterogeneous-player sequential-move ones, where a leader can determine the linear relationship of the players' expected utilities. Such an extension enriches the theoretical hierarchy of ZD strategies, broadening their application scope. Extensive simulations based on real-world data verify the effectiveness and efficiency of the proposed scheme.

With a sponsored content scheme of mobile services, a content provider can encourage end users/subscribers to access its contents, e.g., with an advertisement, by paying part of the data price to the network operator. As a result, the content provider and end users are both actively engaged into the sponsored content ecosystem. As such, a key challenge is how to provide proper sponsorship given the content demand from the users and the service fee charged by the network operator. Furthermore, the information asymmetry between the content provider and users makes the sponsorship problem more challenging. In this paper, we propose a Stackelberg game-based framework to tackle this challenge. In the framework, the network operator, as the leader, determines the data price first, and the content provider as well as users, as the followers, make the decisions on sponsorship and content demand based on the data price, respectively. We model the interaction between the content provider and the users as a contract game in the presence of asymmetric information. In the contract game, the content provider designs a contract that contains its sponsorship strategies toward all types of users. We then derive the necessary and sufficient conditions of feasible contracts and obtain an optimal contract to maximize the profit of the content provider. Taking into account the optimal contract of contract game, we also investigate the optimal pricing of the network operator through backward induction. We prove that the Stackelberg equilibrium is unique under a mild condition and present the numerical results to illustrate some important properties of the equilibrium.

To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity. However, as the high density of BSs is designed to accommodate peak traffic, it would consume an unnecessarily large amount of energy if BSs are on during off-peak time. To save the energy consumption of cellular networks, an effective way is to deactivate some idle base stations that do not serve any traffic demand. In this paper, we develop a traffic-aware dynamic BS sleep control framework, named DeepBSC, which presents a novel data-driven learning approach to determine the BS active/sleep modes while meeting lower energy consumption and satisfactory Quality of Service (QoS) requirements. Specifically, the traffic demands are predicted by the proposed GS-STN model, which leverages the geographical and semantic spatial-temporal correlations of mobile traffic. With accurate mobile traffic forecasting, the BS sleep control problem is cast as a Markov Decision Process that is solved by Actor-Critic reinforcement learning methods. To reduce the variance of cost estimation in the dynamic environment, we propose a benchmark transformation method that provides robust performance indicator for policy update. To expedite the training process, we adopt a Deep Deterministic Policy Gradient (DDPG) approach, together with an explorer network, which can strengthen the exploration further. Extensive experiments with a real-world dataset corroborate that our proposed framework significantly outperforms the existing methods.

Recent advances in artificial intelligence have driven increasing intelligent applications at the network edge, such as smart home, smart factory, and smart city. To deploy computationally intensive Deep Neural Networks (DNNs) on resource-constrained edge devices, traditional approaches have relied on either offloading workload to the remote cloud or optimizing computation at the end device locally. However, the cloud-assisted approaches suffer from the unreliable and delay-significant wide-area network, and the local computing approaches are limited by the constrained computing capability. Towards high-performance edge intelligence, the cooperative execution mechanism offers a new paradigm, which has attracted growing research interest recently. In this paper, we propose CoEdge, a distributed DNN computing system that orchestrates cooperative DNN inference over heterogeneous edge devices. CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions. Experimental evaluations based on a realistic prototype show that CoEdge outperforms status-quo approaches in saving energy with close inference latency, achieving up to 25.5% ~ 66.9% energy reduction for four widely-adopted CNN models.

We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks. The workers are allowed to report noisy versions of their data for privacy protection; and the platform selects workers by taking into account their sensing capabilities to ensure the accuracy level of the aggregated result. Observe that when moving the control of data privacy from the data aggregator to the workers, the data aggregator has limited market power in the sense that it can only partially control the noise by judiciously choosing a subset of workers based on workers' privacy preferences. This introduces externalities because the privacy of each worker depends on the total noise in the aggregated result that in turn relies on which workers are selected. Specifically, we first consider a privacy-passive scenario where workers participate if their privacy loss can be adequately compensated by the rewards. We explicitly characterize the externalities and the hidden monotonicity property of the problem, making it possible to design a truthful, individually rational and computationally efficient incentive mechanism. We then extend the results to a privacy-proactive scenario where workers have individual requirements for their perceivable data privacy levels. Our proposed mechanisms for both scenarios can select a subset of workers to (nearly) minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local data. To tackle these challenges, we propose a platform-aided collaborative learning framework where a model is first trained across a set of source edge nodes by a federated meta-learning approach, and then it is rapidly adapted to learn a new task at the target edge node, using a few samples only. Further, we investigate the convergence of the proposed federated meta-learning algorithm under mild conditions on node similarity and the adaptation performance at the target edge. To combat against the vulnerability of meta-learning algorithms to possible adversarial attacks, we further propose a robust version of the federated meta-learning algorithm based on distributionally robust optimization, and establish its convergence under mild conditions. Experiments on different datasets demonstrate the effectiveness of the proposed Federated Meta-Learning based framework.

In order to meet the real-time performance requirements, intelligent decisions in many IoT applications must take place right here right now at the network edge. The conventional cloud-based learning approach would not be able to keep up with the demands in achieving edge intelligence in these applications. Nevertheless, pushing the artificial intelligence (AI) frontier to achieve edge intelligence is highly nontrivial due to the constrained computing resources and limited training data at the network edge. To tackle these challenges, we develop a distributionally robust optimization (DRO)-based edge learning algorithm, where the uncertainty model is constructed to foster the synergy of cloud knowledge transfer and local training. Specifically, the knowledge transferred from the cloud is in the form of a Dirichlet process prior distribution for the edge model parameters, and the edge device further constructs an uncertainty set centered around the empirical distribution of its local samples to capture the information of local data processing. The edge learning DRO problem, subject to the above two distributional uncertainty constraints, is then recast as an equivalent single-layer optimization problem using a duality approach. We then use an Expectation-Maximization (EM) algorithm-inspired method to derive a convex relaxation, based on which we devise algorithms to learn the edge model parameters. Finally, extensive experiments are implemented to showcase the performance gain over standard learning approaches using local edge data only.

For modern large-scale networked systems, ranging from cloud to edge computing systems, the topology design has a significant impact on the system performance in terms of scalability, cost, latency, throughput, and fault-tolerance. These performance metrics may conflict with each other and design criteria often vary across different networks. To date, there has been little theoretic foundation on topology designs from a prescriptive perspective, indicating that the current status quo of the design process is more of an art than a science. In this paper, we advocate a novel unified framework to describe, generate, and analyze topology design in a systematic fashion. By reverse-engineering existing topology designs and developing a fine-grained decomposition method for topology design, we propose a general procedure that serves as a common language to describe topology design. By proposing general criteria for the procedure, we devise a top-down approach to generate topology models, based on which we can systematically construct and analyze new topologies. To validate our approach, we leverage concrete tools based on combinatorial design theory and propose a novel layered topology model. With quantitative performance analysis, we reveal the trade-offs among performance metrics and generate new topologies with various advantages for different large-scale networks.

The past few years have witnessed the explosive growth of Internet of Things (IoT) devices. The necessity of real-time edge intelligence for IoT applications demands that decision making must take place right here right now at the network edge, thus dictating that a high percentage of the IoT created data should be stored and analyzed locally. However, the computing resources are constrained and the amount of local data is often very limited at edge nodes. To tackle these challenges, we propose a distributionally robust optimization (DRO)-based edge intelligence framework, which is based on an innovative synergy of cloud knowledge transfer and local learning. More specifically, the knowledge transfer from the cloud learning is in the form of a reference distribution and its associated uncertainty set. Further, based on its local data, the edge device constructs an uncertainty set centered around its empirical distribution. The edge learning problem is then cast as a DRO problem subject to the above two distribution uncertainty sets. Building on this framework, we investigate two problem formulations for DRO-based edge intelligence, where the uncertainty sets are constructed using the Kullback-Leibler divergence and the Wasserstein distance, respectively. Numerical results demonstrate the effectiveness of the proposed DRO-based framework.

Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments.

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.

In this work, we consider the multi-party privacy conflict (MPC) in an online social network (OSN). As many data items uploaded to the OSN are “co-owned” by multiple users with different privacy concerns, some personal information of OSN users may be disclosed by others unintentionally. On the contrary with existing mainstream OSN platforms allowing only the very user uploading the data to set the privacy level, in this article we take a fine-grained approach to resolve MPC, in which all co-owners independently determine whether to share their personal content within the data on OSN. Interacted with its peers, the opinion of a co-owner, however, might be influenced by and consequently influence the decision of its peers. To this end, each co-owner, as an individual decision maker, strikes a tradeoff between its internal privacy preference and the external social influence from its neighbors in a OSN. Specifically, we formulate the interaction among co-owners as a multi-player non-cooperative game with a network structure representing their social relations. For the proposed network game, we establish the existence of multiple (pure-strategy) equilibria, and characterize them accordingly. The convergence of interaction is also investigated when synchronous and asynchronous best-response updates are used, respectively. We note that when the action set for the players is discrete, the game exhibits non-linear dynamics, making it challenging to analyze the convergence behavior. We prove that synchronous update may lead to either an equilibrium or a strategy cycle, and the asynchronous update always leads to an equilibrium. Building upon this analysis, we advocate a practical implementation of the proposed MPC management, which balances the automation of the management and intervention of users. Moreover, we take one step further to develop approaches aiming to reach a “stronger agreement” among the players for the sake of benefits of uploader and OSN provider. Numerical examples are also provided to corroborate the analytical results.

We study Waze-inspired spectrum discovery, where the cloud collects the spectrum sensing results from many smartphones and predicts location-specific spectrum availability based on information fusion. Observe that with limited sensing capability, each smartphone can sense only a limited number of channels; and further, the more channels each smartphone senses, the less accurate the sensing results would be. In particular, we consider two different smartphone sensing models: a homogeneous model and a heterogeneous model. To develop a comprehensive understanding, we cast the spectrum discovery problem as a matrix recovery problem, which is different from the classical matrix completion problem, in the sense that it suffices to determine only part of the matrix entries in the matrix recovery formulation. It is shown that the widely-used similarity-based collaborative filtering method would not work well because it requires each smartphone to sense too many channels. With this motivation, we propose a location-aided smartphone data fusion method and show that the channel numbers each smartphone needs to sense could be dramatically reduced. Moreover, we analyze the partial matrix recovery performance by using the location-aided data fusion method. Both theoretical analysis and numerical results corroborate the intuition that with each smartphone sensing more channels, the recovery performance improves at first but then degrades beyond some point because of the decreasing sensing accuracy.

In an effort to facilitate increased online multimedia content, Transactions on Wireless Communications (TWC) has established a new Online Editorial Team. One to two articles will be selected each month. The team will be working with authors to create slides, scripts, and video clips, showcasing innovative and exciting ideas from these articles. Emil Bjornson, Michele Wigger, and Zhu Han will serve as TWC’s Inaugural Editors for this new team; please welcome them to the TWC team.

In a dynamic network environment, the applicability of traffic engineering techniques requires fresh traffic measurements, fast routing solvers and frequent network reconfigurations. However, the ages of traffic measurements exhibit significant variation due to asynchronization and random communication delays between routers and controllers. Besides, frequent reconfigurations may incur routing instability, and hence impair network utilization. We devise a controller-assisted distributed routing scheme with recursive link weight reconfigurations, accounting for the impact of measurement ages and routing instability. In particular, the controller estimates the current traffic conditions using an autoregressive model to account for the uncertainty of the age of measurements. A fast load-sensitive link weight update algorithm swiftly computes a new set of OSPF weights by using the estimated link loads. To reduce complexity, a myopic policy is used to determine link weight reconfiguration, which takes into consideration congestion, measurement ages, and possible instability. Since distributed routing offers stronger robustness against link failures compared to centralized routing, the proposed adaptive routing approach offers desirable robustness and further benefits from the controller assistance via iterative search of better OSPF weights.

Edge computing is envisioned to be the de-facto paradigm of hosting emerging low latency Internet-of-Things (IoT) data streaming services.For IoT data streaming in edge computing, cost management is of strategic significance, due to the low cost-efficiency of edge servers. While existing literature adopts a reactive approach to dynamically provisioning edge servers to reduce cost, the delay of server activation and instantiation has been mostly ignored. In this paper, we target a proactive approach to dynamic edge server provisioning for real-time IoT data streaming across edge nodes, which adjusts server provisioning ahead of time, based on prediction of the upcoming workload. To effectively predict upcoming workload, a learning-based method online gradient descent is applied. We further combine the online learning method with an online optimization algorithm for server provisioning in a joint online optimization framework, through (1) minimizing of the regret incurred by inaccurate workload prediction, and (2) minimizing the cost incurred by near-optimal online decisions. The resulting predictive online algorithm can well leverage the power of prediction and achieve a good performance guarantee, as verified by both rigorous theoretical analysis and extensive trace-driven evaluations.

Federated learning is an emerging machine learning technique that enables distributed model training using local datasets from large-scale nodes, e.g., mobile devices, but shares only model updates without uploading the raw training data. This technique provides a promising privacy preservation for mobile devices while simultaneously ensuring high learning performance. The majority of existing work has focused on designing advanced learning algorithms with an aim to achieve better learning performance. However, the challenges, such as incentive mechanisms for participating in training and worker (i.e., mobile devices) selection schemes for reliable federated learning, have not been explored yet. These challenges have hindered the widespread adoption of federated learning. To address the above challenges, in this article, we first introduce reputation as the metric to measure the reliability and trustworthiness of the mobile devices. We then design a reputation-based worker selection scheme for reliable federated learning by using a multiweight subjective logic model. We also leverage the blockchain to achieve secure reputation management for workers with nonrepudiation and tamper-resistance properties in a decentralized manner. Moreover, we propose an effective incentive mechanism combining reputation with contract theory to motivate high-reputation mobile devices with high-quality data to participate in model learning. Numerical results clearly indicate that the proposed schemes are efficient for reliable federated learning in terms of significantly improving the learning accuracy.

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.

Demand response (DR), as a vital component of smart grid, plays an important role in shaping the load profiles in order to improve system reliability and efficiency. Incentive-based DR has been used in many DR programs by incentivizing customers to adapt their loads to supply availability. Note that users' behavior patterns can be easily identified from fine-grained power consumption when interacting with the load serving entity (LSE), giving rise to serious privacy concerns. One common approach to address the privacy threats is to incorporate perturbations in users' load measurements. Although it can protect the users' privacy, yet the usage data modification would degrade the LSE's performance in achieving an optimal incentive strategy due to unknown characteristics of the augmented perturbations. In this paper, we cast the incentive-based DR problem as a stochastic Stackelberg game. To tackle the challenge induced by users' privacy protection behaviors, we propose a two-timescale reinforcement learning algorithm to learn the optimal incentive strategy under users' perturbed responses. The proposed algorithm computes the expected utility cost to mitigate the impacts of the random characteristics of the augmented perturbations and then updates the incentive strategy based on the perceived expected utility costs. We derive the conditions under which the proposed incentive scheme converges almost surely to an ε-optimal strategy. The efficacy of the proposed algorithm is demonstrated using extensive numerical simulation using real data.

Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an$H_{\infty }$filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy.

Fog computing, evolves from the cloud and migrates the computing to the edge, is a promising solution to meet the increasing demand for ultra-low latency services in wireless networks. Via the forward-looking perspective, we advocate a Fog Radio Access Network (F-RAN) model, which leverages the existing infrastructure such as small cells with limited computing power, to achieve the ultra-low latency by joint edge computing and near-range communications across multiple Fog groups. We formulate the low latency design as an NP-hard optimization problem, which demonstrates the tradeoff between communication and computing in the time domain. Due to each F-RAN node's potential as each user's master F-RAN node with 1) different self computing power; and 2) different cooperative power of assisted F-RAN nodes, we first tackle globally optimized master F-RAN node selection for each user and propose a latency-driven cooperative Fog algorithm with dynamic programming solution for simultaneous selection of the F-RAN nodes to serve proper heterogeneous Fog resource allocation for multi-Fog groups. Considering the limited heterogeneous Fog resources shared among all users, we propose the one-for-all strategy for every user putting him/herself into others' shoes and reaching a “win-win” outcome. The numerical results show that the low latency services can be accomplished by F-RAN via latency-driven Fog cooperation approach.

In this paper, online convex optimization (OCO) problems with time-varying objective and constraint functions are studied from the perspective of an agent who takes actions in real-time. Information about the current objective and constraint functions is revealed only after the corresponding action is already chosen. Inspired by a fast converging algorithm for time-invariant optimization in the very recent work [1], we develop a novel online algorithm based on virtual queues for constrained OCO. Optimal points of the dynamic optimization problems with full knowledge of the current objective and constraint functions are used as a dynamic benchmark sequence. Upper bounds on the regrets with respect to the dynamic benchmark and the constraint violations are derived for the presented algorithm in terms of the temporal variations of the underlying dynamic optimization problems. It is observed that the proposed algorithm possesses sublinear regret and sublinear constraint violations, as long as the temporal variations of the optimization problems are sublinear, i.e., the objective and constraint functions do not vary too drastically across time. The performance bounds of the proposed algorithm are superior to those of the state-of-the-art OCO method in most scenarios.

In this paper, joint energy procurement and demand response is studied from the perspective of the operator of a power system. The operator procures energy from both renewable energy sources (RESs) and the spot market. We observe the fact that the RESs may incur considerable infrastructure cost. This cost is taken into account and the optimal planning of renewables is examined by controlling the investment in RES infrastructures. Due to the uncertainty of renewables, the operator can also purchase energy directly from the spot market to compensate for the possible deficit incurred by the realization of the random renewable energy. By setting appropriate prices, the operator sells the collected energy to heterogeneous end users with different demand response characteristics. We model the decision making process of the operator as a two-stage optimization problem. The optimal decisions on the renewable deployment, energy purchase from the spot market and pricing schemes are derived. Several solution structures are observed and a computationally efficient algorithm, requiring only closed-form calculation and simple bisection search, is proposed to compute the optimal decisions. Finally, numerical experiments are conducted to verify the optimality of the proposed algorithm and the solution structures observed theoretically.

With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model parameters is highlighted to obtain engineering insights into the content caching problem.

We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for a sensing task. In this framework, the workers are allowed to report privacy-preserving versions of their data to protect their data privacy; and the platform selects workers based on their sensing capabilities, which aims to address the drawbacks of game-theoretic models that cannot ensure the accuracy level of the aggregated result, due to the existence of multiple Nash Equilibria. Observe that in this auction based framework, there exists externalities among workers' data privacy, because the data privacy of each worker depends on both her injected noise and the total noise in the aggregated result that is intimately related to which workers are selected to fulfill the task. To achieve a desirable accuracy level of the data aggregation in a cost-effective manner, we explicitly characterize the externalities, i.e., the impact of the noise added by each worker on both the data privacy and the accuracy of the aggregated result. Further, we explore the problem structure, characterize the hidden monotonicity property of the problem, and determine the critical bid of workers, which makes it possible to design a truthful, individually rational and computationally efficient incentive mechanism. The proposed incentive mechanism can recruit a set of workers to approximately minimize the cost of purchasing private sensing data from workers subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

We study Waze-inspired spectrum discovery, where the cloud collects the spectrum sensing results from many smartphones and predicts location-specific spectrum availability based on information fusion. Observe that with limited sensing capability, each smartphone can sense only a limited number of channels; and further, the more channels each smartphone senses, the less accurate the sensing results would be. To develop a comprehensive understanding, we cast the spectrum discovery problem as a matrix recovery problem, which is different from the classical matrix completion problem, in the sense that it suffices to determine only part of the matrix entries in the matrix recovery formulation. It is shown that the widely-used similarity-based collaborative filtering method would not work well because it requires each smartphone to sense too many channels. With this motivation, we propose a location-aided smartphone data fusion method and show that the channel numbers each smartphone needs to sense could be dramatically reduced. Moreover, we analyze the partial matrix recovery performance by using the location-aided data fusion method, and numerical results corroborate the intuition that with each smartphone sensing more channels, the recovery performance improves at first but then degrades beyond some point because of the decreasing sensing accuracy.

Social and communication networks interact with each other in multifaceted ways, yet these interactions are often considered to be secondary in throughput, privacy and security analysis for communication networks. In this paper, full-duplex (FD) and half-duplex (HD) multi-antenna cooperative communication systems are studied by taking both physical links and social connections into account. An optimal beamformer for maximizing communication rate in the proposed socio-technological setting aims to balance between the direct link and the cooperating link as well as respecting the trust degree between the users. The resulting optimization problems are nontrivial to solve, even numerically, as they are not convex. The complexity of the problems is significantly reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rates both in the FD and HD modes. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies assume a trustworthy fusion center (FC) in their co-design of incentive mechanism and privacy preservation. Very recent work has taken the step to relax the assumption on trustworthy FC and allowed participatory users (PUs) to randomly report their binary sensing data, whereas the focus is to examine PUs' equilibrium behavior. Making a paradigm shift, this paper aims to study the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such a scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer injecting larger noises for higher privacy-preserving levels (PPLs). To strike a good balance therein, we propose an efficient incentive mechanism named REAP to reconcile FC's aggregation accuracy and individual PU's data privacy. Specifically, we adopt the celebrated notion of differential privacy to quantify PUs' PPLs and characterize their impacts on FC's aggregation accuracy. Then, appealing to contract theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control them. It can further overcome the information asymmetry problem, i.e., FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.

The rapid growth of cloud computing and data centers with skyrocketing energy consumption, together with the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. Data centers offering large flexible loads in the grid, opens up a unique opportunity to smooth out the significant fluctuation and uncertainty of renewable generation and hence enable seamless integration. To take the market power of data centers into consideration, this paper proposes a bargaining solution to the market program for data center demand response when the load serving entity (LSE) has power supply deficiency. Specifically, due to the uncertainty of load flexibility of data centers incurred by the intermittent on-site renewable generation and dynamic service requests, there exists information asymmetry between the LSE and the data center, which complicates the design of the bargaining solution. Making use of the log-concavity of the (expected) utility functions, a computationally efficient method to implement the best response updates in the bargaining procedure is presented. Furthermore, it is shown analytically that the bid sequences of the LSE and the data center are guaranteed to converge and the final price clinched by the bargaining algorithm is indeed the Nash bargaining solution, which is proportionally fair. In addition, the proposed bargaining solution is compared with two other schemes, namely the Stackelberg game and the social welfare maximization schemes. Finally, extensive numerical experiments are conducted to validate the theoretical guarantees of the bargaining and to examine the impact of various model parameters. Empirical comparison indicates the fairness advantage of the bargaining approach over the other two schemes, especially when the load of the data center is not very flexible, highlighting the importance of information feedback embodied by the bargaining procedure.

In this paper, beamforming design is investigated for a multi-antenna cooperative communication system in which both physical links and social connections (trust degrees) between nodes are taken into account. An optimal beamformer aims to balance between the direct link and the cooperating link as well as respecting the trust degree. The resulting optimization problem is nontrivial to solve, even numerically, as it is not convex. The complexity of the problem is largely reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rate. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

Mobile edge caching enables content delivery directly within the radio access network, which effectively alleviates the backhaul burden and reduces round-trip latency. To fully exploit the edge resources, the most popular contents should be identified and cached. Observing that content popularity varies greatly at different locations, to maximize local hit rate, this paper proposes an online learning algorithm that dynamically predicts content hit rate, and makes location-differentiated caching decisions. Specifically, a linear model is used to estimate the future hit rate. Considering the variations in user demand, a perturbation is added to the estimation to account for uncertainty. The proposed learning algorithm requires no training phase, and hence is adaptive to the time-varying content popularity profile. Theoretical analysis indicates that the proposed algorithm asymptotically approaches the optimal policy in the long term. Extensive simulations based on real world traces show that, the proposed algorithm achieves higher hit rate and better adaptiveness to content popularity fluctuation, compared with other schemes.

In this paper we propose HyFog, a novel hybrid task offloading framework in fog computing, where device users have the flexibility of choosing among multiple options for task executions, including local mobile execution, Device-to-Device (D2D) offloaded execution, and Cloud offloaded execution. We further develop a novel three-layer graph matching algorithm for efficient hybrid task offloading among the devices. Specifically, we first construct a three-layer graph to capture the choice space enabled by these three execution approaches, and then the problem of minimizing the total task execution cost is recast as a minimum weight matching problem over the constructed three-layer graph, which can be efficiently solved using the Edmonds's Blossom algorithm. Numerical results demonstrate that the proposed three-layer graph matching solution can achieve superior performance, with more than 50% cost reduction over the case of local task executions by all the devices.

Fog computing is emerging as one promising solution to meet the increasing demand for ultra-low latency services in wireless networks. Taking a forward-looking perspective, we propose a Fog-Radio Access Network (F-RAN) model, which utilizes the existing infrastructure, e.g., small cells and macro base stations, to achieve the ultra-low latency by joint computing across multiple F-RAN nodes and near-range communications at the edge. We treat the low latency design as an optimization problem, which characterizes the tradeoff between communication and computing across multiple F-RAN nodes. Since this problem is NP-hard, we propose a latency-driven cooperative task computing algorithm with one-for-all concept for simultaneous selection of the F-RAN nodes to serve with proper heterogeneous resource allocation for multi-user services. Considering the limited heterogeneous resources shared among all users, we advocate the one-for-all strategy for every user taking other's situation into consideration and seek for a "win-win" solution. The numerical results show that the low latency services can be achieved by F-RAN via latency-driven cooperative task computing.

The rapid growth of online social networks has strengthened wireless users' social relationships, which in turn has resulted in more data traffic due to network effect in the social domain. Nevertheless, the boosted demand for wireless services may challenge the limited wireless capacity. To build a thorough understanding, we study mobile users' data usage behavior by jointly considering the network effect due to their social relationships in the social domain and the congestion effect in the physical wireless domain. Specifically, we develop a Stackelberg game for socially aware data usage: in Stage I, a wireless provider first decides the data pricing to all users in order to maximize its revenue, and then in Stage II, users decide their data usage, for the given price, subject to mutual interactions under both social network effect and congestion effect. We analyze the two-stage game via backward induction. In particular, for Stage II, we first provide conditions for the existence and the uniqueness of a user demand equilibrium (UDE). Then, we propose algorithms to find the UDE and for users to reach the UDE in a distributed manner. We further investigate the impact of different system parameters on the UDE. Next, for Stage I, we develop an optimal pricing algorithm to maximize the wireless provider's revenue. We numerically evaluate the performance of our proposed algorithms using real data, and thereby draw useful engineering insights for the operation of wireless providers: 1) when social network effect dominates congestion effect, the marginal gain of the total usage increases with the social ties and the number of users, or decreases with the congestion coefficient; in contrast, when congestion effect dominates social network effect, the marginal gain decreases (or increases, respectively) with these parameters and 2) when social network effect is strong, a lower price should be set to increase the total revenue; in contrast, when congestion effect is strong, a higher price is preferred.

Pricing and task allocation are vital to improving the efficiency in mobile crowdsensing, an emerging human-in-the-loop application paradigm. Previous studies focused on incentive mechanism design for specific sensing applications where one party (either task initiators or platform) can dominate the pricing and task allocation process. These results, however, are not applicable to a free crowdsensing market where multiple task initiators and task participants (mobile users), as peers, are engaged to maximize their own interests. New incentive mechanisms are pressingly needed to produce a solution, so that the interests of all participating parties can be considered. In this paper, appealing to exchange economy theory, we employ the notion of “Walrasian Equilibrium” as a comprehensive metric, at which there exists a price vector for mobile users and an allocation for task initiators such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). We consider a standard model where the utility function for sensing quality is monotonically increasing, differentiable, and concave, and the payoff function for a mobile user is linear. To address the problem, we first characterize the supply-demand pattern for a given price vector, which is the subset of mobile users selected by each task initiator to perform the task. We then devise methods for validating the existence of a Walrasian Equilibrium within each supply-demand pattern. One key step is to divide the space of prices into a collection of appropriate cells, based on the hyperplane arrangement, so that each cell has a unique supply-demand pattern. We devise an algorithm that can find a Walrasian Equilibrium in polynomial time, for a case of practical interest where the classes of mobile devices are bounded. Based on the insight, we further consider the general case and design an efficient pattern search (EPS) algorithm to reduce the search space, thus accelerating the search process accordingly. This is realized by choosing the supply-demand pattern which is closer to the “Walrasian Equilibrium” than the pattern in previous iteration in the search process. Our results show that EPS can find an $\epsilon $ -approximation Walrasian Equilibrium in polynomial time for the general case, given a constant $\epsilon $ .

Distributed spectrum access (DSA) is challenging, since an individual secondary user often has limited sensing capabilities only. One key insight is that channel recommendation among secondary users can help to take advantage of the inherent correlation structure of spectrum availability in both time and space, and enable users to obtain more informed spectrum opportunities. With this insight, we advocate to leverage the wisdom of crowds, and devise social recommendation aided DSA mechanisms to orient secondary users to make more intelligent spectrum access decisions, for both strong and weak network information cases. We start with the strong network information case where secondary users have the statistical information. To mitigate the difficulty due to the curse of dimensionality in the stochastic game approach, we take the one-step Nash approach and cast the social recommendation aided DSA decision making problem at each time slot as a strategic game. We show that it is a potential game, and then devise an algorithm to achieve the Nash equilibrium by exploiting its finite improvement property. For the weak information case where secondary users do not have the statistical information, we develop a distributed reinforcement learning mechanism for social recommendation aided DSA based on the local observations of secondary users only. Appealing to the maximum-norm contraction mapping, we also derive the conditions under which the distributed mechanism converges and characterize the equilibrium therein. Numerical results reveal that the proposed social recommendation aided DSA mechanisms can achieve a superior performance using real social data traces and its performance loss in the weak network information case is insignificant, compared with the strong network information case.

With increasing popularity of location-based services (LBSs), there have also been growing concerns for location privacy. To protect location privacy in an LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this paper, we leverage mobile users' social tie structure to motivate them to participate in pseudonym change. Drawing on a social group utility maximization framework, we cast users' decision making of whether to change pseudonyms as a socially aware pseudonym change game (SA-PCG). The SA-PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SA-PCG, we show that there exists a socially aware Nash equilibrium (SNE), and quantify the system efficiency of SNEs with respect to the optimal social welfare. Then, we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. We show that this algorithm efficiently finds an SNE that enjoys desirable properties: 1) it is socially aware coalition-proof, and thus is also Pareto-optimal; 2) it achieves higher social welfare than any SNE for the socially oblivious pseudonym change game. We further quantify the system efficiency of this SNE with respect to the optimal social welfare. We also show that this SNE can be achieved in a distributed manner. Numerical results using real data corroborate that social welfare can be significantly improved by exploiting social ties.

In spite of the pronounced benefit brought by crowdsensing, a user would not participate in sensing without adequate incentive, indicating that effective incentive design plays a critical role in making crowdsensing a reality. In this work, we examine the impact of two conflicting factors on incentives for users' participation: 1) the concern about privacy leakage and 2) the (positive) network effect from many sensing participants. The former factor hinders privacy- aware users from participating, whereas the latter encourages users' participation. Taking into consideration both factors, we devise a privacy-preserving crowdsensing scheme, in which a reverse `privacy' auction is first run by the crowdsensing platform to select users based on their privacy valuations and the network effect. Then the trusted platform carries out differentially private data aggregation over the collected data such that the released sensing result remains useful for the task agent, while all participants' data privacy is guaranteed. A natural objective here is then to maximize the profit of the task agent, i.e., the difference between its utility and the total reward to the participants. To this end, the platform utilizes a random-sampling based mechanism for the 'privacy' auction, followed by a Laplace mechanism for data aggregation. We show that this auction mechanism design is 4-competitive, and further it exhibits desirable properties, including individual rationality, truthfulness, computational efficiency. Simulation results corroborate the theoretical properties of the proposed privacy-preserving crowdsensing scheme.

We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum.

The transmission of simultaneous and latency-sensitive data puts forth a significant challenge for the smart grid communications. In this paper, we investigate the application of device-to-device (D2D) communications for the energy management in the electric distribution network. Specifically, we develop a D2D-assisted relaying framework to exploit the spatial diversity and the differentiated data rate requirements, which improves the spectral efficiency, especially for the scenarios that there are faults in the electric distribution network. We study the data transmission scheduling problem under the proposed D2D-assisted relaying framework, aiming to minimize the overall information loss rate, while taking into account the uncertainties in the communication latency. To this end, we first cast the data transmission scheduling problem as a two-stage stochastic programming problem and derive the solution. Then, we develop a real-time distributed data transmission scheduling scheme based on the sample path realizations. Extensive simulation results show significant performance improvement by using the proposed D2D-assisted relaying framework compared with two baseline frameworks for a variety of different cases.

Multicast transmission based on real-time network state information is a resource-friendly technique to improve the energy efficiency and reduce the traffic burden for cellular systems. This paper evaluates the effectiveness of this technique for downlink transmissions. In particular, a scenario is considered in which multiple mobile users (MUs) asynchronously request to download one common message locally cached at a base station (BS). Due to the randomness of both the channel conditions and the request arrivals from the MUs, the BS may choose to intelligently hold the arrived requests, especially when the channel conditions are bad or the number of requests is small, and then serve them in one shot later via multicasting. Clearly it is of great interest to balance the delay (incurred by holding the requests) and the energy efficiency (EE, defined as the energy cost per request), and this motivates us to quantify the fundamental tradeoff for the proposed “hold-then-serve” scheme. For the scenario with single channel and unit message sizes, it is shown that for a fixed channel bandwidth, the delay-EE tradeoff reduces to judiciously choosing the optimal stopping rule for when to serve all the arrived requests, where the effect of the bandwidth on the achievable delay-EE region is discussed further. By using optimal stopping theory, it is shown that the optimal stopping rule exists for general Markov channel models and request arrival processes. Particularly, for the hard deadline and proportional delay penalty cases, it is shown that the optimal stopping rule exhibits a threshold structure, and the corresponding threshold in the former case is time varying while in the latter case it is a constant. Finally, for the more general scenario with multiple channels and arbitrary message sizes, the optimal scheduling is formulated as a Markov decision process problem, where some efficient suboptimal scheduling algorithms are proposed.

This paper investigates the relation between three different notions of privacy: identifiability, differential privacy, and mutual-information privacy. Under a unified privacydistortion framework, where the distortion is defined to be the expected Hamming distance between the input and output databases, we establish some fundamental connections between these three privacy notions. Given a maximum allowable distortion D, we define the privacy-distortion functions ∈i* (D), ∈d*(D), and ∈m*(D) to be the smallest (most private/best) identifiability level, differential privacy level, and mutual information between the input and the output, respectively. We characterize ∈i* (D) and ∈d*(D), and prove that ∈i* (D) - ∈X≤ ∈d*(D) ≤ ∈i* (D) for D within certain range, where ∈Xis a constant determined by the prior distribution of the original database X, and diminishes to zero when X is uniformly distributed. Furthermore, we show that ∈i* (D) and ∈m*(D) can be achieved by the same mechanism for D within certain range, i.e., there is a mechanism that simultaneously minimizes the identifiability level and achieves the best mutual-information privacy. Based on these two connections, we prove that this mutual-information optimal mechanism satisfies ∈-differential privacy with ∈d*(D) ≤ ∈ ≤ ∈d*(D)+2∈X. The results in this paper reveal some consistency between two worst case notions of privacy, namely, identifiability and differential privacy, and an average notion of privacy, mutual-information privacy.

To meet the explosive demand on delivering high-definition video steams over cellular networks, we design a Social-aware video multiCast (SoCast) system leveraging device-to-device (D2D) communications. One salient feature of SoCast is to stimulate effective cooperation among mobile users (clients), by making use of two types of important social ties, i.e., social trust and social reciprocity. By using SoCast, clients form groups to obtain missing packets from other clients and restore incomplete video frames, according to the unique video encoding structure. In return, the user perception of the mobile video quality can be substantially improved. Specifically, we first cast the problem of social ties based group formation among clients for cooperative video multicast as a coalitional game, and then devise a distributed algorithm to obtain the core solution (group formation) for the formulated coalitional game. Further, a resource allocation scheme is proposed for the base station to handle D2D radio resource requests from client groups. Extensive numerical studies using real video traces corroborate the significant gain using SoCast.

We develop a social group utility maximization (SGUM) framework for cooperative wireless networking that takes into account both social relationships and physical coupling among users. Specifically, instead of maximizing its individual utility or the overall network utility, each user aims to maximize its social group utility that hinges heavily on its social tie structure with other users. We show that this framework provides rich modeling flexibility and spans the continuum between non-cooperative game and network utility maximization (NUM)-two traditionally disjoint paradigms for network optimization. Based on this framework, we study three important applications of SGUM, in database assisted spectrum access, power control, and random access control, respectively. For the case of database assisted spectrum access, we show that the SGUM game is a potential game and always admits a socially-aware Nash equilibrium (SNE). We also develop a distributed spectrum access algorithm that can converge to the SNE and also quantify the trade-off between the performance and convergence time of the algorithm. For the cases of power control and random access control, we show that there exists a unique SNE and the network performance improves as the strength of social ties increase. Numerical results corroborate that the SGUM solutions can achieve superior performance using real social data trace. Furthermore, we show that the SGUM framework can be generalized to take into account both positive and negative social ties among users, which can be a useful tool for studying network security problems.

By taking advantage of active sensing using radio waves, radar sensors can offer several advantages over passive sensors. Although much attention has been given to multistatic and multiple-input-multiple-output (MIMO) radar concepts, little has been paid to understanding radar networks (i.e., multiple individual radars working in concert). In this context, we study the coverage problem of a bistatic radar (BR) sensor network, which is very challenging due to the Cassini oval sensing region of a BR and the coupling of sensing regions across different BRs. In particular, we consider the problem of deploying a network of BRs in a region to maximize the worst-case intrusion detectability, which amounts to minimizing the vulnerability of a barrier. We show that it is optimal to place BRs on the shortest barrier if it is the shortest line segment that connects the left and right boundary of the region. Based on this, we study the optimal placement of BRs on a line segment to minimize its vulnerability, which is a nonconvex optimization problem. By exploiting certain specific structural properties pertaining to the problem (particularly an important structure of detectability), we characterize the optimal placement order and the optimal placement spacing of the BR nodes, both of which present elegant balanced structures. Our findings provide valuable insights into the placement of BRs for barrier coverage. To our best knowledge, this is the first work to explore the barrier coverage of a network of BRs.

This paper considers a heterogeneous ad hoc network with multiple transmitter-receiver pairs, in which all transmitters are capable of harvesting renewable energy from the environment and compete for one shared channel by random access. In particular, we focus on two different scenarios: the constant energy harvesting (EH) rate model where the EH rate remains constant within the time of interest and the i.i.d. EH rate model where the EH rates are independent and identically distributed across different contention slots. To quantify the roles of both the energy state information (ESI) and the channel state information (CSI), a distributed opportunistic scheduling (DOS) framework with two-stage probing and save-then-transmit energy utilization is proposed. Then, the optimal throughput and the optimal scheduling strategy are obtained via one-dimension search, i.e., an iterative algorithm consisting of the following two steps in each iteration: First, assuming that the stored energy level at each transmitter is stationary with a given distribution, the expected throughput maximization problem is formulated as an optimal stopping problem, whose solution is proven to exist and then derived for both models; second, for a fixed stopping rule, the energy level at each transmitter is shown to be stationary and an efficient iterative algorithm is proposed to compute its steady-state distribution. Finally, we validate our analysis by numerical results and quantify the throughput gain compared with the best-effort delivery scheme.

Cyber-Physical Systems (CPS) are emerging as the underpinning technology for major industries in this century. Wide-area monitoring and control is an essential ingredient of CPS to ensure reliability and security. Traditionally, a hierarchical system has been used to monitor and control remote devices deployed in a large geographical region. However, a general consensus is that such a hierarchical system can be highly vulnerable to component (i.e., nodes and links) failures, calling for a robust and cost-effective communication system for CPS. To this end, we consider a middleware approach to leverage the existing commercial communication infrastructure (e.g., Internet and cellular networks) with abundant connectivity. In this approach, a natural question is how to use the middleware to cohesively “glue” the physical system and the commercial communication infrastructure together, in order to enhance robustness and cost-effectiveness. We tackle this problem while taking into consideration two different cases of middleware deployment: single-stage and multi-stage deployments. We design offline and online algorithms for these two cases, respectively. We show that the offline algorithm achieves the best possible approximation ratio while the online algorithm attains the order-optimal competitive ratio. We also demonstrate the performance of our proposed algorithms through simulations.

We study data fusion schemes for early detection of anomalies in an interconnected power grid and communication network, where power nodes rely on the real-time control via communication nodes, which in turn, depend on the former for power supply. Based on a key observation that failures are spatially correlated and propagate through neighboring nodes, we propose a data fusion scheme, which "scans" an anomalous cluster, i.e., a connected component of nodes, in each individual network. We show that the proposed scheme can detect weaker signals of anomalies, compared to baseline approaches, and further its detection capability increases with the size of the anomalous cluster. This finding leads us to further exploit the interdependent structure of failures across two networks and design a more powerful data fusion scheme, which jointly detects an anomalous cluster over the two interconnected networks. To analyze the detection gain of the joint data fusion scheme, we first characterize how quickly the anomalous behavior propagates, in both the power grid and the power- communication network, based on random graph and epidemic models. We then present numerical results to quantify the detection gain of the joint data fusion scheme.

In this paper, we study a privacy-preserving spectrum sharing system to protect secondary users' location privacy while enhancing spectrum access. The location privacy of secondary users can be compromised by an external adversary via the received signal strength (RSS)-based localization technique. To mitigate such privacy threat, we employ a random power perturbation approach that allows each secondary user to judiciously obfuscate the RSS captured by the adversary. While it can protect users' location privacy, the power perturbation approach would inevitably degrade the system performance and bring challenges to the design of the spectrum allocation algorithm. In this work, we adopt a socially-aware database assisted spectrum access system and cast the spectrum allocation under users' power perturbation as a stochastic channel selection game played among the users. To tackle the challenge brought by the privacy protection, we develop a two time-scale distributed learning algorithm, which is shown to converge almost surely to a socially-aware ε-Nash equilibrium. The numerical results show that the higher the privacy protection level is, the more significant the degradation of the network throughput would be.

There has been a significant body of work on evaluating node criticality in information networks. However, most of the existing works are developed for static networks and are not applicable to dynamic settings where connectivities among nodes change frequently over time. In this paper, we treat an opportunistic mobile social network as a time-evolving, dynamic graph, and propose a scheme to ascertain the information dissemination capability for each node based on its contact history. In particular, we analyze the node importance in spreading or forwarding real-time messages which are assumed to become less important or even stale over time. To this end, we take a dynamic walk counting approach to calculate all possible temporal-spatial routes associated with each node, by using the down-weighting method. Since the age of a message increases with time, the old walks are discounted to represent the fading influence on the target node. Extensive experiments are conducted based on 4 real-world trace datasets, and the results show that, our analytical result is effective at ranking the node criticality in disseminating or acquiring real-time messages in opportunistic mobile social networks.

With increasing popularity of location-based services (LBSs), there have been growing concerns for location privacy. To protect location privacy in a LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this study, we leverage the social tie structure among mobile users to motivate them to participate in pseudonym change. Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to change pseudonyms as a socially-aware pseudonym change game (PCG). The PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SGUM-based PCG, we show that there exists a socially-aware Nash equilibrium (SNE), and quantify the system efficiency of the SNE with respect to the optimal social welfare. Then we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. It turns out that this algorithm can efficiently find a Pareto-optimal SNE with social welfare higher than that for the socially-oblivious PCG, pointing out the impact of exploiting social tie structure. We further show that the Pareto-optimal SNE can be achieved in a distributed manner.

While most of existing efforts for dynamic spectrum access have focused on spectrum sensing of a narrowband band in a given region, this paper takes a holistic perspective to determine the usage profile of wide spectrum bands over a large geographic region. Specifically, a mobile crowdsensing approach is taken to develop a spectrum-profiling framework, which leverages the wisdom of many mobile devices to accomplish large-scale sensing tasks. A key step for spectrum profiling via mobile crowdsensing is to strategically assign sensing tasks to mobile users, so as to maximize the utility of the sensing data acquired. We cast this problem as a joint sensing task and subband allocation problem for utility maximization, capturing the location-specific characteristics of spectrum sensing. Since the problem is NP-hard, we design approximation algorithms. First, we design a greedy approximation algorithm as a baseline. Our analysis shows that the proposed greedy algorithm achieves an approximation ratio of 1/6, i.e., at least 1/6 of the utility obtained by the optimal allocation. Next, we design a Linear Program (LP) rounding based approximation algorithm, aiming to achieve a better approximation ratio than the greedy algorithm. We show that the propopsed LP-rounding algorithm attains an approximation ratio of 1/2 (1 - 1/e) for the general case, and further it achieves 1 - 1/e for a special case of the problem, which is the best possible approximation ratio. We also present the complexity analysis of the two proposed algorithms. We perform numerical experiments to evaluate the average performance of the the proposed algorithms.

Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of mobile social networks, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device (D2D) communications. Specifically, as handheld devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game-theoretic framework to devise social-tie-based cooperation strategies for D2D communications. We also develop a network-assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, truthful, and computationally efficient. We evaluate the performance of the mechanism by using real social data traces. Simulation results corroborate that the proposed mechanism can achieve significant performance gain over the case without D2D cooperation.

We study deadline-aware scheduling with adaptive network coding (NC) for real-time traffic over a single-hop wireless network. To meet hard deadlines of real-time traffic, the block size for NC is adapted based on the remaining time to the deadline so as to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. This sequential block size adaptation problem is then cast as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and that the optimal block size is bounded by the “greedy” block size. These unique structures make it possible to significantly narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can find the optimal block size in polynomial time. Furthermore, a joint real-time scheduling and channel learning scheme with adaptive NC is developed to adapt to channel dynamics in a mobile network environment. Then, we generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints. We devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical utility optimality. The analysis and simulation results are corroborated by high-fidelity wireless emulation tests, where actual radio transmissions over emulated channels are performed to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.

A fundamental challenge in wireless networks is how to handle packet loss due to noise, interference, and dynamic channel effects, especially when there is no per-packet acknowledgement due to additional delay and potential loss of feedback packets. We design and implement a packet coding optimization scheme, applied at the source node, to enhance end-to-end transmission reliability in a lossy multi-hop network. Specifically, each source node transmits a file of packets to its destination node in the network where the end-to-end packet acknowledgement is not readily available because of the possible error or delay effects over multiple hops. By simply retransmitting packets, a source node cannot guarantee innovative packet arrivals at the destination node. Thus, we design an optimization scheme for adaptive packet coding applied at the source node to avoid transmitting redundant packets, thereby significantly improving the throughput, even for the case of a single unicast session. This scheme does not require any knowledge of network topology and can seamlessly operate with any routing or network coding protocol used at the intermediate relay nodes. We analyze the throughput properties of coded transmissions and verify the feasibility of performance gains via simulations. Then, we provide high fidelity emulation testbed results with real radio transmissions over emulated channels to evaluate the throughput gains. Without relying on end-to-end acknowledgment for each packet, we show that the adaptive packet coding optimization scheme can achieve significantly higher throughput than the retransmission scheme in lossy networks with unicast traffic.

Cooperative networking is a promising technology to meet the rapidly growing demand of mobile data traffic. To stimulate effective and trustworthy user cooperation, we leverage the knowledge of the social tie structure among mobile users and develop a social trust based cooperative D2D relaying framework, which takes into account both physical distances and social distances among users. Based on (finite-horizon) optimal stopping theory, we derive the optimal social aware relay selection strategy, which strikes a balance between performance gain and relay probing cost. We further show that the optimal stopping policy for social aware relay selection exhibits a stage-dependent threshold structure that has a monotonically non-increasing property. Numerical results demonstrate that the proposed mechanism can yield significant throughput gain over the direct transmission scheme.

In this paper, we propose SoCast — a cooperative video multicast framework to stimulate effective cooperation among mobile users (clients), by leveraging two types of important social ties, i.e., social trust and social reciprocity. By using SoCast, clients can form groups to restore incomplete video frames by obtaining missing packets from other clients, according to the unique video encoding structure. In return, the user perception video quality of mobile video multicast can be improved. Specifically, we first cast the problem of social ties based group formation among clients as a coalitional game, and then devise a distributed algorithm to obtain the core solution (group formation) for the formulated coalitional game. Further, a resource allocation mechanism is proposed for the base station to handle radio resource requests from client groups. Extensive numerical studies with real video traces corroborate the significant performance gain by using the SoCast.

In this paper, we develop a social group utility maximization (SGUM) framework for cooperative networking that takes into account both social relationships and physical coupling among users. Specifically, instead of maximizing its individual utility or the overall network utility, each user aims to maximize its social group utility that hinges heavily on its social ties with other users. We show that this framework provides rich modeling flexibility and spans the continuum space between non-cooperative game and network utility maximization (NUM) - two traditionally disjoint paradigms for network optimization. Based on this framework, we study an important application in database assisted spectrum access. We formulate the distributed spectrum access problem among white-space users with social ties as a SGUM game. We show that the game is a potential game and always admits a social-aware Nash equilibrium. We also design a distributed spectrum access algorithm that can achieve the social-aware Nash equilibrium of the game and quantify its performance gap. We evaluate the performance of the SGUM solution using real social data traces. Numerical results demonstrate that the performance gap between the SGUM solution and the NUM (social welfare optimal) solution is at most 15%.

Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications. In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets. We show that the allocation problem is NP hard. We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives. We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (i.e., the number of mobile users who intend to perform the task). Extensive simulation results are provided to demonstrate the advantages of our proposed scheme.

This paper considers an ad hoc network with multiple transmitter-receiver pairs, in which all transmitters are capable of harvesting renewable energy from the environment and compete for the same channel by random access. To quantify the roles of both the energy state information (ESI) and the channel state information (CSI), a distributed opportunistic scheduling (DOS) framework with a save-then-transmit scheme is proposed. First, in the channel probing stage, each transmitter probes the CSI via channel contention; next, in the data transmission stage, the successful transmitter decides to either give up the channel (if the expected reward calculated over the CSI and ESI is small) or hold and utilize the channel by optimally exploring the energy harvesting and data transmission tradeoff. With a constant energy arrival model, i.e., the energy harvesting rate keeps identical over the time of interest, the expected throughput maximization problem is formulated as an optimal stopping problem, whose solution is shown to exist and have a threshold-based structure, for both the homogeneous and heterogenous cases. Furthermore, we prove that there exists a steady-state distribution for the stored energy level at each transmitter, and propose an efficient iterative algorithm for its computation. Finally, we show via numerical results that the proposed scheme can achieve a potential 175% throughput gain compared with the method of best-effort delivery.

The massive scale of online social networks makes it very challenging to characterize the underlying structure therein. In this paper, we employ the t-cherry junction tree, a very recent advancement in probabilistic graphical models, to develop a compact representation and good approximation of an otherwise intractable model for users' relationships in a social network. There are a number of advantages in this approach: (1) the best approximation possible via junction trees belongs to the class of t-cherry junction trees; (2) constructing a t-cherry junction tree can be largely parallelized; and (3) inference can be performed using distributed computation. To improve the quality of approximation, we also devise an algorithm to build a higher order tree gracefully from an existing one, without constructing it from scratch. We apply this approach to Twitter data containing 100,000 nodes and study the problem of recommending connections to new users.

Wide-area monitoring, protection and control (WAMPAC) plays a critical role in smart grid, for protection against possible contingencies, by using the Supervisory Control and Data Acquisition (SCADA) system. However, a general consensus is that such a hierarchical system can be highly vulnerable to component (i.e., nodes and links) failures, calling for a robust and cost-effective communication system for smart grid. To this end, we consider a middleware approach to leverage the existing commercial communication infrastructure with abundant connectivity. In this approach, a natural question is how to use the middleware to cohesively “glue” the power grid and the commercial communication infrastructure together, in order to enhance robustness and cost-effectiveness. We tackle this problem while taking into consideration the multi-stage deployment of power devices and their redundant connections. We show that this problem can be cast as a minimum-cost middleware design under incremental deployment — an “online” problem where the input is provided gradually due to the incremental deployment. We design a randomized “online” algorithm, and show that it achieves the order-optimal average competitive ratio. Simulation results demonstrate the performance of our proposed algorithm, compared to the optimal offline solution.

This paper studies secret key establishment between two adjacent mobile nodes, which is crucial for securing emerging device-to-device (D2D) communication. As a promising method, cooperative key generation allows two mobile nodes to select some common neighbors as relays and directly extract a secret key from the wireless channels among them. A challenging issue that has been overlooked is that mobile nodes are often self-interested and reluctant to act as relays without adequate reward in return. We propose SYNERGY, a game-theoretical approach for stimulating cooperative key generation. The underlying idea of SYNERGY is to partition a group of mobile nodes into disjoint coalitions such that the nodes in each coalition fully collaborate on cooperative key generation. We formulate the group partitioning as a coalitional game and design centralized and also distributed protocols for obtaining the core solution to the game. The performance of SYNERGY is evaluated by extensive simulations.

Due to high mobility and frequent disconnections in a vehicular network, reliable and efficient vehicular communication is very challenging. Previous studies focus on predicting the trajectories of single vehicles. Due to many random factors, however, there is little regularity in the movements of a single vehicle in an urban area, and this motivates us to take a holistic network perspective. With this insight, we model the time varying regularities of road traffic flows in road segments and intersections by mining statistic trajectories of all vehicles in the network. Based on these regularities and local real-time traffic information, we propose a new method to calculate the expected transfer delay from a current position to a given destination. We also propose a method to collect updated destination information. By combining the above two methods, we design a routing algorithm for vehicle-to-vehicle data transmission in vehicular networks, and then prove that it is a linear-time algorithm. Finally, we evaluate our algorithm by using information of real taxi vehicles. The results show that the performance of our algorithm is significantly better than other solutions in terms of packet delay.

Smart meters, designed for information collection and system monitoring in smart grid, report fine-grained power consumption to utility providers. With these highly accurate profiles of energy usage, however, it is possible to identify consumers' specific activity or behavior patterns, thereby giving rise to serious privacy concerns. In this paper, this concern is addressed by using battery energy storage. Beyond privacy protection, batteries can also be used to cut down the electricity bill. From a holistic perspective, a dynamic optimization framework is designed for consumers to strike a tradeoff between the smart meter data privacy and the electricity bill. In general, a major challenge in solving dynamic optimization problems lies in the need of the knowledge of the future electricity consumption events. By exploring the underlying structure of the original problem, an equivalent problem is derived, which can be solved by using only the current observations. An online control algorithm is then developed to solve the equivalent problem based on the Lyapunov optimization technique. To overcome the difficulty of solving a mixed-integer nonlinear program involved in the online control algorithm, the problem is further decomposed into multiple cases and the closed-form solution to each case is derived accordingly. It is shown that the proposed online control algorithm can optimally control the battery operations to protect the smart meter data privacy and cut down the electricity bill, without the knowledge of the statistics of the time-varying load requirement and the electricity price processes. The efficacy of the proposed algorithm is demonstrated through extensive numerical evaluations using real data.

Link adaptation technologies, such as Adaptive Modulation and Coding (AMC) and Multiple-Input-Multiple-Output (MIMO), are used in advanced wireless communication systems to achieve high spectrum efficiency. Communication performance can be improved significantly by adaptive transmissions based on the quality of received signals, i.e., the signal-to-interference-plus-noise ratio (SINR). However, for multihop wireless communications, most link scheduling schemes have been developed under simplified interference models that do not account for accumulative interference and cannot fully exploit the recent advances in PHY-layer communication theory. This paper focuses on developing link scheduling schemes that can achieve optimal performance under the SINR model. One key idea is to treat an adaptive wireless link as multiple parallel virtual links with different signal quality, building on which we develop throughput-optimal scheduling schemes using a two-stage queueing structure in conjunction with recently developed carrier-sensing techniques. Furthermore, we introduce a novel three-way handshake to ensure, in a distributed manner, that all transmitting links satisfy their SINR requirements. We evaluate the proposed schemes through rigorous analysis and simulations.

This paper studies deterministic sensor deployment for barrier coverage in wireless sensor networks. Most of existing works focused on line-based deployment, ignoring a wide spectrum of potential curve-based solutions. We, for the first time, extensively study the sensor deployment under a general setting. We first present a condition under which the line-based deployment is suboptimal, revealing the advantage of curve-based deployment. By constructing a contracting mapping, we identify the characteristics for a deployment curve to be optimal. Based on the optimal deployment curve, we design sensor deployment algorithms by introducing a new notion of distance-continuous. Our findings show that i) when the deployment curve is distance-continuous, the proposed algorithm is optimal in terms of the vulnerability corresponding to the deployment, and ii) when the deployment curve is not distance-continuous, the approximation ratio of the vulnerability corresponding to the deployment by the proposed algorithm to the optimal one is upper bounded by min (π, ||ÃB̃||/||ÃG̃B̃|| 2n+√2-1/2n ), where ||ÃB̃|| and ||ÃG̃B̃|| are some constants, and n is the number of sensors. We generalize the study to the heterogeneous sensing model, and show that the proposed algorithm can provide close-to-optimal performance. Extensive numerical results corroborate our analysis.

This paper considers the use of energy harvesters for cooperative relaying in a large relay network, which consists of N energy-harvesting (EH) relays and one source-destination pair. In particular, a threshold-based “save-then-transmit” scheme is employed at the relays, where each relay transmits only when both the backward and forward link channel coefficients are above certain thresholds. We assume that the time scale of EH is much larger than that of communication blocks. For general channel fading models, we derive the asymptotic average throughput for the case with many relays, by using the amplify-and-forward (AF) relaying scheme. The throughput maximization is cast as a joint optimization problem over the transmission thresholds corresponding to all possible harvested energy rate states, which is shown to be non-convex in general. By applying a convexification technique via randomization, the original problem is transformed into a new formulation with a generalized threshold-based transmission scheme, which is shown to be efficiently solvable by bisection search, with the help of an offline look-up table only related to the channel statistics. Finally, with some numerical experiments, we demonstrate the performance gain of the proposed threshold-based transmission scheme against some suboptimal ones.

Radar sensors, which actively transmit radio waves and collect RF energy scattered by objects in the environment, offer a number of advantages over purely passive sensors. An important issue in radar is that the transmitted energy may be scattered by objects that are not of interest as well as objects of interest (e.g., targets). The detection performance of radar systems is affected by such clutter as well as noise. Further, in many applications, clutter can be substantially stronger than the signals of interest. To combat the effect of clutter, a popular method is to take advantage of the Doppler frequency shift (DFS) extracted from the echo signal due to the relative motion of a target with respect to the radar. Unfortunately, a sensor coverage model that only depends on the distance to a target would fail to capture the DFS. In this paper, we set forth the concept of Doppler coverage for a network of spatially distributed radars. Specifically, a target is said to be Doppler-covered if, regardless of its direction of motion, there exists some radar in the network whose signalto-noise ratio (SNR) is sufficiently high and the DFS at that radar is sufficiently large. Based on the Doppler coverage model, we first propose an efficient method to characterize Dopplercovered regions for arbitrarily deployed radars. Then we design an algorithm for deriving the minimum radar density required to achieve Doppler coverage in a region under any polygonal deployment pattern, and further apply it to investigate the regular triangle based deployment.

This paper studies deterministic sensor deployment to ensure barrier coverage in wireless sensor networks. Most of existing work focused on line-based deployment, ignoring a wide spectrum of potential curve-based solutions. We, for the first time, extensively study the sensor deployment under general settings. We first present a condition under which line-based deployment is suboptimal, pointing to the advantage of curve-based deployment. By constructing a contracting mapping, we identify the characteristics for a deployment curve to be optimal. We then design sensor deployment algorithms for the optimal deployment curve by introducing a new notion of distance-continuous. Our findings show that i) when the deployment curve is distance-continuous, the proposed algorithm is optimal in terms of the vulnerability corresponding to the deployment, and ii) when the deployment curve is not distance-continuous, the approximation ratio of the vulnerability corresponding to the deployment by the proposed algorithm to the optimal one is upper bounded by min (π, ||AB||/||AGB|| 2n+√(2-1)/2n), where ||AB||, ||AGB|| and n are constants. Extensive numerical results corroborate our analysis.

Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of the interplay between social structure and mobile communications, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device communications. Specifically, as hand-held devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game theoretic framework to devise social-tie based cooperation strategies for device-to-device communications. We also develop a network assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, and truthful. We evaluate the performance of the mechanism by using real social data traces. Numerical results show that the proposed mechanism can achieve up-to 122% performance gain over the case without D2D cooperation.

By taking advantage of active sensing using radio waves, radar sensors can offer several advantages over passive sensors. Although much recent attention has been given to multistatic and MIMO radar concepts, little has been paid to understanding the performance of radar networks (i.e., multiple individual radars working in concert). In this context, we study the optimal placement of a bistatic radar (BR) sensor network for barrier coverage. The coverage problem in a bistatic radar network (BRN) is challenging because: 1) in contrast to the disk sensing model of a traditional passive sensor, the sensing region of a BR depends on the locations of both the BR transmitter and receiver, and is characterized by a Cassini oval; 2) since a BR transmitter (or receiver) can potentially form multiple BRs with different BR transmitters (or receivers, respectively), the sensing regions of different BRs are coupled, making the coverage of a BRN highly non-trivial. This paper considers the problem of deploying a network of BRs in a region for maximizing the worst-case intrusion detectability, which amounts to minimizing the vulnerability of a barrier. We show that the shortest barrier-based placement is optimal if the shortest barrier is also the shortest line segment connecting the region's two boundaries. Based on this observation, we study the optimal placement of the BRs on a line segment for minimizing its vulnerability, which is a non-convex optimization problem. By exploiting some specific structural properties pertaining to the problem (particularly an important structure of detectability), we find the optimal placement order and the optimal placement spacing of the BR nodes, both of which exhibit elegant balanced structures. Our findings give valuable insight for the placement of BRs for barrier coverage. To our best knowledge, this is the first work to explore the coverage of a network of BRs.

The virtual multiple input multiple output (MIMO) technique can dramatically improve the performance of a multi-cell distributed antenna system (DAS), thanks to its great potentials for inter-cell interference mitigation. One of the most challenging issues for virtual MIMO is the acquisition of channel state information at the transmitter (CSIT), which usually leads to an overwhelming amount of system overhead. In this work, we focus on the case that only the slowly-varying large-scale channel state is required at the transmitter, and explore the performance gain that can be achieved by coordinated transmissions for virtual MIMO with large-scale CSIT. Aiming at maximizing the achievable ergodic sum rate, the input covariances for all the mobile terminals (MTs) are jointly optimized, which turns out to be a complicated non-convex problem with a non-closed-form objective function. Further analysis reveals that the coordinated transmission problem can be recast as a Max-Min problem with a closed-form objective function and linear constraints. Then, by appealing to the successive approximation method and the saddle-point theory of concave-convex functions, we propose an iterative algorithm for coordinated transmissions with large-scale CSIT and establish its convergence. Simulation results corroborate that the proposed scheme converges quickly, and it yields significant performance gains compared to the existing schemes. Moreover, it is observed that the proposed scheme can achieve a nearly globally-optimal point under the diagonal input covariance constraint. Since the acquisition of large-scale CSIT is far less demanding than that of full CSIT, we believe that the proposed coordinated transmissions with large-scale CSIT in DASs shed some light on virtual MIMO in the making.

Cognitive radio can significantly improve the spectrum utilization by enabling secondary users (SUs) to opportunistically access the spectrum licensed to primary users (PUs). One practical yet challenging scenario is when both the PU occupancy and the channel fading change over time and exhibit temporal correlations. Little work has been done for simultaneously exploiting the temporal memory in both channel fading and PU occupancy for spectrum scheduling, and in particular, the scenario where PU occupancy presents a long temporal memory has been underexplored. In this work, we consider a cognitive radio network with multiple PUs and one SU, where a spectrum server is employed for scheduling the SU to transmit over one of the PU channels opportunistically. A primary goal is to understand the tradeoffs that arise from the intricate interactions between channel fading and PU occupancy, and the impact of the associated temporal memory. By casting the problem as a partially observable Markov decision process, we identify and illustrate a set of multi-tier tradeoffs that go beyond the classic "exploitation vs. exploration" tradeoff. We show that a simple greedy policy is optimal in some special cases. To build a deeper understanding of the tradeoffs, we further introduce a full-observation genie-aided system that helps in decomposing the tradeoffs in the original system into multiple layers, which we examine progressively. Numerical examples indicate that the optimal scheduler in the original system, with observation on the scheduled channel only, achieves a performance very close to the genie-aided system. In addition, the optimal policy in the original system significantly outperforms randomized scheduling, as well as a policy that explores memory in PU occupancy only, pointing to the advantages of jointly exploiting the temporal correlation structure in both channel fading and PU occupancy.

We study the diffusion of information in an overlaying social-physical network. Specifically, we consider the following set-up: There is a physical information network where information spreads amongst people through conventional communication media (e.g., face-to-face communication, phone calls), and conjoint to this physical network, there are online social networks where information spreads via web sites such as Facebook, Twitter, FriendFeed, YouTube, etc. We quantify the size and the critical threshold of information epidemics in this conjoint social-physical network by assuming that information diffuses according to the SIR epidemic model. One interesting finding is that even if there is no percolation in the individual networks, percolation (i.e., information epidemics) can take place in the conjoint social-physical network. We also show, both analytically and experimentally, that the fraction of individuals who receive an item of information (started from an arbitrary node) is significantly larger in the conjoint social-physical network case, as compared to the case where the networks are disjoint. These findings reveal that conjoining the physical network with online social networks can have a dramatic impact on the speed and scale of information diffusion.

In this paper, we study distributed scheduling in multihop multiple-input–multiple-output (MIMO) networks. We first develop a “MIMO-pipe” model that provides the upper layers a set of rates and signal-to-interference-plus-noise ratio (SINR) requirements that capture the rate–reliability tradeoff in MIMO communications. The main thrust of this paper is then dedicated to developing distributed carrier sense multiple access (CSMA) algorithms for MIMO-pipe scheduling under the SINR interference model. We choose the SINR model over the extensively studied protocol-based interference models because it more naturally captures the impact of interference in wireless networks. The coupling among the links caused by the interference under the SINR model makes the problem of devising distributed scheduling algorithms very challenging. To that end, we explore the CSMA algorithms for MIMO-pipe scheduling from two perspectives. We start with an idealized continuous-time CSMA network, where control messages can be exchanged in a collision-free manner, and devise a CSMA-based link scheduling algorithm that can achieve throughput optimality under the SINR model. Next, we consider a discrete-time CSMA network, where the message exchanges suffer from collisions. For this more challenging case, we develop a “conservative” scheduling algorithm by imposing a more stringent SINR constraint on the MIMO-pipe model. We show that the proposed conservative scheduling achieves an efficiency ratio bounded from below.

This paper investigates pricing-based spectrum access control in cognitive radio networks, where primary users (PUs) sell the temporarily unused spectrum and secondary users (SUs) compete via random access for such spectrum opportunities. Compared to existing market-based approaches with centralized scheduling, pricing-based spectrum management with random access provides a platform for SUs contending for spectrum access and is amenable to decentralized implementation due to its low complexity. We focus on two market models, one with a monopoly PU market and the other with a multiple-PU market. For the monopoly PU market model, we devise decentralized pricing-based spectrum access mechanisms that enable SUs to contend for channel usage. Specifically, we first consider SUs contending via slotted Aloha. Since the revenue maximization problem therein is nonconvex, we characterize the corresponding Pareto-optimal region and obtain a Pareto-optimal solution that maximizes the SUs' throughput subject to their budget constraints. To mitigate the spectrum underutilization due to the “price of contention,” we revisit the problem where SUs contend via CSMA, which results in more efficient spectrum utilization and higher revenue. We then study the tradeoff between the PU's utility and its revenue when the PU's salable spectrum is controllable. Next, for the multiple-PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We explore the existence and the uniqueness of Nash equilibrium, in terms of access prices and the spectrum offered to SUs, and develop an iterative algorithm for strategy adaptation to achieve the Nash equilibrium. Our findings reveal that there exists a unique Nash equilibrium when the number of PUs is less than a threshold determined by the budgets and elasticity of SUs.

We consider a bistatic radar network that consists of multiple separated radar transmitters and receivers, which are deployed to detect potential attacks at some points of interest (PoIs). To better defend these PoIs, the design of the bistatic radar network is investigated in two stages. First, we study the problem of optimally placing a number of radar transmitters and receivers in the sense of minimizing the maximum distance product between a PoI and its closest transmitter-receiver pair. For this problem, we propose a randomized Voronoi algorithm. Next, given the radars' locations, assuming that the transmitters use fixed and orthogonal frequencies to illuminate signals for interference avoidance, we study the problem of frequency selection for the receivers. Since an intelligent attacker can adaptively change the PoI to attack, the receivers should dynamically adapt their frequencies to cover different subsets of the PoIs. Accordingly, we model the dynamic interactions between the bistatic radar network and the attacker as a repeated security game. Based on their respective information, we propose two learning algorithms for each of them, respectively. We show that if both players follow the modified-regret-matching procedures, the empirical distributions of their actions converge to the set of correlated equilibria.

We study the diffusion behavior of real-time information. Typically, real-time information is valuable only for a limited time duration, and hence needs to be delivered before its “deadline.” Therefore, real-time information is much easier to spread among a group of people with frequent interactions than between isolated individuals. With this insight, we consider a social network which consists of many cliques and information can spread quickly within a clique. Furthermore, information can also be shared through online social networks, such as Facebook, twitter, Youtube, etc. We characterize the diffusion of real-time information by studying the phase transition behaviors. Capitalizing on the theory of inhomogeneous random networks, we show that the social network has a critical threshold above which information epidemics are very likely to happen. We also theoretically quantify the fractional size of individuals that finally receive the message. The numerical results indicate that real-time information could be much easier to propagate in a social network when large size cliques exist.

We consider a cognitive radio network with multiple primary users (PUs) and one secondary user (SU), where a spectrum server is utilized for scheduling the SU to transmit over one of the PU channels opportunistically. One practical yet challenging scenario is when both the PU occupancy and the channel fading vary over time and exhibit temporal correlations. Little work has been done for exploiting such temporal memory in the channel fading and the PU occupancy simultaneously for opportunistic spectrum scheduling. Further, the scenario where PU occupancy possesses a long temporal memory has been underexplored as well. By casting the problem as a partially observable Markov decision process, we aim to understand the intricate tradeoffs resulting from the interactions of the two sets of system states (i.e., channel fading and PU occupancy) and the impact of the associated temporal memory. We identify and illustrate a set of multi-tier tradeoffs that go beyond the classic “exploitation vs. exploration” tradeoff. For certain special cases, we establish the optimality of a simple greedy policy. To build a more comprehensive understanding, we then introduce a full-observation genie-aided system that helps in decomposing the tradeoffs in the original system into multiple layers, which we examine progressively. Numerical examples indicate that the optimal scheduler in the original system, with observation on the scheduled channel only, achieves a performance very close to the genie-aided system. In addition, the optimal policy in the original system significantly outperforms randomized scheduling, as well as a policy that explores memory in one system state only, pointing to the merit of jointly exploiting the temporal correlation structure in both channel fading and PU occupancy.

This paper studies risk-aware day-ahead scheduling and real-time dispatch for plug-in electric vehicles (EVs), aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. It turns out that the inclusion of the load mismatch risk in the objective function complicates the risk-aware day-ahead scheduling and indeed the optimization problem is nonconvex. A key step is to utilize the hidden convexity structure to recast it as a two-stage stochastic linear program, which can be solved by using the L-shaped method. Further, we develop a distributed risk-aware real-time dispatch algorithm, where the aggregator only needs to compute the shadow prices for each EV to optimize its own charging strategy in a distributed manner. We show, based on real data, that the proposed risk-aware day-ahead scheduling algorithm can reduce not only the overall charging cost, but also the peak demand of EV charging.

We consider a directed acyclic network with two source-sink pairs {s1, t1} and {s2, t2}. The source s1wishes to communicate a message X1to the sink t1and the source s2wishes to communicate two messages X2and X3to the sink t2, where Xi, i = 1,2,3, are independent random variables of unit rate. We give a simple characterization for linear solvability of such networks under the condition that the minimum cut from {s1, s2} to t2equals 3. We develop a region decomposition method for proving this result, which we believe can be an effective approach for non-multicast network coding problem.

This paper considers opportunistic cooperative networking (OCN) in wireless ad hoc networks, with a focus on characterizing the desired tradeoff between the probing cost for establishing cooperative relaying and hence higher throughput via opportunistic cooperative networking. Specifically, opportunistic cooperative networking is treated as an optimal stopping problem with two-levels of incomplete information. Cases with or without dedicated relays are considered, and the existence of the optimal strategies for both cases are established. Then, it is shown that for the case with dedicated relays, the optimal strategy exhibits a threshold structure, in which it is optimal to probe the dedicated relay when the signal-to-noise ratio (SNR) of the source-relay link exceeds some threshold. For the case without dedicated relays, under more restrictive conditions, the optimal strategy is also threshold-based, in the sense that it is optimal to probe potential relays when the SNR of the source-destination link lies between two thresholds. Furthermore, these strategies can be implemented in a distributed manner.

In wireless sensor networks, relay node placement has been proposed to improve energy efficiency. In this paper, we study two-tiered constrained relay node placement problems, where the relay nodes can be placed only at some prespecified candidate locations. To meet the connectivity requirement, we study the connected single-cover problem where each sensor node is covered by a base station or a relay node (to which the sensor node can transmit data), and the relay nodes form a connected network with the base stations. To meet the survivability requirement, we study the 2-connected double-cover problem where each sensor node is covered by two base stations or relay nodes, and the relay nodes form a 2-connected network with the base stations. We study these problems under the assumption that R \ge 2r > 0, where R and r are the communication ranges of the relay nodes and the sensor nodes, respectively. We investigate the corresponding computational complexities, and propose novel polynomial time approximation algorithms for these problems. Specifically, for the connected single-cover problem, our algorithms have {\cal O}(1)-approximation ratios. For the 2-connected double-cover problem, our algorithms have {\cal O}(1)-approximation ratios for practical settings and {\cal O}(\ln n)-approximation ratios for arbitrary settings. Experimental results show that the number of relay nodes used by our algorithms is no more than twice of that used in an optimal solution.

We consider a cyber-physical system consisting of two interacting networks, i.e., a cyber network overlaying a physical network. It is envisioned that these systems are more vulnerable to attacks since node failures in one network may result in (due to the interdependence) failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. The robustness of interdependent systems against this sort of catastrophic failure hinges heavily on the allocation of the (interconnecting) links that connect nodes in one network to nodes in the other network. In this paper, we characterize the optimum inter-link allocation strategy against random attacks in the case where the topology of each individual network is unknown. In particular, we analyze the “regular” allocation strategy that allots exactly the same number of bidirectional internetwork links to all nodes in the system. We show, both analytically and experimentally, that this strategy yields better performance (from a network resilience perspective) compared to all possible strategies, including strategies using random allocation, unidirectional interlinks, etc.

We consider a cognitive radio network where multiple secondary users (SUs) contend for spectrum usage, using random access, over available primary user (PU) channels. Our focus is on SUs' queueing delay performance, for which a systematic understanding is lacking. We take a fluid queue approximation approach to study the steady-state delay performance of SUs, for cases with a single PU channel and multiple PU channels. Using stochastic fluid models, we represent the queue dynamics as Poisson driven stochastic differential equations, and characterize the moments of the SUs' queue lengths accordingly. Since in practical systems, an SU would have no knowledge of other users' activities, its contention probability has to be set based on local information. With this observation, we develop adaptive algorithms to find the optimal contention probability that minimizes the mean queue lengths. Moreover, we study the impact of multiple channels and multiple interfaces on SUs' delay performance. As expected, the use of multiple channels and/or multiple interfaces leads to significant delay reduction. Finally, we consider packet generation control to meet the delay requirements for SUs, and develop randomized and queue-length-based control mechanisms accordingly.

This paper considers distributed opportunistic scheduling (DOS) with cooperative relaying in wireless ad hoc networks, with a focus on characterizing the desired tradeoff between the probing cost for establishing cooperative relaying and the higher throughput via opportunistic cooperative networking. Specifically, distributed scheduling and probing for cooperative relaying is treated as an optimal stopping problem with two levels of incomplete information. Cases with or without dedicated relays are considered, and the existence of the optimal strategies for both cases are established. Then, it is shown that for the case with dedicated relays, the optimal strategy exhibits a threshold structure, in which it is optimal to probe the dedicated relay when the signal-to-noise ratio (SNR) of the source-relay link exceeds some threshold. For the case without dedicated relays, under more restrictive conditions, the optimal strategy is also threshold-based, in the sense that it is optimal to probe potential relays when the SNR of the source-destination link lies between two thresholds. Furthermore, these strategies can be implemented in a distributed manner.

In this study, downlink scheduling of multiuser traffic with hard deadlines and packet-level priorities is cast as a partially observable Markov decision process. User channels are modeled as Markovian and the base station can learn only the channel condition of the currently scheduled user. The optimization of joint channel learning and scheduling presents the combined challenges incurred by the strict deadline constraint of real-time traffic and the partial observability of multiuser channels. In particular, we show that idling adds a new dimension to the action space; and that, through a case study of heterogeneous multiuser networks, idling is indeed the optimal action under certain system states. This somewhat surprising result reveals the existence of the fundamental tradeoffs between exploitation and exploration/idling, going beyond the classic `exploitation vs exploration'. We find that, due to hard deadlines and packet priorities, idling is intimately related to the tradeoff between the successful transmission of backlogged packets and that of future arrivals. In contrast, for the special case with a symmetric two-user system, we show that the scheduling problem exhibits unique structures, rendering a non-idling greedy policy optimal.

It is known that distributed power control in wireless ad-hoc networks is challenging, due to the inherent global coupling between concurrent transmissions interfering with each other. Observing that the globally optimal point lies on the boundary of the feasible region, we transform the utility maximization problem into a more structured problem in the form of maximizing the minimum weighted utility. Then, we develop a centralized algorithm for the minimum weighted utility maximization problem as a benchmark. Next, by using extended duality theory, we introduce penalty multipliers and decompose the minimum weighted utility maximization problem into subproblems for individual users. Appealing to the simulated annealing method, we propose a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the overall system utility. Although the underlying optimization problem is nonconvex, our algorithm can guarantee global optimality although the convergence rate may be slow due to the usage of simulated annealing. We improve the convergence rate further by devising an enhanced algorithm based on the geometric cooling schedule.

Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that the load and generation in the system remain balanced all the time. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and real-time scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in real-time scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve system-wide reliability. More specifically, when the opportunistic users are non-persistent, we obtain closed-form solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multi-timescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques.

This paper studies multicasting compressively sampled signals from a source to many receivers, over lossy wireless channels. Our focus is on the network outage from the perspective of signal distortion across all receivers, for both cases where the transmitter may or may not be capable of reconstructing the compressively sampled signals. Capitalizing on extreme value theory, we characterize the network outage in terms of key system parameters, including the erasure probability, the number of receivers and the sparse structure of the signal. We show that when the transmitter can reconstruct the compressively sensed signal, the strategy of using network coding to multicast the reconstructed signal coefficients can reduce the network outage significantly. We observe, however, that the traditional network coding could result in suboptimal performance with power-law decay signals. Thus motivated, we devise a new method, namely subblock network coding, which involves fragmenting the data into subblocks, and allocating time slots to different subblocks, based on its priority. We formulate the corresponding optimal allocation as an integer programming problem. Since integer programming is often intractable, we develop a heuristic algorithm that prioritizes the time slot allocation by exploiting the inherent priority structure of power-law decay signals. Numerical results show that the proposed schemes outperform the traditional methods with significant margins.

We consider a cognitive radio network where primary users (PUs) employ network coding for data transmissions. We view network coding as a spectrum shaper, in the sense that it increases spectrum availability to secondary users (SUs) and offers more structure of spectrum holes, which in turn improves the predictability of the primary spectrum. With this spectrum shaping effect of network coding, each SU can carry out adaptive channel sensing by dynamically updating the list of the (predicted) idle PU channels and giving priority to these channels for spectrum sensing. This dynamic spectrum access approach with network coding improves how SUs detect and utilize spectrum holes over PU channels. Our results show that compared to the existing approaches based on retransmission, both PUs and SUs can achieve higher stable throughput, thanks to the spectrum shaping effect of network coding.

Market-based mechanisms offer promising approaches for spectrum access in cognitive radio networks. In this paper, we focus on two market models, one with a monopoly primary user (PU) market and the other with a multiple PU market, where each PU sells its temporarily unused spectrum to secondary users (SUs). We propose a pricing-based spectrum trading mechanism that enables SUs to contend for channel usage by random access, in a distributed manner, which naturally mitigates the complexity and time overhead associated with centralized scheduling. For the monopoly PU market model, we first consider SUs contending via slotted Aloha. The revenue maximization problems here are nonconvex. We first characterize the Pareto optimal region, and then obtain a Pareto optimal solution that maximizes the SUs' throughput subject to the SUs' budget constraints. To mitigate the spectrum underutilization due to the “price of contention,” we revisit the problem where SUs contend via CSMA, and show that spectrum utilization is enhanced, resulting in higher revenue. When the PU's unused spectrum is a control parameter, we study further the tradeoff between the PU's utility and its revenue. For the multiple PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We characterize the Nash equilibria, in terms of access prices and the spectrum offered to SUs. Our findings reveal that the number of equilibria exhibits a phase transition phenomenon, in the sense that when the number of PUs is greater than a threshold, there exist infinitely many equilibria; otherwise, there exists a unique Nash equilibrium, where the access prices and spectrum opportunities are determined by the budgets/elasticity of SUs and the utility level of PUs.

A two-user interference channel is considered where each transmitter has access to a part of the information intended to the other destination. A primary objective is to maximize the information rates, by exploring the cooperation between the transmitters for interference mitigation, based on the partial side information. It is clear that full cooperation between the transmitters is not possible since each transmitter has only a part of the side information. With this insight, several “layered coding” schemes, consisting of binning and superposition at different stages, are developed. These schemes are are carefully built on coding strategies for the classical interference channel and node cooperation mechanisms. In particular, two layered coding schemes, which are based on a combination of MIMO broadcast coding and the Han-Kobayashi (HK) coding, are thoroughly studied : The first one, namely layered coding with binning, makes heavy use of the Gelfand-Pinsker binning and the HK coding and the second one, namely layered superposition coding, involves superposition coding over different tiers. Rate regions corresponding to the proposed schemes are derived. Then the application of these coding schemes are illustrated for the Gaussian case and numerical results corroborate that the proposed layered coding schemes yield substantial gains at high SNR.

We consider a two-user Gaussian interference channel with a relay. Specifically, two source-destination pairs and the relay share a single common channel, and the relay receives messages from both sources, and assists them in communicating the messages to their respective destinations. We propose two relaying schemes using "amplify-and-forward" (AF). For each of two modes considered, we characterize the achievable sum rate and present upper bounds as functions of relay locations.

This paper investigates pricing of Internet connectivity services in the context of a monopoly ISP selling broadband access to consumers. We first study the optimal combination of flat-rate and usage-based access price components for maximization of ISP revenue, subject to a capacity constraint on the data-rate demand. Next, we consider time-varying consumer utilities for broadband data rates that can result in uneven demand for data-rate over time. Practical considerations limit the viability of altering prices over time to smoothen out the demanded data-rate. Despite such constraints on pricing, our analysis reveals that the ISP can retain the revenue by setting a low usage fee and dropping packets of consumer demanded data that exceed capacity. Regulatory attention on ISP congestion management discourages such ``technical" practices and promotes economics based approaches. We characterize the loss in ISP revenue from an economics based approach. Regulatory requirements further impose limitations on price discrimination across consumers, and we derive the revenue loss to the ISP from such restrictions. We then develop partial recovery of revenue loss through non-linear pricing that does not explicitly discriminate across consumers. While determination of the access price is ultimately based on additional considerations beyond the scope of this paper, the analysis here can serve as a benchmark to structure access price in broadband access networks.

We study the problem of distributed scheduling in multi-hop MIMO networks. We first develop a ``MIMO-pipe" model that provides the upper layers a set of rates and SINR requirements, which capture the rate-reliability tradeoff in MIMO communications. The main thrust of this study is then dedicated to developing CSMA-based MIMO-pipe scheduling under the SINR model. We choose the SINR model over the extensively studied matching or protocol-based interference models because it more naturally captures the impact of interference in wireless networks. The coupling among the links caused by the interference makes the problem of devising distributed scheduling algorithms particularly challenging. To that end, we explore CSMA-based MIMO-pipe scheduling, from two perspectives. First, we consider an idealized continuous time CSMA network. We propose a dual-band approach in which control messages are exchanged instantaneously over a channel separate from the data channel, and show that CSMA-based scheduling can achieve throughput optimality under the SINR model. Next, we consider a discrete time CSMA network. To tackle the challenge due to the coupling caused by interference, we propose a ``conservative" scheduling algorithm in which more stringent SINR constraints are imposed based on the MIMO-pipe model. We show that this suboptimal distributed scheduling can achieve an efficiency ratio bounded from below.

With the convergence of multimedia applications and wireless communications, there is an urgent need for developing new scheduling algorithms to support real-time traffic with stringent delay requirements. However, distributed scheduling under delay constraints is not well understood and remains an under-explored area. A main goal of this study is to take some steps in this direction and explore the distributed opportunistic scheduling (DOS) with delay constraints. Consider a network with M links which contend for the channel using random access. Distributed scheduling in such a network requires joint channel probing and distributed scheduling. Using optimal stopping theory, we explore DOS for throughput maximization, under two different types of average delay constraints: 1) a network-wide constraint where the average delay should be no greater than ?; or 2) individual user constraints where the average delay per user should be no greater than am, m = 1,..., M. Since the standard techniques for constrained optimal stopping problems are based on sample-path arguments and are not applicable here, we take a stochastic Lagrangian approach instead. We characterize the corresponding optimal scheduling policies accordingly, and show that they have a pure threshold structure, i.e. data transmission is scheduled if and only if the rate is above a threshold. Specifically, in the case with a network-wide delay constraint, somewhat surprisingly, there exists a sharp transition associated with a critical time constant, denoted by ?*. If a is less than ?*, the optimal rate threshold depends on ?; otherwise it does not depends on a at all, and the optimal policy is the same as that in the unconstrained case. In the case with individual user delay constraints, we cast the threshold selection problem across links as a non-cooperative game, and establish the existence of Nash equilibria. Again we observe a sharp transition associated with critical time constants {?m*}, in the sense that when ?m? am*for all users, the Nash equilibrium becomes the same one as if there were no delay constraints.

We consider a cognitive radio network where multiple secondary users (SUs) contend for spectrum usage, using random access, over available primary user (PU) channels. Our focus is on SUs' queueing delay performance, for which a systematic understanding is lacking. We take a fluid queue approximation approach to study the steady-state delay performance of SUs, for cases with a single PU channel and multiple PU channels. Using stochastic fluid models, we represent the queue dynamics as Poisson driven stochastic differential equations, and characterize the moments of the SUs' queue lengths accordingly. Since in practical systems, a secondary user would have no knowledge of other users' activities, its contention probability has to be set based on local information. With this observation, we develop adaptive algorithms to find the optimal contention probability that minimizes the mean queue lengths. Moreover, we study the impact of multiple channels and multiple interfaces, on SUs' delay performance. As expected, the use of multiple channels and/or multiple interfaces leads to significant delay reduction.

In a wireless sensor network, short range multihop transmissions are preferred to prolong the network lifetime due to super-linear nature of energy consumption with communication distance. It has been proposed to deploy some relay nodes such that the sensors can transmit the sensed data to a nearby relay node, which in turn delivers the data to the base stations. In general, the relay node placement problems aim to meet certain connectivity and/or survivability requirements of the network by deploying a minimum number of relay nodes. In this paper, we study two-tiered constrained relay node placement problems, where the relay nodes can only be placed at some pre-specified candidate locations. To meet the connectivity requirement, we study the connected single-cover problem where each sensor node is covered by a relay node (to whom the sensor node can transmit data), and the relay nodes form a connected network with the base stations. To meet the survivability requirement, we study the 2-connected double-cover problem where each sensor node is covered by at least two relay nodes, and the relay nodes form a 2-connected network with the base stations. We focus on the computational complexities of the problems, and propose novel polynomial time approximation algorithms for these problems. For the connected single-cover problem, our algorithms have O(1) approximation ratios. For the 2-connected double-cover problem, our algorithms have O(1) approximation ratios for practical settings and O(lnn) approximation ratios for arbitrary settings. Experimental results show that the number of relay nodes used by our algorithms is no more than twice of the number of relay nodes used in an optimal solution.

Distributed opportunistic scheduling (DOS) is studied for wireless ad hoc networks in which many links contend for a channel using random access before data transmission. Simply put, DOS involves a process of joint channel probing and distributed scheduling for ad hoc (peer-to-peer) communications. Since, in practice, link conditions are estimated with noisy observations, the transmission rate must be backed off from the estimated rate in order to avoid transmission outages. Then, a natural question to ask is whether or not it is worthwhile for the link with successful contention to perform further channel probing to mitigate estimation errors, at the cost of additional probing. Thus motivated, this work investigates DOS with two-level channel probing by optimizing the tradeoff between the throughput gain from more accurate rate estimation and the resulting additional delay. By capitalizing on optimal stopping theory with incomplete information, it is shown that the optimal scheduling policy is threshold-based and is characterized by either one or two thresholds, depending on network settings. Necessary and sufficient conditions for both cases are rigorously established. In particular, this analysis reveals that performing second-level channel probing is optimal when the first-level estimated channel condition falls in between the two thresholds. Numerical results are provided to illustrate the effectiveness of the proposed DOS with two-level channel probing. This study is also extended to the case with limited feedback, in which the feedback from the receiver to its transmitter takes the form of$(0,1,e)$.

Distributed opportunistic scheduling (DOS) is studied for wireless ad-hoc networks in which many links contend for the channel using random access before data transmissions. Simply put, DOS involves a process of joint channel probing and distributed scheduling for ad-hoc (peer-to-peer) communications. Since, in practice, link conditions are estimated with noisy observations, the transmission rate has to be backed off from the estimated rate to avoid transmission outages. Then, a natural question to ask is whether it is worthwhile for the link with successful contention to perform further channel probing to mitigate estimation errors, at the cost of additional probing. Thus motivated, this work investigates DOS with two-level channel probing by optimizing the tradeoff between the throughput gain from more accurate rate estimation and the resulting additional delay. Capitalizing on optimal stopping theory with incomplete information, we show that the optimal scheduling policy is threshold-based and is characterized by either one or two thresholds, depending on network settings. Necessary and sufficient conditions for both cases are rigorously established. In particular, our analysis reveals that performing second-level channel probing is optimal when the first-level estimated channel condition falls in between the two thresholds. Finally, numerical results are provided to illustrate the effectiveness of the proposed DOS with two-level channel probing.

Distributed scheduling algorithms for wireless ad hoc networks have received substantial attention over the last decade. The complexity levels of these algorithms span a wide spectrum, ranging from no message passing to constant/polynomial time complexity, or even exponential complexity. However, by and large it remains open to quantify the impact of message passing complexity on throughput and delay. In this paper, we study the effective throughput and delay performance in wireless scheduling by explicitly considering complexity through a vacation model, where signaling complexity is treated as "vacations" and data transmissions as "services," with a focus on delay analysis in heavy traffic regimes. We analyze delay performance in two regimes of vacation models, depending on the relative lengths of data transmission and vacation periods. State space collapse properties proved here enable a significant dimensionality reduction in the challenging problem of delay characterization. We then explore engineering implications and quantify intuitions based on the heavy traffic analysis.

In this paper, we study distributed opportunistic scheduling (DOS) in an ad hoc network, where many links contend for the same channel using random access. In such a network, DOS involves a process of joint channel probing and distributed scheduling. Due to channel fading, the link condition corresponding to a successful channel probing could be either good or poor. In the latter case, further channel probing, although at the cost of additional delay, may lead to better channel conditions and hence yield higher throughput. The desired tradeoff boils down to judiciously choosing the optimal stopping rule for channel probing and distributed scheduling. In this paper, we pursue a rigorous characterization of the optimal strategies from two perspectives, namely, a network-centric perspective and a user-centric perspective. We first consider DOS from a network-centric point of view, where links cooperate to maximize the overall network throughput. Using optimal stopping theory, we show that the optimal scheme for DOS turns out to be apurethresholdpolicy, where the rate threshold can be obtained by solving a fixed-point equation. We further devise iterative algorithms for computing the threshold. We also generalize the studies to take into account fairness requirements. Next, we explore DOS from a user-centric perspective, where each link seeks to maximize its own throughput. We treat the problem of threshold selection across different links as a noncooperative game. We explore the existence and uniqueness of the Nash equilibrium, and show that the Nash equilibrium can be approached by the best response strategy. Since the best response strategy requires message passing from neighboring nodes, we then develop an online stochastic iterative algorithm based on local observations only, and establish its convergence to the Nash equilibrium. Because there is an efficiency loss at the Nash equilibrium, we then study pricing-based mechanisms to mitigate the loss. Our results reveal that rich physical layer/MAC layer (PHY/MAC) diversities are available for exploitation in ad hoc networks. We believe that these initial steps open a new avenue for channel-aware distributed scheduling.

We take a cross-layer design approach to study rate control in multihop wireless networks. Due to the lossy nature of wireless links, the data rate of a given flow becomes smaller and smaller along its routing path. As a result, the data rate received successfully at the destination node (the effective rate) is typically lower than the transmission rate at the source node (the injection rate). In light of this observation, we treat each flow as a "leaky-pipe" flow and introduce the notion of "effective utility" associated with the effective rate (not the injection rate) of each flow. We then explore rate control through effective network utility maximization (ENUM) in this study. Two network models are studied in this paper: (1) ENUM with link outage constraints with a maximum error rate at each link; (2) ENUM with path outage constraints where there exists an end-to-end outage requirement for each flow. For both models, we explicitly take into account the "thinning" feature of data flows and devise distributed hop-by-hop rate control algorithms accordingly. Our numerical examples corroborate that higher effective network utility and better fairness can be achieved by the ENUM algorithms than the standard NUM.

Multicast communications is an efficient mechanism for one-to-many transmissions over a broadcast wireless channel, and is considered as a key technology for supporting emerging broadband multimedia services in the next generation wireless networks, such as Internet Protocol Television (IPTV), mobile TV, etc. Therefore, it is critical to design efficient multicast scheduling schemes to support these multimedia services. In this paper, we propose a cooperative multicast scheduling scheme for achieving efficient and reliable multicast transmission in IEEE 802.16 based wireless metropolitan area networks (WMAN). By exploiting the multi-channel diversity across different multicast groups and user cooperation among group members, the proposed scheme can achieve higher throughput than existing multicast schemes, for subscriber stations in both good and bad channel conditions. In addition, it has good fairness performance by considering the normalized relative channel condition of each multicast group. An analytical model is developed to evaluate the performance of the proposed scheme, in terms of service probability, power consumption, and throughput of each group member and multicast groups. The efficiency of the proposed scheme and the accuracy of the analytical model are corroborated by extensive simulations.

We consider a random-access-based ad hoc network, where different links use mini-slots to contend for the channel, and then successful links transmit data packets, as in CSMA. The focus of our study is to develop optimal strategies for physicallayer- aware (PHY-aware) distributed scheduling, which involves a joint process of channel probing and distributed scheduling. Because of channel fading and cochannel interference, the signalto- interference-plus-noise-ratio (SINR) across links is highly dynamic and can exhibit significant variation. In the low SINR case, further channel probing is likely to lead to better SINR conditions and hence yield higher throughput. The desired tradeoff boils down to judiciously choosing the optimal stopping strategy for channel probing before data transmissions. In this paper, we investigate PHY-aware distributed scheduling, aiming to maximize the overall network throughput. The problem under consideration is inherently challenging: 1) multiple links can transmit successfully simultaneously and the number of simultaneously transmitting links is random; and 2) the network throughput is the sum rate of all transmitting links, but each link involved in the transmission has no knowledge of the instantaneous rates of other links, and the stopping decision is made in a distributed manner based on local information only. We use optimal stopping theory to tackle this challenge, and show that the optimal policy for distributed scheduling has a threshold structure. Accordingly, after a channel probing, a link would proceed with data transmissions only if a function of its instantaneous rate is greater than the optimal rate threshold. Observing that the network throughput depends heavily on the contention probability of each link, we generalize the study to jointly optimize the rate threshold and the contention probability, and propose a two-stage algorithm for computing the pair of optimal rate threshold and contention probability by using fractional optimization and geometric programming.

Node cooperation is one unique feature distinguishing wireless sensor networks (WSNs) from traditional wireless cellular networks. In this paper, we investigate joint clustering and optimal cooperative routing, where neighboring nodes dynamically form coalitions and cooperatively transmit packets to the next hop destination. We show that the cooperative sensor network can be modeled as an edge-weighted graph, based on which minimum energy cooperative routing is characterized by using the standard shortest path algorithm. We then focus on energy-delay-constrained maximum throughput routing, which is known to be NP-hard. We study two interesting cases: (1) For the case where the delay can be expressed in terms of the number of hops, we use the bi-section method to find the maximum throughput routing; (2) For large scale networks where the end-to-end delay can be approximated as the product of the number of hops and the average one-hop delay, we present a polynomial time algorithm to find the maximum throughput routing. Our numerical results confirm that the energy efficient cooperative routing can enhance the performance of WSNs significantly.

Exploiting the broadcast nature of wireless communications, multicast transmission is an efficient way to improve the network throughput by transmitting the same contents to multiple receivers simultaneously. It has been considered as a key technology for supporting emerging services in next-generation IEEE 802.16 based wireless metropolitan area networks (WMANs), such as Internet Protocol TV (IPTV) and mobile TV. Therefore, it is critical to devise efficient multicast scheduling schemes to support these multimedia services. In this paper, we propose a novel multicast scheduling scheme, using downlink cooperative transmission for achieving high throughput not only for all multicast groups but also for each group member. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

Distributed opportunistic scheduling (DOS) protocols are proposed for multiple-input multiple-output (MIMO) ad-hoc networks with contention-based medium access. The proposed scheduling protocols distinguish themselves from other existing works by their explicit design for system throughput improvement through exploiting spatial multiplexing and diversity in a distributed manner. As a result, multiple links can be scheduled to simultaneously transmit over the spatial channels formed by transmit/receiver antennas. Taking into account the tradeoff between feedback requirements and system throughput, we propose and compare protocols with different levels of feedback information. Furthermore, in contrast to the conventional random access protocols that ignore the physical channel conditions of contending links, the proposed protocols implement a pure threshold policy derived from optimal stopping theory, i.e. only links with threshold-exceeding channel conditions are allowed for data transmission. Simulation results confirm that the proposed protocols can achieve impressive throughput performance by exploiting spatial multiplexing and diversity.

Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation, where the transmission rate needs to be backed off from the estimated rate to reduce the outage. It is shown that the optimal scheduling policy remains to be threshold-based, and that the rate threshold turns out to be a function of the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, a suboptimal linear backoff scheme that backs off the estimated signal-to-noise ratio (SNR) and hence the rate is proposed. The corresponding optimal backoff ratio and rate threshold can be obtained via an iterative algorithm. Finally, simulation results are provided to illustrate the tradeoff caused by increasing training time to improve channel estimation at the cost of probing efficiency.

Due to multi-path fading and co-channel interference, wireless links are lossy in nature. As a result, the data rate of a given flow becomes "thinner and thinner" along its routing path, and the data rate received successfully at the destination node (theeffectiverate) is typically lower than the transmission rate at the source node (theinjectionrate). In light of this observation, each flow is treated as a "leaky-pipe" model in this study. Moreover, we introduce the notion of "effective utility" associated with the effective rate (not the injection rate) for each flow, and explore rate control mechanisms through effective network utility maximization (ENUM). We focus on two network models: (1) ENUM with link outage constraints with a maximum error rate at each link; (2) ENUM with path outage constraints where there exists an end-to-end outage requirement for each flow. For both problems, we explicitly take into account the "thinning" feature of data flows and devise distributed hop-by-hop rate control algorithms accordingly. Our numerical examples corroborate that higher effective network utility and better fairness among effective flow rates can be achieved by the ENUM algorithms than the standard NUM.

We study channel aware distributed scheduling in ad hoc networks where many links contend for the common channel using random access, and the focus here is on the model where each transmitter node has multiple intended receivers. In such a network, channel probing takes place in two phases: 1) in phase I, all transmitters contend for the channel using random access to reserve the channel, and the probing to accomplish a successful channel contention takes a random duration; and 2) in phase II, subsequent probings are carried out to estimate the link conditions from the successful transmitter in phase I to its intended receivers, according to specific probing mechanisms, and the probing for each receiver takes a constant duration. We shall study various probing mechanisms for utilizing multi-receiver diversity in phase II and multiuser diversity in phase I for ad hoc (peer-to-peer) communications. Clearly, further probing increases the likelihood of seeing better channel conditions for exploiting diversities, but at the cost of additional time. Therefore, channel probing must be done efficiently to balance the tradeoff between the throughput gain from better channel conditions and the probing cost. One main objective of this study is to characterize this tradeoff in a stochastic decision making framework. Specifically, we cast network throughput optimization as an optimal stopping problem, and then explore channel aware distributed scheduling to leverage multi-receiver diversity and multiuser diversity in a joint manner. We show that the optimal scheduling policies for all proposed probing mechanisms exhibit threshold structures, indicating that they are amenable to easy distributed implementation. We show that the optimal thresholds and the maximum network throughput can be obtained off-line by solving fixed point equations. We further develop iterative algorithms to compute the optimal thresholds and the throughput.

The implementation of distributed network utility maximization (NUM) algorithms hinges heavily on information feedback through message passing among network elements. In practical systems the feedback is often obtained using error-prone measurement mechanisms and suffers from random errors. In this paper, we investigate the impact of noisy feedback on distributed NUM. We first study the distributed NUM algorithms based on the Lagrangian dual method, and focus on the primal-dual (P-D) algorithm, which is a single time-scale algorithm in the sense that the primal and dual parameters are updated simultaneously. Assuming strong duality, we study both cases when the stochastic gradients are unbiased or biased, and develop a general theory on the stochastic stability of the P-D algorithms in the presence of noisy feedback. When the gradient estimators are unbiased, we establish, via a combination of tools in Martingale theory and convex analysis, that the iterates generated by distributed P-D algorithms converge with probability one to the optimal point, under standard technical conditions. In contrast, when the gradient estimators are biased, we show that the iterates converge to a contraction region around the optimal point, provided that the biased terms are asymptotically bounded by a scaled version of the true gradients. We also investigate the rate of convergence for the unbiased case, and find that, in general, the limit process of the interpolated process corresponding to the normalized iterate sequence is a stationary reflected linear diffusion process, not necessarily a Gaussian diffusion process. We apply the above general theory to investigate stability of cross-layer rate control for joint congestion control and random access. Next, we study the impact of noisy feedback on distributed two time-scale NUM algorithms based on primal decomposition. We establish, via the mean ODE method, the convergence of the stochastic two time-scale algorithm under mild conditions, for the cases where the gradient estimators in both time scales are unbiased. Numerical examples are used to illustrate the finding that compared to the single time-scale counterpart, the two time-scale algorithm, although having lower complexity, is less robust to noisy feedback.

Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation. In such cases, the transmission rate needs to be backed off from the estimated rate to reduce outages. It is shown that the optimal scheduling policy remains threshold-based, and that the rate threshold turns out to hinge on the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, we devise suboptimal linear backoff schemes that back off the estimated signal-to-noise ratio (SNR) and hence the rate. The corresponding optimal backoff ratios and rate thresholds can be obtained via iterative algorithms. Finally, simulation results are provided to illustrate the tradeoff between increased training time to improve channel estimation and probing efficiency.

The implementation of distributed network utility maximization (NUM) algorithms hinges heavily on information feedback through message passing among network elements. In practical systems the feedback is often obtained using error-prone measurement mechanisms and suffers from random errors. There has been little work in this direction, and by and large the impact of noisy feedback remains unclear. A main objective of this study is to fill this void and to obtain a rigorous and systematic understanding of the impact of stochastic noisy feedback. In this paper, we consider distributed NUM in multi-hop wireless networks, and focus on the impact of noisy feedback on the distributed algorithms based on the Lagrangian dual method. These algorithms can in general be regarded as some form of gradient (or sub-gradient) based methods. Assuming strong duality, we study both cases when the stochastic gradients are unbiased or biased, and develop a general theory on the stochastic stability of these algorithms in the presence of noisy feedback. When the gradient estimator is unbiased, we establish, via a combination of the stochastic Lyapunov Stability Theorem and local analysis, that the iterates generated by distributed NUM algorithms converge with probability one to the optimal point, under standard technical conditions. In contrast, when the gradient estimator is biased, we show that the iterates converge to a contraction region around the optimal point, provided that the biased terms are asymptotically bounded by a scaled version of the true gradients. We also investigate the rate of convergence for the unbiased case, and find that, in general, the limit process of the interpolated process corresponding to the normalized iterate sequence is a stationary reflected linear diffusion process, not necessarily a Gaussian diffusion process. We also apply the above general theory to investigate stability of cross-layer rate control for joint congestion control and random access. Our numerical examples corroborate the theoretic findings well.

We consider a collocated wireless network where all links contend for a given channel and each link can hear others' transmissions. Due to channel fading, the link condition corresponding to a successful channel probing could be either good or bad. In the latter case, further channel probing may lead to better channel conditions, which in turn yields higher throughput. There is clearly a tradeoff between the throughput gain from better channel conditions and the cost for further channel probing. The desired tradeoff can be achieved by choosing the optimal stopping time for channel probing and the transmission rate, in the sense of maximizing the overall average throughput. Using optimal stopping theory, we show that the joint optimal channel probing and transmission (JOCPT) strategy is a pure threshold policy, and interestingly, the threshold turns out to be the optimal average throughput, which is the solution to a fixed point equation. Since the optimal throughput usually cannot be obtained in closed form, we derive a lower bound and an upper bound on the optimal throughput, and devise an iterative algorithm to compute it. Finally, we use numerical examples to show that significant throughput gain can be achieved by the JOCPT strategy.

We consider distributed opportunistic scheduling (DOS) in wireless ad-hoc networks, where many links contend for the same channel using random access. In such networks, distributed opportunistic scheduling involves a process of joint channel probing and distributed scheduling. Due to channel fading, the link condition corresponding to a successful channel probing could be either good or poor. In the latter case, further channel probing, although at the cost of additional delay, may lead to better channel conditions and hence higher transmission rates. The desired tradeoff boils down to judiciously choosing the optimal stopping strategy for channel probing and the rate threshold. In this paper, we pursue a rigorous characterization of the optimal strategies from two perspectives, namely, a network-centric perspective and a user-centric perspective. We first consider DOS from a network-centric point of view, where links cooperate to maximize the overall network throughput. Using optimal stopping theory, we show that the optimal strategy turns out to be a pure threshold policy, where the rate threshold can be obtained by solving a fixed point equation. We further devise an iterative algorithm for computing the threshold. Next, we explore DOS from a user-centric perspective, where each links seeks to maximize its own throughput. We treat the problem of rate threshold selections for different links as a non-cooperative game. We explore the existence and uniqueness of the Nash equilibrium, and show that the Nash equilibrium can be approached by the best response strategy. We then develop an online stochastic iterative algorithm using local observations only, and establish its convergence. Finally, we observe that there is an efficiency loss in terms of the throughput at the Nash equilibrium, and introduce apricing-based mechanism to mitigate the loss.

In this paper, a new achievable rate region for general interference channels with common information is presented. Our result improves upon by applying simultaneous superposition coding over sequential superposition coding. A detailed computation and comparison of the achievable rate region for the Gaussian case is conducted. The proposed achievable rate region is shown to coincide with the capacity region of the strong interference case.

We study rate optimization for multicast communications at the media access control (MAC) layer, and explore transport layer erasure coding to enhance multicast reliability in wireless networks. We start with investigating network models with single-input-single-output (SISO) links. For threshold-T based multicast policies, we characterize the optimal transmission rates that maximize the throughput in stable networks and in saturated networks, respectively. We investigate the tradeoff between stability and throughput therein. We then generalize our study to network models with multiple-input-multiple-output (MIMO) links and non-i.i.d. channel links, and investigate the optimal transmission rate. In addition, to ensure multicast reliability while no retransmission is required at the MAC layer, we propose to use transport layer erasure coding for reliability enhancement, where the problem boils down to jointly optimizing the transmission rate and the multicast threshold. We provide a solution to this optimization problem accordingly

In multi-hop wireless networks, the optimal medium access control (MAC) design is challenging, partially due to the time-varying nature of the PHY-layer communication channels and the network topology. In this paper, we take a utility maximization approach to study fair MAC design towards QoS provisioning. To this end, we first identify two key challenges of wireless access control, namely the topology dependency and the channel dependency, therein. Based on the observation that the topology change and channel variation occur on different time scales, we decompose the utility maximization to two phases: a "global" optimization phase addresses the topology dependency, and arbitrates fair channel access across the links by adapting the persistence probability to achieve long-term fairness, and a "local" optimization phase deals with the channel dependence, and determines the transmission duration based on local channel conditions while maintaining short-term fairness. Observing that the MAC throughput depends on the realizations of channel contention in random access networks, we use stochastic approximation to investigate in depth the MAC design with the adaptive persistence mechanism in the global phase. Using Lyapunov's Stability Theorems and LaSalle's Invariance Theorem, we establish the stability of the proposed algorithm for the global phase and analyze the fairness under (omegaoarr, kappa)-fair utility functions. Our findings reveal that under the large network assumption, there exists a single equilibrium point for the proposed (omegaoarr, kappa)-fair MAC algorithm provided that kappa > 1. We also present the solution to the local optimization phase under general fairness constraints.

We study cooperative data transmissions in coalition-based wireless sensor networks, where neighboring nodes are organized into groups to form coalitions. We assume that data compression and cooperative communications can be carried out within one coalition, and that CSMA is used to reserve the channel from the coalitions to the sink. We examine three schemes for data transmissions from the coalition (with reservation) to the sink. In Scheme 1, one node in the coalition is selected randomly to transmit the data; in Scheme 2, the node with the best channel condition in the coalition transmits the data; and in Scheme 3, all the nodes in the coalition transmit in a cooperative manner. We investigate the corresponding delay performance and the energy consumption of the three schemes. Our numerical examples show that significant gains can be achieved by the coalition-aided transmission schemes, and as expected, Scheme 3 achieves the best performance.

We consider a many-to-one sensor network where a large number of sensors are deployed to monitor a physical environment. We explore sensor activity management to maximize the network lifetime while meeting the quality of service (QoS) requirement. Specifically, in each round the sink estimates the number of active sensors and the control information is fed back to the sensors for activity control. We start with a basic case where the total number of sensors N is known, and the estimator of the number of active sensors fit is accurate. We devise a sensor activity control scheme under which the number of active sensors would converge to the minimum that can meet the QoS requirement. Next, we generalize the study to the following two more complicated cases. (1) The case with known N and inaccurate nt. For this case, we propose a stochastic approximation algorithm to minimize the average number of active sensors while meeting the QoS requirement. (2) The case with unknown N and accurate nt. For this case, we cast the problem as the adaptive control of a Markov chain with unknown parameters and propose a composite optimization-oriented approach for the corresponding sensor activity control. We show that using this composite optimization-oriented approach the number of active sensors would converge to the minimum that can meet the QoS requirement

In this paper, we consider a many-to-one sensor network where a large number of sensors are deployed to monitor a physical environment. We explore sensor activity management to maximize the network lifetime, while meeting the quality-of-service (QoS) requirement. Specifically, in each round the sink estimates the number of active sensors and the control information is fed back to the sensors for activity control. We start with a basic case where the total number of sensors N is known, and the estimator of the number of active sensors n/spl circ//sub t/ is accurate. We devise a sensor activity control scheme under which the number of active sensors would converge to the minimum that can meet the QoS requirement. Next, we generalize the study to the following two more complicated cases: (1) The case with known N and inaccurate n/spl circ//sub t/: For this case, we propose a stochastic approximation algorithm to minimize the average number of active sensors while meeting the QoS requirement. (2) The case with unknown N and accurate n/spl circ//sub t/: For this case, we cast the problem as the adaptive control of a Markov chain with unknown parameters and propose a composite optimization-oriented approach for the corresponding sensor activity control. We show that using this composite optimization-oriented approach the number of active sensors would converge to the minimum that can meet the QoS requirement.

A wireless sensory relay network consists of one source node, one destination node and multiple intermediate relay nodes. In this paper, we study the achievable rates and the scaling laws of power-constrained wireless relay networks in the wideband regime, assuming that relay nodes have no a priori knowledge of channel-state information (CSI) for both the backward channels and the forward channels. We examine the achievable rates in the joint asymptotic regime of the number of relay nodes n, the channel coherence interval L, and the bandwidth W (or the SNR per link rho). We first study narrowband relay networks in the low SNR regime. We investigate a relaying scheme, namely amplify-and-forward (AF) with network training, in which the source node and the destination node broadcast training symbols and each relay node carries out channel estimation and then applies AF relaying to relay information. We provide an equivalent source-to-destination channel model, and characterize the corresponding achievable rate. Our findings show that when rhoL, proportional to the transmission energy in each fading block, is bounded below, the achievable rate has the same scaling order as in coherent relaying, thus enabling us to characterize the scaling law of the relay networks in the low SNR regime. We then generalize the study to power-constrained wideband relay networks, where frequency-selective fading is taken into account. Again, the focus is on the achievable rates by using AF with network training for information relaying. In particular, we examine the scaling behavior of the achievable rates corresponding to two power allocation policies across the frequency subbands at relay nodes, namely, a simple equal power allocation policy and the optimal power allocation policy. We identify the conditions under which the scaling law of the wideband relay networks can be achieved by both power allocation policies. Somewhat surprising, our findings indicate that these two power allocation policies result in achievable rates of the same scaling order, and the scaling law can be characterized under the condition that L/W, proportional to the energy per fading block per subband, is bounded below, and that W is sublinear in n

Time-varying channel conditions, due to multipath fading, present a unique challenge for wireless network design. In this paper, we take a cross-layer approach to study frequency-agile medium access control design for ad hoc networks. Specifically, building on the IEEE 802.11 standard, we propose an opportunistic multi-channel MAC protocol (OMC-MAC), with three key features: 1) by exploiting the channel variations across multiple channels, OMC-MAC achieves selection diversity gain in an opportunistic and distributed manner; 2) the size of the contention window is adjusted adaptively based on the estimate of the number of competing stations, which is obtained via using a sequential Monte Carlo technique; and 3) OMC-MAC achieves "resource pooling" and thus improves the stability of the network. Analysis results reveal that OMC-MAC in wireless LANs achieves significant throughput gain, even under heavy traffic conditions. Extensive simulation studies show that OMC-MAC can achieve efficient channel utilization for each added channel, compared with the standard 802.11 MAC protocol and other multichannel protocols such as DCA and MMAC. Finally, we show via examples that the sequential Monte Carlo method is effective for the adaptation of the contention window size

Multicast is an efficient mechanism to transmit data to multiple receivers in wireless networks. In this paper, we explore rate optimization for multicast communications at the media access control (MAC) layer in wireless networks. We first consider network models with single-input-single-output (SISO) links. For threshold-based multicast policies, we characterize the optimal transmission rates that maximize the throughput in stable networks and in saturated networks, respectively. We then investigate the tradeoff between stability and throughput therein. Furthermore, we generalize our study to networks with multiple-input-multiple-output (MIMO) links and non-i.i.d. channel links, and investigate the optimal transmission rate correspondingly.

The number of competing stations has great impact on the network performance of wireless LANs. It is therefore of great interest to obtain accurate estimation of the number of competing stations so that adaptive control mechanisms can be carried out accordingly. Based on the observation that this estimation problem is nonlinear/non-Gaussian in nature, we propose to use a sequential Monte Carlo technique, namely, the particle filtering to improve the estimation accuracy. One key step in the proposed scheme is to exploit the unscented particle filter, which combines the merits of unscented transformation and particle filtering. Our simulation results indicate that the unscented particle filter can increase the accuracy of the estimation upto 33% in terms of the root mean square error (RMSE), compared with the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the SIR-particle filter

We consider multi-access control for the uplink in OFDMA networks. Assuming that subcarriers are grouped into clusters, we investigate multichannel random access based on local channel state information, and propose an opportunistic multichannel Aloha scheme to maximize the system throughput. A key step is to build a mapping from a user's channel state information to its transmission probability and channel allocation. For the sake of comparison, we also characterize the throughput corresponding to the optimal centralized scheduling by using the extreme-value theory of order statistics. We show that the opportunistic multichannel Aloha scheme is asymptotically order-optimal, in the sense that the only performance loss compared to the optimal centralized scheduling is due to the contention inherent in random access. In addition, we generalize the study to heterogeneous cases. Our findings show that when each user behaves as if it were in homogeneous systems, the proposed scheme can provide proportional fairness among the users.

MIMO techniques using multiple antennas for both transmitting and receiving have recently manifested themselves to be very promising for future broadband wireless networks. Aiming to leverage the impact of these MIMO techniques on network protocol design in wireless LANs (WLANs), we take a utility approach to study channel-aware weighted proportional fair medium access control (WPF MAC) for QoS provisioning. Simply put, in this utility based approach, every user in a WLAN attempts to maximize its own utility, and the optimization procedure takes place in two stages: a channel contention phase that arbitrates fair channel access across the users via combining adaptive persistence mechanism with random backoff, and a data transmission phase that determines the transmission duration based on the channel conditions in each transmission round. Furthermore, adaptive beamforming is carried out by using the training signals embedded in the RTS/CTS handshake to enhance the spectral efficiency of the MIMO links. Using a stochastic approximation method, together with the Lyapunov stability theorems, we establish the stability of the adaptive persistence mechanism in the proposed WPF MAC and analyze the fairness across the users therein.

We consider three-node wireless relay channels in a Rayleigh-fading environment. Assuming transmitter channel state information (CSI), we study upper bounds and lower bounds on the outage capacity and the ergodic capacity. Our studies take into account practical constraints on the transmission/reception duplexing at the relay node and on the synchronization between the source node and the relay node. We also explore power allocation. Compared to the direct transmission and traditional multihop protocols, our results reveal that optimum relay channel signaling can significantly outperform multihop protocols, and that power allocation has a significant impact on the performance.

We study the capacity of multiple-input multiple- output (MIMO) relay channels. We first consider the Gaussian MIMO relay channel with fixed channel conditions, and derive upper bounds and lower bounds that can be obtained numerically by convex programming. We present algorithms to compute the bounds. Next, we generalize the study to the Rayleigh fading case. We find an upper bound and a lower bound on the ergodic capacity. It is somewhat surprising that the upper bound can meet the lower bound under certain regularity conditions (not necessarily degradedness), and therefore the capacity can be characterized exactly; previously this has been proven only for the degraded Gaussian relay channel. We investigate sufficient conditions for achieving the ergodic capacity; and in particular, for the case where all nodes have the same number of antennas, the capacity can be achieved under certain signal-to-noise ratio (SNR) conditions. Numerical results are also provided to illustrate the bounds on the ergodic capacity of the MIMO relay channel over Rayleigh fading. Finally, we present a potential application of the MIMO relay channel for cooperative communications in ad hoc networks.

We consider bursty data communications in code-division multiple-access (CDMA) cellular networks. The significant fluctuation of the cochannel multiple-access interference (MAI) in such systems makes it very challenging to carry out radio resource management. A main goal of this paper is to obtain a fundamental understanding of the temporal correlation structure of the MAI, which plays a crucial role in effective resource allocation. To this end, we take a cross-layer design approach, and characterize the stochastic MAI process while taking into account both the burstiness of data traffic and time-varying channel conditions. Our main results reveal that under standard assumptions on ON/OFF traffic flows and fading channels, the MAI process exhibits scale-invariant burstiness and is "self-similar" (with Hurst parameter 1/2<H<1), in both the uplink and the downlink cases. The MAI self-similarity indicates that the MAI levels are long-range dependent and therefore there exists a nontrivial predictive MAI structure across multiple time scales. The predictive MAI structure can be utilized for effective interference management through dynamic resource allocation. We illustrate this via a rate control scheme based on the MAI prediction, and our results show that the performance gain is substantial. The exploitation of the MAI temporal correlation structure for resource allocation parallels and complements multiuser detection which utilizes the MAI snapshot structure at the symbol level.

This paper studies the ergodic capacity and power allocation for a three-node relay channel over Rayleigh fading. Assuming perfect channel side information (CSI) at the transmitters and the receivers, we find the upper bounds and lower bounds on the capacity by solving generalized "water-filling" power allocation problems. Our results reveal that the optimum relay channel signaling can significantly outperform direct transmissions and traditional multi-hop transmissions. We also characterize the power gain of using the optimum signaling in the high SNR regime and find the corresponding power allocation policies.

In multiuser wireless networks, opportunistic scheduling can improve the system throughput and thus reduce the total completion time. We explore the possibility of reducing the completion time further by incorporating traffic information into opportunistic scheduling. In particular, we first establish general properties for opportunistic scheduling with file size information. Then, we develop new traffic aided opportunistic scheduling (TAOS) schemes by making use of file size information and channel variation in a unified manner. We also derive lower and upper bounds on the total completion time. Our results show that the proposed TAOS schemes can yield significant reduction in the total completion time.

In code-division multiple-access (CDMA) packet data networks, the throughput depends on physical-layer receiver algorithms and medium access control (MAC) layer protocols. Taking a holistic approach, we investigate the throughput of a CDMA network employing linear multiuser detection, type-I automatic retransmission request (ARQ), and packet combining. In particular, the following two models of CDMA data networks are considered. 1) Fixed-access CDMA data networks, by which we mean that the number of active users is fixed. The corresponding throughput is analyzed by using a one-dimensional Markov chain, and conditions for achieving the optimal throughput are explored. 2) Random-access CDMA data networks, in which we take into account the random arrivals/departures of users. By viewing the fixed-access network studied in the first case as a snapshot of the random-access network, we exploit the results therein to analyze the random-access network, under a processor-sharing system model. Moreover, we identify some important properties of the corresponding throughput and devise a simple recursive algorithm to find the throughput-optimal admission region. Some recent advances on large-system performance of various multiuser detection algorithms are employed in our study. The results in this paper quantitatively characterize the potential for network throughput gain by employing multiuser detection and packet combining, in both fixed-access and random-access CDMA packet data networks.

We study opportunistic scheduling in a multiuser wireless network where there are K users, each with a backlogged message for transmission. A key goal is to minimize the total completion time via a cross-layer design approach. Our main contribution consists of two size-aided opportunistic scheduling (SAOS) schemes. The SAOS schemes use both the traffic information and channel variation to reduce the completion time. Our results show that the SAOS schemes can yield significant reduction in the total completion time. We also examine two extreme cases, i.e., wireless shortest processing time first (SRPT) and "riding on the channel peak", which utilize only either the file size information or the instantaneous channel conditions.

Deep shadowing and "hot-spots" are two challenging issues in wireless networks. Aiming to resolve these issues, we take a cross-layer design approach and devise a hierarchical multiuser diversity (HMD) transmission scheme, in which a user can choose to communicate with the base station either directly or using multiple hops via relay stations. Using a throughput-based criterion, we develop a direct/relay link construction algorithm. We then explore opportunistic scheduling under performance-based fairness constraints, for both direct links and relay links; a key goal is to achieve multiuser diversity in both tiers. Our results show that the HMD scheme can combat shadowing and "hot-spots" effectively in the sense that the total system throughput is increased significantly while the throughput requirements of the users are met.

We study bursty data communications in the downlink in code division multiple access (CDMA) systems. We first present a new model that simultaneously takes into account the traffic burstiness and time-varying fading for studying the multi-access interference (MAI), and characterize the MAI from a stochastic process perspective. This new approach enables us to understand the temporal correlation structure. Our finding reveals that the MAI exhibits scale-invariant burstiness and is "self similar" across multiple time scales. The MAI self similarity indicates the existence of a nontrivial predictive MAI structure, which we exploit to conduct resource allocation for interference management. In particular, we utilize the MAI temporal structure to construct a multiple time-scale interference predictor, which is used to predict the MAI level. Rate adaptation is then carried out based on the predicted MAI. Our results show that this rate control scheme achieves better performance than that of the packet-level predictor, and can yield significant performance gain. We also devise a joint rate control and admission control scheme. Specifically, observation time windows are divided into slots, and rate control based on interference prediction is conducted in each slot. Then, the corresponding throughput in each observation window is used for admission control. We also investigate the impact of feedback delay and data burstiness on the system performance.

We characterize the best achievable performance of lossy compression algorithms operating on arbitrary random sources, and with respect to general distortion measures. Direct and converse coding theorems are given for variable-rate codes operating at a fixed distortion level, emphasizing: (a) nonasymptotic results, (b) optimal or near-optimal redundancy bounds, and (c) results with probability one. This development is based in part on the observation that there is a precise correspondence between compression algorithms and probability measures on the reproduction alphabet. This is analogous to the Kraft inequality in lossless data compression. In the case of stationary ergodic sources our results reduce to the classical coding theorems. As an application of these general results, we examine the performance of codes based on mixture codebooks for discrete memoryless sources. A mixture codebook (or Bayesian codebook) is a random codebook generated from a mixture over some class of reproduction distributions. We demonstrate the existence of universal mixture codebooks, and show that it is possible to universally encode memoryless sources with redundancy of approximately (d/2) log n bits, where d is the dimension of the simplex of probability distributions on the reproduction alphabet.

We explore the performance of minimum mean-square error (MMSE) multiuser receivers in wireless systems where the signatures are modeled as random and take values in complex space. First we study the conditional distribution of the output multiple-access interference (MAI) of the MMSE receiver. By appealing to the notion of conditional weak convergence, we find that the conditional distribution of the output MAI, given the received signatures and received powers, converges in probability to a proper complex Gaussian distribution that does not depend on the signatures. This result indicates that, in a large system, the output interference of the MMSE receiver is approximately Gaussian with high probability, and that systems with MMSE receivers are robust to the randomness of the signatures. Building on the Gaussianity of the output interference, we then take the quality of service (QoS) requirements as meeting the signal-to-interference ratio (SIR) constraints and identify the network capacity of single-class systems with random spreading. The network capacity is expressed uniquely in terms of the SIR requirements and received power distributions. Compared to the network capacity corresponding to the optimal signature allocation, we conclude that at the cost of transmission power, the gap between the network capacity corresponding to optimal signatures and that corresponding to random signatures can be made arbitrarily small. Therefore, from the viewpoint of the network capacity, systems with MMSE receivers are robust to the randomness of signatures.

We present a large-system performance analysis of blind and group-blind multiuser detection methods. In these methods, the receivers are estimated based on the received signal samples. In particular, we assume binary random spreading, and let the spreading gain N, the number of users K, and the number of received signal samples M all go to infinity, while keeping the ratios K/N and M/N fixed. We characterize the asymptotic performance of the direct-matrix inversion (DMI) blind linear minimum mean-square error (MMSE) receiver, the subspace blind linear MMSE receiver, and the group-blind linear hybrid receiver. We first derive the asymptotic average output signal-to-interference-plus-noise ratio (SINR) for each of these receivers. Our results reveal an interesting "saturation" phenomenon: The output SINR of each of these receivers converges to a finite limit as the signal-to-noise ratio (SNR) of the desired user increases, which is in stark contrast to the fact that the output SINR achieved by the exact linear MMSE receiver can get arbitrarily large. This indicates that the capacity of a wireless system with blind or group-blind multiuser receivers is not only interference-limited, but also estimation-error limited. We then show that for both the blind and group-blind receivers, the output residual interference has an asymptotic Gaussian distribution, independent of the realizations of the spreading sequences. The Gaussianity indicates that in a large system, the bit-error rate (BER) is related to the SINR simply through the Q function.

In a mobile wireless system, fading effects can be classified into large-scale (long-term) effects and small-scale (short-term) effects. We use transmission power control to compensate for large-scale fading and exploit receiver antenna (space) diversity to combat small-scale fading. We show that the interferences across the antennas are jointly Gaussian in a large system, and then characterize the signal-to-interference ratio for both independent and correlated (across the antennas) small-scale fading cases. Our results show that when each user's small-scale fading effects are independent across the antennas, there is a clear separation between the gains of transmission power control and diversity combining, and the two gains are additive (in decibels). When each user's small-scale fading effects are correlated across the antennas, we observe that, in general, the gains of transmission power control and diversity combining are coupled. However, when the noise level diminishes to zero, using maximum ratio combining "decouples" the gains and achieves the same diversity gain as in the independent case. We then characterize the Pareto-optimal (minimum) transmission power allocation for the cases of perfect and noisy knowledge of the desired user's large-scale fading effects. We find that using antenna diversity leads to significant gains for the transmission power.

Multiple-access interference (MAI) in a code-division multiple-access (CDMA) system plays an important role in performance analysis and characterization of fundamental system limits. We study the behavior of the output MAI of the minimum mean-square error (MMSE) receiver employed in the uplink of a direct-sequence (DS)-CDMA system. We focus on imperfect power-controlled systems with random spreading, and establish that in a synchronous system (1) the output MAI of the MMSE receiver is asymptotically Gaussian, and (2) for almost every realization of the signatures and received powers, the conditional distribution of the output MAI converges weakly to the same Gaussian distribution as in the unconditional case. We also extend our study to asynchronous systems and establish the Gaussian nature of the output interference. These results indicate that in a large system the output interference is approximately Gaussian, and the performance of the MMSE receiver is robust to the randomness of the signatures and received powers. The Gaussianity justifies the use of single-user Gaussian codes for CDMA systems with linear MMSE receivers, and implies that from the viewpoints of detection and channel capacity, signal-to-interference ratio (SIR) is the key parameter that governs the performance of the MMSE receiver in a CDMA system.

In a wireless system, fading effects can be classified into large-scale effects and small-scale effects. We use power control to compensate for large-scale fading and exploit spatial diversity to combat small-scale fading. We characterize the SIR and our results show that when each user's small-scale fading effects are independent across the antennas, there is a clear separation between the gains of power control and diversity combining, and the two gains are additive (in decibels). We then characterise the Pareto-optimal transmission power allocation.

We study the admissibility and network capacity of imperfect power-controlled code-division multiple access (CDMA) systems with linear receivers in fading environments. In a CDMA system, a set of users is admissible if their simultaneous transmission does not result in violation of any of their quality-of-service (QoS) requirements; the network capacity is the maximum number of admissible users. We consider a single-cell imperfect power-controlled CDMA system, assuming known received power distributions. We identify the network capacities of single-class systems with matched-filter (MF) receivers for both the deterministic and random signature cases. We also characterize the network capacity of single-class systems with linear minimum-mean-square-error (MMSE) receivers for the deterministic signature case. The network capacities can be expressed uniquely in terms of the users' signal-to-interference ratio (SIR) requirements and received power distributions. For multiple-class systems equipped with MF receivers, we find a necessary and sufficient condition on the admissibility for the random signature case, but only a sufficient condition for the deterministic signature case. We also introduce the notions of effective target SIR and effective bandwidth, which are useful in determining the admissibility and hence network capacity of an imperfect power-controlled system.

Due to the fast-growing demand for network capacity in wireless networks, the characterization of network capacity has become one of the most fundamental and pressing issues. While there have been considerable efforts to study CDMA systems at both the physical layer and network layer, the network capacity of power-controlled CDMA systems with linear receivers, especially in fading environments, is not well-understood. In this paper, we study a single-cell synchronous CDMA system equipped with the matched filter receiver in fading channels, and identify the network capacity for single-class systems and the network capacity region for multiple-class systems assuming known distributions of received powers of mobile users. Both the network capacity and network capacity region can be uniquely expressed in terms of the users QoS requirements and the distributions of the received powers. We also find the tightest upper bound of the network capacity over all possible distributions of received powers, and explore the concepts of effective target SIR and effective bandwidth, which play an important role in determining the admissibility and characterizing the network capacity.