Wireless federated analytics face two critical challenges: data privacy and communication efficiency, since the local data may contain sensitive information and the users may be equipped with limited communication capability. Existing methods often adopt a direct combination of privacy-preservation schemes and compression mechanisms but overlook the privacy amplification effect from errors introduced in compression and wireless communication. With such consideration, a Differentially Private Quadrature Amplitude Modulation (DP-QAM) scheme, which leverages privacy amplification from both compression and noisy wireless channels, is proposed. The privacy guarantee is established in terms of the emergingf-DP, and the trade-off between privacy, communication cost, and accuracy in terms of mean square error (MSE) is characterized in the fundamental use cases of distributed mean estimation and frequency estimation, which outperforms the state-of-the-art methods. Moreover, the advantage of the proposed method over the classic Gaussian mechanism is further demonstrated from a rate-distortion perspective. Finally, extensive simulation results validate the effectiveness of the proposed mechanism.

Federated Learning (FL) offers a pioneering distributed learning paradigm that enables devices/clients to build a shared global model that can be obtained through frequent model transmissions between clients and a central server, causing high latency, energy consumption, and congestion over backhaul links. To overcome these drawbacks, Hierarchical Federated Learning (HFL) has emerged, which organizes clients into multiple clusters and utilizes edge nodes (e.g., edge servers) for intermediate model aggregations between clients and the central server. Current research on HFL mainly focus on enhancing model accuracy, latency, and energy consumption in scenarios with a stable/fixed set of clients. However, addressing the dynamic availability of clients – a critical aspect of real-world scenarios – remains underexplored. This study delves into optimizing client selection and client-to-edge associations in HFL under intermittent client participation so as to minimize overall system costs (i.e., delay and energy), while achieving fast model convergence. We unveil that achieving this goal involves solving a complex NP-hard problem. To tackle this, we propose a stagewise methodology that splits the solution into two stages, referred to as Plan A and Plan B. Plan A focuses on identifying long-term clients with high chance of participation in subsequent model training rounds. Plan B serves as a backup, selecting alternative clients when long-term clients are unavailable during model training rounds. This stagewise methodology offers a fresh perspective on client selection that can enhance both HFL and conventional FL via enabling low-overhead decision-making processes. Through evaluations on diverse datasets, we show that our methodology outperforms existing benchmarks on crucial factors such as model accuracy and system costs.

While the recently advocated coded edge computing paradigm is promising for mitigating the unfavorable latency caused by the straggling edge nodes (ENs) in distributed edge networks, it also introduces new challenges in communications. To this end, existing works often treat the transmission of coded computing information as a traditional multi-user communication problem. However, a fundamental difference between multi-user communication and coded computing is that the transmitted messages are often assumed independent in the former while arecorrelatedin the latter, due to task encoding. With this consideration, a novel distributed source coding (DSC)-assisted downlink transmission scheme is proposed in this work for coded edge computing. Particularly, by exploiting such correlation with DSC, the proposed scheme can achieve efficient data compression and flexible load reallocation to better match the channel conditions of the ENs for efficient transmission. In addition, to efficiently fulfill the proposed scheme, a syndrome puncturing based DSC method well-suited to coded edge computing is also developed. To the best of our knowledge, this work is among the first to explore the interesting analogy between the transmission in coded edge computing and the classic DSC problem. The effectiveness of the proposed scheme is manifested through its exemplary integration with non-orthogonal multiple access and corroborated by simulation results.

The advantage assumed by conventional distributed edge computing in handling large-scale tasks is often overshadowed by straggling edge nodes (ENs). This in turn catalyzes the recent emergence of coded edge computing that can effectively mitigate straggling via subtle task encoding. Nonetheless, coded edge computing presents new challenges in communications. In particular, existing offloading schemes are mainly designed for conventional distributed edge computing, where the data offloaded to different ENs are often non-overlapping. This makes them not well-suited to coded edge computing, where substantial redundancy exists among the data offloaded to different ENs due to task encoding. To the best of our knowledge, a tailor-designed efficient offloading scheme for coded edge computing still remains underexplored. With this consideration, a novel coding-aware rate splitting scheme is proposed in this work, which splits the data offloaded to different ENs in a coding-aware manner to avoid transmission redundancy and enables multiple concurrent multi-casts to the ENs. In addition, based on the concave-convex procedure and the sequential parametric optimization framework, two optimization algorithms are developed to minimize the overall latency and the energy consumption under the proposed scheme, respectively. Simulations are conducted to corroborate the effectiveness of the proposed scheme.

Existing score-based adversarial attacks mainly focus on crafting $top$-1 adversarial examples against classifiers with single-label classification. Their attack success rate and query efficiency are often less than satisfactory, particularly under small perturbation requirements; moreover, the vulnerability of classifiers with multi-label learning is yet to be studied. In this paper, we propose a comprehensive surrogate free score-based attack, named \b geometric \b score-based \b black-box \b attack (GSBA$^K$), to craft adversarial examples in an aggressive $top$-$K$ setting for both untargeted and targeted attacks, where the goal is to change the $top$-$K$ predictions of the target classifier. We introduce novel gradient-based methods to find a good initial boundary point to attack. Our iterative method employs novel gradient estimation techniques, particularly effective in $top$-$K$ setting, on the decision boundary to effectively exploit the geometry of the decision boundary. Additionally, GSBA$^K$ can be used to attack against classifiers with $top$-$K$ multi-label learning. Extensive experiential results on ImageNet and PASCAL VOC datasets validate the effectiveness of GSBA$^K$ in crafting $top$-$K$ adversarial examples.

The prevalent distributed machine learning paradigm faces two critical challenges: communication efficiency and data privacy. SIGNSGD provides a simple-to-implement approach with improved communication efficiency by requiring workers to share only the signs of the gradients. However, it fails to converge in the presence of data heterogeneity, and a simple fix is to add Gaussian noise before taking the signs, which leads to the Noisy SIGNSGD algorithm that enjoys competitive performance while significantly reducing the communication overhead. Existing results suggest that Noisy SIGNSGD with additive Gaussian noise has the same privacy guarantee as classic DP-SGD due to the post-processing property of differential privacy, and logistic noise may be a good alternative to Gaussian noise when combined with the sign-based compressor. Nonetheless, discarding the magnitudes in Noisy SIGNSGD leads to information loss, which may intuitively amplify privacy. In this paper, we make this intuition rigorous and quantify the privacy amplification of the sign-based compressor. Particularly, we analytically show that Gaussian noise leads to a smaller estimation error than logistic noise when combined with the sign-based compressor and may be more suitable for distributed learning with heterogeneous data. Then, we further establish the convergence of Noisy SIGNSGD. Finally, extensive experiments are conducted to validate the theoretical results.

Decentralized federated learning (DFL) is a collaborative machine learning framework for training a model across participants without a central server or raw data exchange. DFL faces challenges due to statistical heterogeneity, as participants often possess data of different distributions reflecting local environments and user behaviors. Recent work has shown that the neural tangent kernel (NTK) approach, when applied to federated learning in a centralized framework, can lead to improved performance. We propose an approach leveraging the NTK to train client models in the decentralized setting, while introducing a synergy between NTK-based evolution and model averaging. This synergy exploits inter-client model deviation and improves both accuracy and convergence in heterogeneous settings. Empirical results demonstrate that our approach consistently achieves higher accuracy than baselines in highly heterogeneous settings, where other approaches often underperform. Additionally, it reaches target performance in 4.6 times fewer communication rounds. We validate our approach across multiple datasets, network topologies, and heterogeneity settings to ensure robustness and generalization. Source code for NTK-DFL is available at https://github.com/Gabe-Thomp/ntk-dfl}{https://github.com/Gabe-Thomp/ntk-dfl

The ever-increasing scale and more stringent latency requirements of mobile computing tasks have driven the recent development of distributed edge computing. In distributed edge computing, a large-scale computing task is partitioned into multiple small subtasks and executed in parallel on multiple edge nodes (ENs) to reduce computation delay. In early works of this area, the computation results of the subtasks are often transmitted back in a non-cooperative manner, which may lead to suboptimal downlink communication delay. Replicated edge computing can alleviate this issue by replicating the computing task over multiple ENs to enable cooperative transmission in the downlink. However, this will inevitably entail multi-fold increase of computation costs. To bridge the gap between the conventional distributed edge computing and the replicated edge computing, a novel partial replication based distributed edge computing scheme is proposed in this work. In particular, by judiciously determining the portion of task to be replicated at the ENs, the proposed scheme can harvest cooperative transmission gains while avoiding excessive computational replication costs. Accordingly, a partial replication based delay minimization problem is formulated. By leveraging the generic alternating optimization framework, this problem can be divided into two subproblems of power allocation and task partitioning. Through analysis, a semi-closed form solution is derived for the former non-convex subproblem, while the latter subproblem turns out to be linear. Simulation results are presented to corroborate the effectiveness of the proposed scheme.

In recent years, the application of artificial intelligence (AI) in wireless communications has demonstrated inherent robustness against wireless channel distortions. Most existing works empirically leverage this robustness to yield considerable performance gains through AI architectural designs. However, there is a lack of direct theoretical analysis of this robustness and its potential to enhance communication efficiency, which restricts the full exploitation of these advantages. In this paper, we adopt an information-theoretic approach to evaluate the robustness in wireless distributed learning by deriving an upper bound on the task performance loss due to imperfect wireless channels. Utilizing this insight, we define task outage probability and characterize the maximum transmission rate under task accuracy guarantees, referred to as the task-aware ϵ-capacity resulting from the robustness. To achieve the utility of the theoretical results in practical settings, we present an efficient algorithm for the approximation of the upper bound. Subsequently, we devise a robust training framework that optimizes the trade-off between robustness and task accuracy, enhancing the robustness against channel distortions. Extensive experiments validate the effectiveness of the proposed upper bound and task-aware ϵ-capacity and demonstrate that the proposed robust training framework achieves high robustness, thus ensuring a high transmission rate while maintaining inference performance.

Distributed multi-exit neural networks (MeNNs) enable mobile devices to handle complex tasks such as image classification, but their performance is highly dependent on transmission quality and is therefore vulnerable to side-channel attacks. In this paper, we design a side-channel attack model and propose an efficient inference framework based on the distributed MeNN to resist the designed attack. First, we design an intelligent side-channel attack model, in which the attacker can eavesdrop on the communication channel and use deep reinforcement learning (RL) to predict the early exit decision of each sample. Next, we develop a defense method that employs a hierarchical and multi-agent RL to determine whether to infer locally or offload to a chosen early exit on the server, and to adjust the transmit power accordingly. We further propose a critic-guided safety mechanism that steers local agents away from risky policies that would cause inference failures or severe data leakage. We prove that our framework enforces a strict instantaneous security constraint and asymptotically achieves the optimum by deriving a regret bound. Extensive experiments on several datasets (including CIFAR‑10, CIFAR‑100, STL‑10, EMNIST, FMNIST, and Stanford Cars) show that our method reduces inference latency, improves classification accuracy, and significantly enhances robustness against side-channel attacks, as compared with two benchmarks SCAN and PCE.

Due to the different privacy and local model quality requirements for each participant, federated learning (FL) is vulnerable to membership inference attacks. To solve this issue, we propose a risk-aware reinforcement learning (RL)-based personalized differentially private FL framework. This framework uses local model accuracy and privacy loss as the constraints to satisfy the user’s personalized requirements. By designing a multi-agent RL, this framework optimizes perturbation policy including perturbation mechanisms and parameters (such as privacy budget and probabilistic relaxation). The goal of each participant is to improve global accuracy and reduce privacy loss, attack success rate, and short-term risk value. Firstly, the framework designs a two-level hierarchical policy selection module to choose the perturbation policy to accelerate learning speed. Secondly, our proposed framework designs a punishment function to evaluate short-term risk and an R-network to estimate long-term risk, which guarantees safe exploration. Thirdly, this framework formulates an improved Boltzmann policy distribution to increase the impact of risk, thus avoiding risky policies that may cause severe privacy leakage or local task failure. We also analyze the convergence performance and provide privacy analysis for both Gaussian and Laplace mechanisms. Experimental results based on the MNIST dataset demonstrate the effectiveness of our framework compared with benchmarks.

Plagued by slow or failing workers (also known as stragglers), the speedup gain assumed by distributed learning often falls short. Although substantial efforts have been devoted to mitigating this straggling effect with coding-theoretic techniques, existing pioneering works often suffer from two issues: dense combination and inflexibility. In particular, a code that involves dense combination of sub-tasks may destroy sparsity and lead to heavy workload. In contrast, an inflexible code that conservatively designs its computation procedure according to the presumed maximum number of stragglers may entail unnecessary redundancy when the actual number of stragglers is small. To this end, a generic framework based on matrix splitting is proposed in this work to construct sparse and flexible codes. Specifically, by splitting an original sparse coding matrix into two sparser sub-matrices, a two-layer coded computation that maintains sparsity can be created accordingly. In the meantime, when the actual number of stragglers is small, the computation may be flexibly terminated at layer-one without executing layer-two, thereby avoiding unnecessary computation. Based on this framework, a novel flexible Bernoulli code is proposed. In addition, by deriving a lower bound in closed-form through the lens of a bipartite graph, its decoding probability is shown to be high and asymptotically one. Moreover, extensive simulations including an application in distributed learning of LeNet are conducted to validate the effectiveness of the proposed scheme.

Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices. The data distributed among the edge devices are highly heterogeneous. Thus, FL faces the challenge of data distribution and heterogeneity, where non-Independent and Identically Distributed (non-IID) data across edge devices may yield in significant accuracy drop. Furthermore, the limited computation and communication capabilities of edge devices increase the likelihood of stragglers, thus leading to slow model convergence. In this article, we propose the FedDHAD FL framework, which comes with two novel methods: dynamic heterogeneous model aggregation (FedDH) and adaptive dropout (FedAD). FedDH dynamically adjusts the weights of each local model within the model aggregation process based on the non-IID degree of heterogeneous data to deal with the statistical data heterogeneity. FedAD performs neuron-adaptive operations in response to heterogeneous devices to improve accuracy while achieving superb efficiency. The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and computation cost (up to 15.0% smaller).

Communication overhead has become one of the major bottlenecks in the distributed training of modern deep neural networks. With such consideration, various quantization-based stochastic gradient descent (SGD) solvers have been proposed and widely adopted, among which SignSGD with majority vote shows a promising direction because of its communication efficiency and robustness against Byzantine attackers. However, SignSGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. In this article, a sufficient condition for the convergence of the sign-based gradient descent method is derived, based on which a novel magnitude-driven stochastic-sign-based gradient compressor is proposed to address the non-convergence issue of SignSGD. The convergence of the proposed method is established in the presence of arbitrary data heterogeneity. The Byzantine resilience of sign-based gradient descent methods is quantified, and the error-feedback mechanism is further incorporated to boost the learning performance. Experimental results on the MNIST dataset, the CIFAR-10 dataset, and the Tiny-ImageNet dataset corroborate the effectiveness of the proposed methods.

Timely gathering of sensing data is critical in wireless sensor networks (WSNs). However, in delay-sensitive applications, maintaining the freshness of collected data poses a significant challenge. To tackle this issue, an age of information (AoI)-aware data collection method leveraging unmanned aerial vehicle (UAV) and intelligent reflective surface (IRS) is proposed in this work. Particularly, a UAV is employed to traverse over ground sensor nodes (SNs) and reliably collect their sensing data where the received signal strength is enhanced through IRS. The UAV’s flight trajectory and its association with SNs, as well as the IRS phase control strategy are jointly optimized to minimize the weighted sum of the average AoI of the SNs and energy consumption of the UAV. However, this optimization is complicated by potential inaccuracies in IRS channel state estimation. To tackle this challenge, we propose an enhanced deep reinforcement learning (DRL) framework that incorporates a dual-network agent with two nested neural networks (NNs): UAV-NN, which jointly optimizes the UAV trajectory and SN association, and IRS-NN, which dynamically adjusts IRS phase shifts based on sampled channel states, UAV position, and associated SN. By integrating this architecture into proximal policy optimization (PPO) and deep Q-network (DQN), we develop two novel algorithms: PPO-RAC and DQN-RAC, tailored for IRS-assisted UAV data collection. Extensive simulations validate their effectiveness across diverse scenarios, demonstrating significant AoI reduction compared to baseline methods.

As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices significantly differ in terms of both computation and communication capacity, i.e., system heterogeneity. The statistical heterogeneity leads to severe accuracy degradation while the system heterogeneity significantly prolongs the training process. In order to address the heterogeneity issue, we propose an Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two novel methods. First, we propose an asynchronous FL system model with a dynamical model aggregation method between updated local models and the global model on the server for superior accuracy and high efficiency. Then, we propose an adaptive local model adjustment method by aggregating the fresh global model with local models on devices to further improve the accuracy. Extensive experimentation with 6 models and 5 public datasets demonstrates that FedASMU significantly outperforms baseline approaches in terms of accuracy (0.60% to 23.90% higher) and efficiency (3.54% to 97.98% faster).

Modern vehicular networks encounter a multitude of computation-intensive tasks that have unique processing topologies represented by graph structures. The integration of edge computing and vehicular networks has provided a unique platform for handling these tasks at the network edge. However, the complex structure of these tasks makes their scheduling and execution challenging. This paper proposes a Vehicular Computing-assisted Edge Network (VCEN) architecture, where graph tasks are scheduled over a Vehicle-Edge Collaborative Cloud (VECC) for parallel execution. Our goal is to obtain feasible mappings between task components and computing nodes in the VECC while minimizing task execution latency and energy consumption. We show that achieving this goal requires solving an NP-hard optimization problem with complex constraints related to task structure and VECC topology. We then propose a fast and lightweight approach for graph task scheduling over VECC that comprises two key phases. In the former phase, we introduce a preprocessing algorithm that reduces the graph task's dimensionality by merging important components and cutting redundant edges. In the latter phase, we deploy a cost-reduction-preferred mapping algorithm to obtain feasible mappings between task components and VECC. Through simulations, we demonstrate our superior performance in different network settings.

Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exchanges between devices and the centralized server in the standard FL paradigm suffer from severe efficiency bottlenecks on the server. While enabling collaborative training without a central server, existing decentralized FL approaches either focus on the synchronous mechanism that deteriorates FL convergence or ignore device staleness with an asynchronous mechanism, resulting in inferior FL accuracy. In this paper, we propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments with three unique contributions. First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence. Second, we propose a dynamic staleness-aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce communication and computation costs without significant accuracy degradation. Extensive experimentation on four public datasets and four models demonstrates the strength of AEDFL in terms of accuracy (up to 16.3% higher), efficiency (up to 92.9% faster), and computation costs (up to 42.3% lower).

Unmanned aerial vehicles (UAVs) offer exceptional maneuverability and mobility, making them valuable for data collection in the Internet of Things (IoT). However, to ensure sustainable data services, UAVs with limited battery capacity require energy replenishment during their operational period. In this study, we investigate the joint design of charging station (CS) placement and UAV trajectory to enable continuous and timely data gathering in IoT networks. We formulate a mixed combinatorial optimization problem aimed at minimizing the network’s peak age of information (AoI) by deploying a specific number of CSs from a set of potential sites and designing the UAV trajectory for data gathering and energy recharging. Convex optimization techniques are employed to find the optimal UAV trajectory, given any feasible CS placement solution. Furthermore, we demonstrate that, with the optimized UAV trajectory, the optimal CS placement problem becomes a maximization problem of a non-submodular, non-decreasing set function under a cardinality constraint, known to be NP-hard. To tackle this challenge, we propose a greedy CS deployment algorithm that provides an approximate optimal solution within a constant factor of 1α1-(1-αγK)K, where α ϵ [0,1] represents the generalized curvature, γ ϵ [0,1] denotes the submodularity ratio, and K represents the number of CSs. Additionally, we introduce a low-complexity CS placement algorithm based on path allocation, which is particularly useful in scenarios involving UAVs with very limited battery capacity. Through simulation results, we demonstrate that our proposed approaches, which jointly optimize CS placement and UAV trajectory, achieve significantly smaller AoI values compared to distance-based strategies, both with and without UAV trajectory optimization.

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller).

Federated learning allows collaborative clients to solve a machine-learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learning, but the joint optimization of communication overhead, learning reliability, and deployment efficiency is still an open problem. To this end, we propose a new scheme named federated learning via plurality vote (FedVote). In each communication round of FedVote, clients transmit binary or ternary weights to the server with low communication overhead. The model parameters are aggregated via weighted voting to enhance the resilience against Byzantine attacks. When deployed for inference, the model with binary or ternary weights is resource-friendly to edge devices. Our results demonstrate that the proposed method can reduce quantization error and converges faster compared to the methods directly quantizing the model updates.

Vehicular Clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as Directed Acyclic Graphs (DAGs) consisting of interdependent vertices/subtasks and directed edges. However, efficient scheduling of DAG tasks over VCs presents significant challenges, mainly due to the dynamic service provisioning of vehicles within VCs and non-Euclidean representation of DAG tasks’ topologies. In this paper, we propose a Graph neural network-Augmented Deep Reinforcement Learning scheme (GA-DRL) for the timely scheduling of DAG tasks over dynamic VCs. In doing so, we first model the VC-assisted DAG task scheduling as a Markov decision process. We then adopt a multi-head Graph ATtention network (GAT) to extract the features of DAG subtasks. Our developed GAT enables a two-way aggregation of the topological information in a DAG task by simultaneously considering predecessors and successors of each subtask. We further introduce non-uniform DAG neighborhood sampling through codifying the scheduling priority of different subtasks, which makes our developed GAT generalizable to completely unseen DAG task topologies. Finally, we augment GAT into a double deep Q-network learning module to conduct subtask-to-vehicle assignment according to the extracted features of subtasks, while considering the dynamics and heterogeneity of the vehicles in VCs. Through simulating various DAG tasks under real-world movement traces of vehicles, we demonstrate that GA-DRL outperforms existing benchmarks in terms of DAG task completion time.

Coded edge computing is envisioned as a promising solution to cope with the ever-increasing large-scale and computation-intensive mobile applications. Besides alleviating the computation straggling issue, task encoding in coded edge computing is also beneficial to the transmission of computation results. Nonetheless, existing pioneering works in this direction mainly take an information-theoretical perspective and assume the ideal scenarios of high signal-to-noise ratio. To the best of our knowledge, the issue of power control still remains largely unexplored for coded edge computing. In this work, two novel power control schemes are developed for coded edge computing in dynamic wireless environments, which apply to the repetition encoded task computing and the general linearly encoded task computing, respectively. However, the corresponding optimization problems turn out to be non-convex and highly non-trivial. To this end, by exploiting the underlying structural property, a novel partition-based iterative optimization method is developed to obtain the closed-form expression of the optimal dynamic power control strategy for repetition encoded task computing. For the case of more general linearly encoded task computing, the corresponding problem is transformed into a sum-of-ratio problem and then solved iteratively. Simulations are conducted to corroborate the effectiveness of the proposed schemes.

As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems. Given that the inference stage of GNNs can be naturally implemented in a decentralized manner, GNN is a potential enabler for decentralized control/management in the next-generation wireless communications. Privacy leakage, however, may occur due to the information exchanges among neighbors during decentralized inference with GNNs. To deal with this issue, in this paper, we analyze and enhance the privacy of decentralized inference with GNNs in wireless networks. Specifically, we adopt local differential privacy as the metric, and design novel privacy-preserving signals as well as privacy-guaranteed training algorithms to achieve privacy-preserving inference. We also define the SNR-privacy trade-off function to analyze the performance upper bound of decentralized inference with GNNs in wireless networks. To further enhance the communication and computation efficiency, we adopt the over-the-air computation technique and theoretically demonstrate its advantage in privacy preservation. Through extensive simulations on the synthetic graph data, we validate our theoretical analysis, verify the effectiveness of proposed privacy-preserving wireless signaling and privacy-guaranteed training algorithm, and offer some guidance on practical implementation.

Distributed edge computing has been advocated as a key enabling technology to tackle large-scale intelligence applications, which is however hampered by the straggling effect. To overcome straggling, coded edge computing emerges as a promising solution by creating judiciously designed redundant computations using coding theory. Nonetheless, existing transmission schemes for coded edge computing that make edge nodes (ENs) transmit independently are often sub-optimal, as the computation results are correlated due to coding redundancy. This entails a pressing need for more effective transmission for coded edge computing. With this consideration, a noveltask-decoding assisted cooperative transmissionscheme is proposed in this work to facilitate cooperative transmission in general coded edge computing settings. Specifically, by exploiting the structural relation among the encoded sub-tasks, a task-decoding mechanism is developed to enable ENs to reconstruct computation results ofallother ENs, so that they can cooperatively transmit withanyother EN by forming a virtual multi-antenna system. To characterize the delay performance of the proposed scheme, an analytic bound with closed-form expression is derived first, followed by a more accurate algorithmic bound for scenarios with a relatively small recovery threshold. Simulations are conducted to validate the effectiveness of the proposed scheme.

While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users’ devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth. Owing to these practical constraints and system models, this paper leverages model pruning and proposes a pruning-enabled hierarchical federated learning (PHFL) in heterogeneous networks (HetNets). We first derive an upper bound of the convergence rate that clearly demonstrates the impact of the model pruning and wireless communications between the clients and the associated BS. Then we jointly optimize the model pruning ratio, central processing unit (CPU) frequency and transmission power of the clients in order to minimize the controllable terms of the convergence bound under strict delay and energy constraints. However, since the original problem is not convex, we perform successive convex approximation (SCA) and jointly optimize the parameters for the relaxed convex problem. Through extensive simulation, we validate the effectiveness of our proposed PHFL algorithm in terms of test accuracy, wall clock time, energy consumption and bandwidth requirement.

Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data.However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios.Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency.The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance.In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance.This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., Fed-PepTAO, to enable efficient and effective FL of LLMs.First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously.Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further.Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8% in terms of accuracy) and efficiency (up to 97.59% in terms of training time) of FedPepTAO compared with 9 baseline approaches.Our code is available at https://github.com/llm-eff/FedPepTAO.

Decision-based black-box attacks often necessitate a large number of queries to craft an adversarial example. Moreover, decision-based attacks based on querying boundary points in the estimated normal vector direction often suffer from inefficiency and convergence issues. In this paper, we propose a novel query-efficient curvature-aware geometric decision-based black-box attack (CGBA) that conducts boundary search along a semicircular path on a restricted 2D plane to ensure finding a boundary point successfully irrespective of the boundary curvature. While the proposed CGBA attack can work effectively for an arbitrary decision boundary, it is particularly efficient in exploiting the low curvature to craft high-quality adversarial examples, which is widely seen and experimentally verified in commonly used classifiers under non-targeted attacks. In contrast, the decision boundaries often exhibit higher curvature under targeted attacks. Thus, we develop a new query-efficient variant, CGBA-H, that is adapted for the targeted attack. In addition, we further design an algorithm to obtain a better initial boundary point at the expense of some extra queries, which considerably enhances the performance of the targeted attack. Extensive experiments are conducted to evaluate the performance of our proposed methods against some well-known classifiers on the ImageNet and CIFAR10 datasets, demonstrating the superiority of CGBA and CGBA-H over state-of-the-art non-targeted and targeted attacks, respectively. The source code is available at https://github.com/Farhamdur/CGBA.

We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it remains an open problem whether such discrete-valued mechanisms provide any privacy protection. In this paper, we study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP). More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms, including the binomial noise and the binomial mechanisms that are proposed for privacy preservation, and the sign-based methods that are proposed for data compression, in closed-form expressions. We further investigate the amplification in privacy by sparsification and propose a ternary stochastic compressor. By leveraging compression for privacy amplification, we improve the existing methods by removing the dependency of accuracy (in terms of mean square error) on communication cost in the popular use case of distributed mean estimation, therefore breaking the three-way tradeoff between privacy, communication, and accuracy.

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT) networks. Information freshness in data collection can be characterized by the Age of Information (AoI). It is highly challenging to schedule multiple energy-constrained UAVs to improve information freshness especially when the generation instants of sensing samples are unpredictable. To deal with this issue, we leverage state-of-art reinforcement learning (RL) methods to design flight trajectories of UAVs without knowing the sampling mode each sensor node (SN) adopts. Each SN can sample the environment at periodical or random intervals. Multiple energy-constrained UAVs are dispatched to collect update packets from the SNs when flying over them. The UAV trajectory planning problem for AoI minimization is formulated as a Markov decision process (MDP). The objective is to minimize the average AoI of the SNs under the constraints of energy capacity and collision avoidance for the UAVs. Then, we propose two learning algorithms based on the Sarsa and value-decomposition network (VDN), respectively, which allow the UAVs to fulfill data collection tasks requested by the SNs. By learning directly from the environment, the Sarsa-based algorithm can approach the optimal policy asymptotically when certain conditions are satisfied. As one of the most popular multiagent deep RL methods, the VDN-based algorithm enables each UAV to make its own decision independently on its flight and data collection based on the partially observed network information. Simulation results validate the effectiveness of the proposed two learning-based algorithms compared with baseline policies.

Recent years have witnessed a renewed interest in reinforcement learning (RL) due to the rapid growth of the Internet of Things (IoT) and their associated intelligent information processing and decision-making demands. As the slow learning speed is one of the major stumbling blocks of the classic RL algorithms, substantial efforts have been devoted to developing faster RL algorithms. Among them, post-decision state (PDS) learning is a prominent one, which can often improve the learning speed by orders of magnitude by exploiting the structural property of the underlying Markov decision processes (MDPs). However, conventional PDS learning requires prior information about the PDS transition probability, which may not be always available in practice. To lift this limitation, a novel blind PDS (b-PDS) learning algorithm is proposed in this work by leveraging the generic two-timescale stochastic approximation framework. By introducing an extra estimating procedure about the PDS transition probability, b-PDS learning can achieve a similar improvement of learning speed as conventional PDS learning while excluding the need for prior information. In addition, by analyzing the globally asymptotically stable equilibrium of the corresponding ordinary differential equation (o.d.e.), the convergence and optimality of b-PDS learning are established. Moreover, extensive simulation results are provided to validate the effectiveness of the proposed algorithm. Over the considered random MDPs, it has been observed that, to reach 90% of the best possible time average reward, the proposed b-PDS learning can reduce the learning time by 70% compared to$Q$-learning and 30% compared to Dyna.

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles’ (CVs’) onboard central processing units (CPUs) and local datasets to train a global model. Convergence analysis reveals that the VEFL training loss depends on the successful receptions of the CVs’ trained models over the intermittent vehicle-to-infrastructure (V2I) wireless links. Owing to high mobility, in the full device participation case (FDPC), the edge server aggregates client model parameters based on a weighted combination according to the CVs’ dataset sizes and sojourn periods, while it selects a subset of CVs in the partial device participation case (PDPC). We then devise joint VEFL and radio access technology (RAT) parameters optimization problems under delay, energy and cost constraints to maximize the probability of successful reception of the locally trained models. Considering that the optimization problem is NP-hard, we decompose it into a VEFL parameter optimization sub-problem, given the estimated worst-case sojourn period, delay and energy expense, and an online RAT parameter optimization sub-problem. Finally, extensive simulations are conducted to validate the effectiveness of the proposed solutions with a practical 5G new radio (5G-NR) RAT under a realistic microscopic mobility model.

Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications. Different from other neural network models, GNN can be implemented in a decentralized manner during the inference stage with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems. The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN. To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN during the inference stage in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.

Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this article, we propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. The multi-job FL framework is composed of a system model and a scheduling method. The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process. We propose a novel intelligent scheduling approach based on multiple scheduling methods, including an original reinforcement learning-based scheduling method and an original Bayesian optimization-based scheduling method, which corresponds to a small cost while scheduling devices to multiple jobs. We conduct extensive experimentation with diverse jobs and datasets. The experimental results reveal that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 12.73 times faster) and accuracy (up to 46.4% higher).

This article investigates vehicular cloud (VC)-assisted task scheduling in an air-ground integrated vehicular network (AGVN), where tasks carried by unmanned aerial vehicles (UAVs) and resources of VCs are both modeled as graph structures. We consider a scenario in which resource-limited UAVs carry a set of computation-intensive graph tasks, which are offloaded to resource-abundant vehicles for processing. We formulate an optimization problem to jointly optimize the mapping between task components and vehicles, and transmission powers of UAVs, while addressing the trade-off between i) completion time of tasks, ii) energy consumption of UAVs, and iii) data exchange cost among vehicles. We show that this problem is a mixed-integer non-linear programming, and thus NP-hard. We subsequently reveal that satisfying constraints related to graph task structure requires addressing the non-trivial subgraph isomorphism problem over a dynamic vehicular topology. Accordingly, we propose a decoupling approach by segregating template searching from transmission power allocation, where atemplatedenotes a mapping between task components and vehicles. For template search, we introduce a low-complexity algorithm for isomorphic subgraphs extraction. For power allocation, we develop an algorithm using$p$-norm and convex optimization techniques. Extensive simulations demonstrate that our approach outperforms baseline methods in various network settings.

Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task processing delay by partitioning the original large-scale task into several small subtasks and offloading them to multiple edge nodes (ENs) for parallel computing. In edge computing, as the mobile user usually tends to offload computing tasks to closer ENs to save transmit power, the attacker may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on offloading related location privacy, they mainly focused on the scenario where each task can only be offloaded to a single EN, and may not be directly applicable to distributed edge computing. Besides, the privacy issues considered in existing works are mainly based on good heuristics, and there is still a lack of concrete examples of location privacy attacks in edge computing. To the best of our knowledge, the location privacy issue in distributed edge computing still remains largely unexplored in existing literature. With this consideration, a location inference attack based on matrix sequential probability ratio test (MSPRT) is identified in this work. Besides, a countermeasure based on dynamic multi-EN selection is proposed, together with a location privacy-aware and energy-efficient distributed offloading scheme based on the generic Lyapunov optimization framework. Both theoretic analysis and simulations based on real-world channel measurements are employed to validate the feasibility of the identified MPSRT attack and the effectiveness of the proposed defense scheme.

Recent years have witnessed a large amount of decentralized data in multiple (edge) devices of end-users, while the aggregation of the decentralized data remains difficult for machine learning jobs due to laws or regulations. Federated Learning (FL) emerges as an effective approach to handling decentralized data without sharing the sensitive raw data, while collaboratively training global machine learning models. The servers in FL need to select (and schedule) devices during the training process. However, the scheduling of devices for multiple jobs with FL remains a critical and open problem. In this paper, we propose a novel multi-job FL framework to enable the parallel training process of multiple jobs. The framework consists of a system model and two scheduling methods. In the system model, we propose a parallel training process of multiple jobs, and construct a cost model based on the training time and the data fairness of various devices during the training process of diverse jobs. We propose a reinforcement learning-based method and a Bayesian optimization-based method to schedule devices for multiple jobs while minimizing the cost. We conduct extensive experimentation with multiple jobs and datasets. The experimental results show that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 8.67 times faster) and accuracy (up to 44.6% higher).

Federated learning (FL) is a privacy-preserving paradigm where multiple participants jointly solve a machine learning problem without sharing raw data. Unlike traditional distributed learning, a unique characteristic of FL is statistical heterogeneity, namely, data distributions across participants are different from each other. Meanwhile, recent advances in the interpretation of neural networks have seen a wide use of neural tangent kernels (NTKs) for convergence analyses. In this paper, we propose a novel FL paradigm empowered by the NTK framework. The paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the conventional FL paradigms. Specifically, sample-wise Jacobian matrices, rather than model weights/gradients, are uploaded by participants. The server then constructs an empirical kernel matrix to update a global model without explicitly performing gradient descent. We further develop a variant with improved communication efficiency and enhanced privacy. Numerical results show that the proposed paradigm can achieve the same accuracy while reducing the number of communication rounds by an order of magnitude compared to federated averaging.

Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this paper, we propose a novel FL framework, i.e., FedDUAP, with two original contributions, to exploit the insensitive data on the server and the decentralized data in edge devices to further improve the training efficiency. First, a dynamic server update algorithm is designed to exploit the insensitive data on the server, in order to dynamically determine the optimal steps of the server update for improving the convergence and accuracy of the global model. Second, a layer-adaptive model pruning method is developed to perform unique pruning operations adapted to the different dimensions and importance of multiple layers, to achieve a good balance between efficiency and effectiveness. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller).

While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is urgently necessary to make it a reality. Motivated by this, we propose a novel mobility, communication and computation aware online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories, while the on-board processors can train machine learning models using the collected data. To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold. To satisfy this threshold, the central server accepts partially trained models, the distributed roadside units (a) perform downlink multicast beamforming to minimize global model distribution delay and (b) allocate optimal uplink radio resources to minimize local model offloading delay, and the vehicle agents conduct heterogeneous local model training. Using real-world vehicle trace datasets, we validate our FL solutions. Simulation shows that the proposed integrated FL platform is robust and outperforms baseline models. With reasonable local training episodes, it can effectively satisfy all constraints and deliver near ground truth multi-horizon velocity and vehicle-specific power predictions.

The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user flexibility by allowing bidders to submit bids for combinations of different items instead of only for individual items. However, the problem of allocating items among the bidders to maximize the auctioneers’ revenue, i.e., the winner determination problem (WDP), is NP-complete to solve and inapproximable. Existing works for WDPs are generally based on mathematical optimization techniques and most of them focus on the single-unit WDP, where each item only has one unit. On the contrary, few works consider the multi-unit WDP in which each item may have multiple units. Given that the multi-unit WDP is more complicated but prevalent in cloud computing, we propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss. Specifically, we model the multi-unit WDP as an augmented bipartite bid-item graph and use a graph neural network (GNN) with half-convolution operations to learn the probability of each bid belonging to the optimal allocation. To improve the sample generation efficiency and decrease the number of needed labeled instances, we propose two different sample generation processes. We also develop two novel graph-based post-processing algorithms to transform the outputs of the GNN into feasible solutions. Through simulations on both synthetic instances and a specific virtual machine (VM) allocation problem in a cloud computing platform, we validate that our proposed method can approach optimal performance with low complexity and has good generalization ability in terms of problem size and user-type distribution.

The dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs) foster the recent surge of interests in incorporating UAVs into edge computing systems to facilitate on-demand deployment and extended coverage. Nonetheless, due to the limited communication capability of the UAVs, single-UAV edge computing systems may still be incompetent when serving remote users. Although the traditional multi-UAV relay network can be a viable solution, it fails to exploit the computing capability of the UAVs. With this consideration, a multi-hop task offloading with on-the-fly computation scheme is proposed in this work to enable a more powerful multi-UAV remote edge computing network. To solve the corresponding joint resource allocation and deployment problem, two efficient algorithms are proposed. One of them can find the global optimal strategy in a special case, while the other can obtain a good local optimal strategy in the general cases. Both algorithms have a complexity only linear in the number of UAVs and admit distributed implementation. In addition to analysis, numerical results are provided to corroborate the effectiveness of the proposed scheme.

Vehicular cloud computing has been emerged as a promising solution to fulfill users’ demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users’ selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers’ utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as a 0-1 integer programming (0-1 IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the abovementioned 0-1 IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the contrast methods considering various problem sizes.

Federated learning has generated significant interest, with nearly all works focused on a “star” topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through thenetworkdimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra-and inter-layer model learning that considers the network as amulti-layer cluster-based structure.MH-FLconsiders thetopology structuresamong the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes asemi-decentralized architecturefor federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to formlocal consensuson the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence forMH-FLwith respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm forMH-FLto tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages ofMH-FLin terms of resource utilization metrics.

The rapid growth in scale and complexity of mobile applications fosters the development of the coded edge computing paradigm. By exploiting the redundancy in the encoded subtasks, coded edge computing enables collaborative transmission of multiple edge nodes and is promising for distributed computing in wireless fading environments. Nonetheless, to the best of our knowledge, due to challenges arising from the selection of the coding parameters, offloading strategy design for coded edge computing in general fading environments still remains open. With this consideration, the coded offloading problem is studied in this work and a delay-optimal coded offloading scheme is proposed. In particular, when the offloaded tasks are encoded by$(k,r)$linear codes, transmission diversity gains can be obtained by performing edge node selection to mitigate fading. However, the corresponding optimization problem turns out to be a highly non-trivial non-linear mixed-integer programming. To this end, through in-depth analysis based on order statistics, it is found that the average processing delay of the offloaded tasks admits a favorable$V$-structure with respect to the coding parameter$r$, under arbitrary fading distribution. This key theoretic result allows us to efficiently solve the original problem using monotonic optimization. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

In federated learning (FL), reducing the communication overhead is one of the most critical challenges since the parameter server and the mobile devices share the training parameters over wireless links. With such consideration, we adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity. In this work, only the parameter server side CSI is assumed, and channel capacity with outage is considered. In this case, an essential problem for the mobile devices is to select appropriate local processing and communication parameters (including the transmission rates) to achieve a desired balance between the overall learning performance and their energy consumption. Two optimization problems are formulated and solved, which optimize the learning performance given the energy consumption requirement, and vice versa. Furthermore, considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a stochastic sign-based algorithm is proposed. Extensive simulations are performed to demonstrate the effectiveness of the proposed methods.

We investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.

Dynamic Spectrum Access (DSA) is proposed to improve spectrum efficiency by enabling opportunistic access of underutilized spectrum resources. The key to successful DSA operations is the correct understanding of spectrum hole distributions. Though huge amounts of studies have been conducted on spectrum tenancy due to the significance of spectrum hole distributions, there are still two overlooked aspects. One is the measurement resolution, and the other is the spectrum distribution in the spectral perspective. Since the spectrum hole analysis relies on the measurement data, we decode the LTE downlink control information to obtain the spectrum tenancy at the same time-frequency granularity with LTE scheduling. We analyze the spectrum hole distributions in fine resolutions along both the temporal and the spectral dimensions, and investigate the performance of two widely used spectrum tenancy models, the Markov and the on/off models, in terms of their capabilities on capturing the distributions of spectrum holes. Our observations include but are not limited to the following. The spectrum holes follow the power law distributions when examined in the LTE scheduling unit from both the time and the frequency perspectives. Both Markov and on/off models should be fitted to the spectrum tenancy along the frequency perspective to achieve their best performance.

Physical layer security (PLS) techniques can help to protect wireless networks from eavesdropper attacks. In this paper, we consider the authentication technique that uses fingerprint embedding to defend 5G cellular networks with unmanned aerial vehicle (UAV) systems from eavesdroppers and intruders. Since the millimeter wave (mmWave) cellular networks use narrow and directional beams, PLS can take further advantage of the 3D spatial dimension for improving the authentication of UAV users. Considering a multi-user mmWave cellular network, we propose a power allocation technique that jointly takes into account splitting of the transmit power between the precoder and the authentication tag, which manages both the secrecy as well as the achievable rate. Our results show that we can obtain optimal achievable rate with expected secrecy.

This paper considers unmanned aerial vehicle (UAV)-assisted infrastructure-to-vehicle (I2V) communication employing UAVs as relays to increase the throughput between a roadside unit (RSU) and a vehicular user equipment (VUE). We investigate the UAV position planning problem under both single UAV and multiple cooperative UAVs scenarios while considering the mobility of the VUE, aiming to maximize the data rate of the system. We first consider using a single UAV and prove that the single UAV position planning can be formulated as a convex optimization problem, and then obtain the optimal position of the UAV. Next, we investigate the multiple cooperative UAVs scenario and formulate the joint power control and position planning problem to improve the data rate of the system under a fixed total power consumption. Numerical simulations are provided to verify our theoretical results. Our findings highlight the effects of important system parameters, such as height, transmit power, and the number of UAVs, on the optimal UAV positioning and system performance.

As the use of unmanned aerial vehicles (UAVs) in various commercial, civil, and military applications increases, it becomes important to study the design of aerial drone corridors that can support multiple simultaneous UAV missions. In this work, we study the placement of base stations (BSs) to serve aerial drone corridors while satisfying specific UAV mission requirements, such as the geometrical waypoints for the UAV to fly through and the minimum data rate to be supported along the mission trajectory. We develop a mathematical model of the drone corridor and propose a brute force algorithm that leverages A* search to meet the quality of service (QoS) requirements of the corridor by choosing the minimal set of BS locations from a pre-determined initial set. Using raytracing simulations, BS placement results are presented for various antenna array sizes in a dense urban region in East Manhattan. It was found that, for the scenario under consideration, a single BS equipped with an 8x8 antenna array is sufficient to satisfy the given QoS requirements of the corridor, while two BSs are required when using 4x4 antenna arrays.

Driven by the burgeoning development of unmanned aerial vehicle (UAV) technology, the recently advocated multi-UAV edge computing paradigm is anticipated to greatly enhance the coverage and on-demand deployment capability of the edge networks. One of the prominent advantage of this paradigm is to allow the UAVs to participate in the edge computing process by executing some computing tasks at their onboard processors. To this end, a key prerequisite is that the corresponding computing services must be placed onboard beforehand. Nonetheless, unlike its counterpart for conventional ground edge systems, the service placement issue in multi-UAV edge computing systems remains much less explored. To the best of our knowledge, this work is among the first to consider the joint service placement and resource allocation problem for multi-UAV edge computing. Due to the mutual influence between service placement and resource allocation, this problem turns out to be a computationally intractable mixed-integer nonlinear programming. Fortunately, through our analysis, it is found that this problem can be divided into two subproblems that are submodular and convex, respectively. Based on this observation and the general alternative optimization framework, an efficient joint service placement and resource allocation scheme that can find a reasonably good solution with only a linear complexity is proposed. In addition to the analysis, simulations are conducted to validate the effectiveness of the proposed scheme.

The contemporary literature on cloud resource allocation is mostly focused on studying the interactions between customers and cloud managers. Nevertheless, the recent growth in the customers’ demands and the emergence of private cloud providers (CPs) entice the cloud managers to rent extra resources from the CPs so as to handle their backlogged tasks and attract more customers. This also renders the interactions between the cloud managers and the CPs an important problem to study. In this paper, we investigate both interactions through a two-stage auction mechanism. For the interactions between customers and cloud managers, we adopt the options-based sequential auctions (OBSAs) to design the cloud resource allocation paradigm. As compared to existing works, our framework can handle customers with heterogeneous demands, provide truthfulness as the dominant strategy, enjoy a simple winner determination procedure, and preclude the delayed entrance issue. We also provide the performance analysis of the OBSAs, which is among the first in literature. Regarding the interactions between cloud managers and CPs, we propose two parallel markets for resource gathering, and capture the selfishness of the CPs by theiroffered prices. We conduct a comprehensive analysis of the two markets and identify the bidding strategies of the cloud managers.

The recent advances in wireless sensor networks and sensing techniques enable various time-sensitive applications that require timely exchange of updates between a Base Station (BS) and ground terminals. In practice, the ground terminals may not be able to communicate with the BS directly due to constraints in transmit power and communication capability, and mobile agents are commonly employed to help collect and deliver the updates. In particular, the emerging mobile crowd sensing (MCS) provides an appealing cost-effective paradigm for such employment. However, in this case, the mobile agents are required to share their locations with the ground terminals and the BS, which incurs location privacy concerns and may deter them from participating in the information delivery process. With this consideration, a location privacy-aware payment mechanism, which can stimulate the mobile agents to report their locations with differential privacy levels desired by the BS, is proposed. Furthermore, considering that the BS usually has a limited budget, it is essential to properly select the set of mobile agents to perform the information collection tasks. Therefore, a cost-efficient mobile agent selection algorithm is proposed. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.

In this article, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system. Explicitly, in the proposed algorithm, each node interacts with only part of its neighboring nodes, the number of which is progressively determined according to a heuristic searching procedure, which takes into account both the predicted convergence rate and the communication and computation costs at each iteration, resulting in a trade-off between communication and computation. Then the node chooses its neighboring nodes according to an importance sampling distribution derived theoretically to minimize the variance with the latest information it locally stores. Finally, the node updates its local information with a new update rule which adapts to the number of communication nodes. We prove the convergence of the proposed algorithm and provide an upper bound of the convergence variance brought by randomness. Extensive simulations validate the excellent performances of the proposed algorithm in terms of convergence rate and variance, the overall communication and computation cost, the impact of network topology as well as the time for evaluation, in comparison with the traditional counterparts.

Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing and transmission have to address jamming attacks with dynamic topology and limited energy. In this paper, we propose a reinforcement learning (RL)-based UAV anti-jamming video transmission scheme to choose the video compression quantization parameter, the channel coding rate, the modulation and power control strategies against jamming attacks. More specifically, this scheme applies RL to choose the UAV video compression and transmission policy based on the observed video task priority, the UAV-controller channel state and the received jamming power. This scheme enables the UAV to guarantee the video quality-of-experience (QoE) and reduce the energy consumption without relying on the jamming model or the video service model. A safe RL-based approach is further proposed, which uses deep learning to accelerate the UAV learning process and reduce the video transmission outage probability. The computational complexity is provided and the optimal utility of the UAV is derived and verified via simulations. Simulation results show that the proposed schemes significantly improve the video quality and reduce the transmission latency and energy consumption of the UAV compared with existing schemes.

In controller area networks (CANs), electronic control units (ECUs) such as telematics ECUs and on-board diagnostic ports must protect the message exchange from spoofing attacks. In this paper, we propose a CAN bus authentication framework that exploits physical layer features of the messages, including message arrival intervals and signal voltages, and applies reinforcement learning to choose the authentication mode and parameter. By applying the Dyna architecture and using a double estimator, this scheme improves the utility in terms of authentication accuracy without changing the CAN bus protocol or the ECU components and requiring knowledge of the spoofing model. We also propose a deep learning version to further improve the authentication efficiency for the CAN bus. The learning scheme applies a hierarchical structure to reduce the exploration time, and uses two deep neural networks to compress the high-dimensional state space and to fully exploit the physical authentication experiences. We provide the computational complexity and the performance analysis. Experimental results verify the theoretical analysis and show that our proposed schemes significantly improve the authentication accuracy as compared with benchmark schemes.

With the ease of deployment, capabilities of evading the jammers and obscuring their existence, unmanned aerial vehicles (UAVs) are one of the most suitable candidates to perform surveillance. There exists a body of literature in which the inspectors follow a deterministic trajectory to conduct surveillance, which results in a predictable environment for malicious entities. Thus, introducing randomness to the surveillance is of particular interest. In this work, we propose a novel framework for stochastic UAV-assisted surveillance that i) inherently considers the battery constraints of the UAVs, ii) proposes random moving patterns modeled via random walks, and iii) adds another degree of randomness to the system via considering probabilistic inspections. We formulate the problem of interest, i.e., obtaining the energy-efficient random walk and inspection policies of the UAVs subject to probabilistic constraints on inspection criteria of the sites and battery consumption of the UAVs, which turns out to be signomial programming that is highly non-convex. To solve it, we propose a centralized and a distributed algorithm along with their performance guarantee. This work contributes to both UAV-assisted surveillance and classic random walk literature by designing random walks with random inspection policies on weighted graphs with energy limited random walkers.

Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the area of wireless resource allocation. The main idea of GBD is decomposing an MINLP problem into a primal problem and a master problem, which are iteratively solved until their solutions converge. However, a direct implementation of GBD is time- and memory-consuming. The main bottleneck is the high complexity of the master problem, which increases over the iterations. Therefore, we propose to leverage machine learning (ML) techniques to accelerate GBD aiming at decreasing the complexity of the master problem. Specifically, we utilize two different ML techniques, classification and regression, to deal with this acceleration task. In this way, a cut classifier and a cut regressor are learned, respectively, to distinguish between useful and useless cuts. Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced. By using a resource allocation problem in device-to-device communication networks as an example, we validate that the proposed method can reduce the computational complexity of GBD without loss of optimality and has good generalization ability. The proposed method is applicable for solving various MINLP problems in wireless networks since the designs are invariant for different problems.

In this work, we study the UAV-enabled data collection problem for high information freshness in wireless sensor networks, where one UAV is dispatched to collect information of ground Sensor Nodes (SNs). The information freshness is measured by the Age of Information (AoI) of each SN, which is defined as the sum of the SN's data uploading time and the UAV's flight time after leaving this SN. Two optimization problems of age-optimal data collection are formulated to minimize the SNs' maximal AoI and average AoI, respectively. An iterative SN association and trajectory planning policy is proposed to seek the age-optimal solutions via an iterative two-step procedure. Firstly, SN association is performed based on the affinity propagation clustering method with an appropriate weight to find a set of data Collection Points (CPs) at which the UAV hovers to collect data and schedules which SNs to upload in what order. Based on this result, trajectory planning is performed to find the max-AoI-optimal and ave-AoI-optimal trajectories of the UAV along the CPs using dynamic programming or genetic algorithm. With the optimized clustering weight, the proposed scheme can always strike a balance between the SNs' uploading time and the UAV's flight time in various scenarios. Simulation results show that the proposed strategy can improve the freshness of information collected from all the SNs.

Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided$K$-best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a$24 \times 24$MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than 90% without any performance loss compared to conventional SD schemes. For a$32 \times 32$MIMO system with QPSK, the proposed FDL-KSD only requires$K = 32$to attain the performance of the conventional KSD with$K=256$, where$K$is the number of survival paths in KSD. This implies a dramatic improvement in the performance–complexity tradeoff of the proposed FDL-KSD scheme.

To address the demands for more capacity and higher data rates, the fifth generation network (5G) technology is being developed and gradually rolled out by major cellular carriers. Millimeter wave (mmWave) systems and unmanned aerial vehicles (UAVs) are two critical enablers of 5G. To ease the integration of 5G into existing networks, it is essential to study how base stations (BSs) can be used to serve both ground and aerial users simultaneously in a mmWave network. In this work, we consider BSs equipped with two antennas- one tilted down to serve ground users and another tilted up to serve aerial users. Using ray tracing simulations, we investigate the ideal tilt of these two antennas. Our results indicate that reusing the spectrum effectively in urban environments requires an understanding of the interplay between the effects of shadowing due to buildings, ground reflections, beam orientation, BS separation, interference between the two beams, and UAV heights. Specifically, to simultaneously serve UAVs at a height of 200 m and ground users, it is desirable to use a BS equipped with one antenna tilted up at 30 degrees and another tilted down at of 10 degrees. This achieves the best compromise between the above effects for the simulation configuration considered in this paper. Further, it was observed that the aerial and ground users are, in some scenarios, actually better served by the antenna not meant for them.

Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-1 label of the classifier. Our framework is based on the observation that the decision boundary of deep networks usually has a small mean curvature in the vicinity of data samples. We propose an effective iterative algorithm to generate query-efficient black-box perturbations with small p norms which is confirmed via experimental evaluations on state-of-the-art natural image classifiers. Moreover, for p=2, we theoretically show that our algorithm actually converges to the minimal perturbation when the curvature of the decision boundary is bounded. We also obtain the optimal distribution of the queries over the iterations of the algorithm. Finally, experimental results confirm that our principled black-box attack algorithm performs better than state-of-the-art algorithms as it generates smaller perturbations with a reduced number of queries.

Driven by the dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs), the recently advocated UAV edge computing paradigm is expected to enhance the coverage and the on-demand deployment capability of existing terrestrial edge computing systems. Nonetheless, due to the limited onboard resource of the UAV, single- UAV edge computing systems may still be incompetent when serving remote users. Although using multiple UAVs to form a traditional relay network is a viable solution to remote edge computing, it fails to exploit the computing capability of the UAVs. This entails a pressing need to develop multi-UAV remote edge computing mechanisms that allow the UAVs to handle part of the computation tasks using their local processors while conducting multi-hop computation task offloading. To achieve the best performance in such cases, the UAVs have to properly split their power budget for communication and computation and also move to suitable service locations. Nonetheless, finding the optimal UAV power allocation and deployment turns out to be an intractable high-dimensional monotonic optimization problem, even for a mild number of UAVs. To overcome this challenge, a more efficient algorithm that has a complexity only linear in the number of UAVs is developed by exploiting the special structure of this problem. In addition to analysis, numerical results are provided to validate the effectiveness of the proposed scheme.

Recent advances in social media and information technology have enabled much faster dissemination of information, while at the same time raise concerns about privacy leakage after various privacy breaches. Therefore, the privacy guarantees of information dissemination protocols have attracted increasing research interests, among which the gossip protocols assume vital importance in various information exchange applications. Very recently, the rigorous framework of differential privacy has been introduced to measure the privacy guarantees of gossip protocols in the simplified complete network scenario. In this work, we extend the study to general networks. First, lower bounds of the differential privacy guarantees are derived for the gossip protocols in general networks in both synchronous and asynchronous settings. The prediction uncertainty of the source node given a uniform prior is also determined. It is found that source anonymity is closely related to some key network structure parameters in the general network setting. Then, we investigate information spreading in wireless networks with unreliable communications, and quantity the tradeoff between differential privacy guarantees and information spreading efficiency. Finally, considering that the attacker may not be present in the beginning of the information dissemination process, the scenario of delayed monitoring is studied and the corresponding differential privacy guarantees are evaluated.

The integration of unmanned aerial vehicles (UAVs) into the terrestrial communications networks with a variety of tasks is viewed as a key technology for 5G and beyond. In this work, we consider the uplink millimeter-wave (mmWave) transmission between a set of UAVs and a base station (BS), where the UAVs deploy uplink non-orthogonal multiple access (NOMA) in multiple clusters. Furthermore, the BS also serves its own desired ground user equipment (UE) in the presence of many other ground UEs associated with other cells, which share the same frequency band. Considering the limited energy budget of UAVs, we formulate an energy efficiency (EE) problem, and propose a solution aided by the Dinkelbach's algorithm and successive convex approximation (SCA). Using realistic air-to-ground (A2G) and terrestrial channel models, we assess the performance of the proposed algorithm under various circumstances (maximum transmit power for UAVs, quality-of-service (QoS) constraint for the desired UE, etc.), and identify the best use cases.

We consider the relaying application of unmanned aerial vehicles (UAVs), in which UAVs are placed between two transceivers (TRs) to increase the throughput of the system. Instead of studying the placement of UAVs as pursued in existing literature, we focus on investigating the placement of a jammer or a major source of interference on the ground to effectively degrade the performance of the system, which is measured by the maximum achievable data rate of transmission between the TRs. We demonstrate that the optimal placement of the jammer is in general a non-convex optimization problem, for which obtaining the solution directly is intractable. Afterward, using the inherent characteristics of the signal-to-interference ratio (SIR) expressions, we propose a tractable approach to find the optimal position of the jammer. Based on the proposed approach, we investigate the optimal positioning of the jammer in both dual-hop and multi-hop UAV relaying settings. Numerical simulations are provided to evaluate the performance of our proposed method.

Vehicular cloud computing has emerged as a promising paradigm for fulfilling user requirements in computation-intensive tasks in modern driving environments. In this paper, a novel framework of multi-task offloading over vehicular clouds (VCs) is introduced where tasks and VCs along with their internal connections are modeled as undirected weighted graphs. Aiming to achieve a trade-off between minimizing task completion time and data exchange costs, task components are efficiently mapped to available virtual machines in the related VCs. The problem is formulated as a non-linear integer programming problem, mainly under constraints of limited contact between vehicles as well as available resources, and addressed considering different problem sizes. In small size scenarios with a couple of tasks and service providers in a VC, we determine optimal solutions; in larger size cases, a connection-restricted random-matching-based subgraph isomorphism algorithm is proposed that presents low computational complexity. Evaluation of the proposed algorithms against greedy-based baseline methods is conducted via extensive simulations.

Graph jobs represent a wide variety of computation-intensive tasks in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components). Recent years have witnessed dramatic growth in smart vehicles and computation-intensive graph jobs, which pose new challenges to the provision of efficient services related to the Internet of Vehicles. Fortunately, vehicular clouds (VCs) formed by a collection of vehicles, which allows jobs to be offloaded among vehicles, can substantially alleviate heavy onboard workloads and enable on-demand provisioning of computational resources. In this article, we present a novel framework for VCs that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over VCs is formulated as a nonlinear integer programming with respect to vehicles' contact duration and available resources, aiming to minimize the job completion time and data exchange cost. The problem is addressed for two scenarios: 1) low-traffic and 2) rush-hour scenarios. For the former, we determine the optimal solutions for the problem. In the latter case, given the intractable computations for deriving feasible allocations, we propose a novel low complexity randomized graph job allocation mechanism by considering hierarchical tree-based subgraph isomorphism extraction. The evaluation of the performance of both optimal and proposed randomized algorithms with two greedy-based baseline methods is carried out through extensive simulations.

In the era of big-data, the jobs submitted to the clouds exhibit complicated structures represented by graphs, where the nodes denote the sub-tasks each of which can be accommodated at a slot in a server, while the edges indicate the communication constraints among the sub-tasks. We develop a framework for efficient allocation of graph jobs in geo-distributed cloud networks (GDCNs), explicitly considering the power consumption of the datacenters (DCs). We address the following two challenges arising in graph job allocation: i) the allocation problem belongs to NP-hard nonlinear integer programming; ii) the allocation requires solving the NP-complete sub-graph isomorphism problem, which is particularly cumbersome in large-scale GDCNs. We develop a suite of efficient solutions for GDCNs of various scales. For small-scale GDCNs, we propose an analytical approach based on convex programming. For medium-scale GDCNs, we develop a distributed allocation algorithm exploiting the processing power of DCs in parallel. Afterward, we provide a novel low-complexity (decentralized) sub-graph extraction method, based on which we introduce cloud crawlers aiming to extract allocations of good potentials for large-scale GDCNs. Given these suggested strategies, we further investigate strategy selection under both fixed and adaptive DC pricing schemes, and propose an online learning algorithm for each.

The wireless offloading feature of the recently advocated mobile-edge computing (MEC) imposes a risk of disclosing private user data to eavesdroppers. Physical-layer security approaches that are built on information theoretic methods can be applied to defend eavesdropping in MEC. Nonetheless, directly incorporating existing physical-layer security technique may introduce extra energy and delay costs to the resource-limited mobile device and thus substantially disrupt the users' offloading decisions. To fulfill effective secure offloading in MEC, there is a compelling need to properly optimize existing physical-layer security techniques and develop new offloading schemes accordingly. With this consideration, a novel physical-layer assisted secure offloading scheme is proposed in this work, in which the edge server proactively broadcasts jamming signals to impede eavesdropping and leverages full-duplex communication technique to effectively suppress the self-interference. Finding the optimal jamming signal and the corresponding optimal offloading ratio turns out to be a challenging bilevel optimization problem. The special structure of the secure offloading problem is exploited to develop efficient offloading algorithms. Numerical results are presented to validate the effectiveness of the proposed scheme.

The limited information processing capability and battery life of mobile devices is becoming a bottleneck in delivering more advanced and high-quality services to the customers. To address this problem, the recently advocated mobile-edge computing (MEC) architecture is promising, where the essential idea is to bring the computation resource to the network edge and allow users to wirelessly offload resource demanding computation tasks to the nearby MEC servers for potentially faster execution and lower battery consumption. Nonetheless, the existing understanding of the privacy aspect of MEC is still far from complete. In this work, a user presence inference attack that invades user privacy by exploiting the feature tasks offloaded from users is identified for MEC. Existing privacy-preserving techniques developed for other applications cannot be applied to defeat this attack in MEC, as they may disrupt the optimal task offloading scheduling and cause severe degradation in user experience. With this consideration, a novel privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user privacy while still ensure the best possible user experience is developed in this work based on the generic Lyapunov optimization framework. The effectiveness of the proposed scheme is validated through both analysis and simulations.

We consider unmanned aerial vehicle (UAV)-assisted wireless communication employing UAVs as relays to increase the throughput between a pair of transmitter and receiver. We focus on developing effective methods to position the UAV(s) in the presence of interference in the environment, the existence of which makes the problem non-trivial and our methodology different from the current art. We study the optimal position planning, which aims to maximize the (average) signal-to-interference-ratio (SIR) of the system, in the presence of: i) one major source of interference, ii) stochastic interference. For each scenario, we first consider utilizing a single UAV in the dual-hop relay mode and determine its optimal position. Afterward, multiple UAVs in the multi-hop relay mode are considered, for which we investigate two novel problems concerned with determining the optimal number of required UAVs and developing an optimal distributed position alignment method. Subsequently, we propose a cost-effective method that simultaneously minimizes the number of UAVs and determines their optimal positions to guarantee a certain (average) SIR of the system. Alternatively, for a given number of UAVs, we develop a fully distributed placement algorithm along with its performance guarantee. Numerical simulations are provided to evaluate the performance of our proposed methods.

The spectrum-efficient millimeter-wave (mmWave) communications has recently attracted much attention as a viable solution to spectrum crunch problem. In this work, we propose a novel non-orthogonal multiple access (NOMA) framework, which makes use of the directional propagation characteristics of mmWave communications so as to improve the spectral efficiency through non-orthogonal signaling. In particular, we consider one-bit quantized angle information as a limited yet effective feedback scheme describing the channel quality of user equipment (UE) in mmWave bands. The UE pairs for NOMA transmission are then established using not only the one-bit distance feedback as a classical approach, but also the one-bit angle feedback. The proposed strategy is therefore referred to as two-bit NOMA. We also propose a novel hybrid strategy, called combined NOMA, for the circumstances with no UE pair through two-bit NOMA. Whenever no UE pair is available through any NOMA strategy, we resort to single user transmission (SUT) with proper UE selection schemes. The hybrid outage sum-rate performance is also analyzed thoroughly with the respective outage and rate expressions. The numerical results verify that the proposed strategy outperforms one-bit NOMA schemes with either angle- or distance-only feedback, and has a very close outage sum-rate performance to that for the optimal full-resolution feedback.

Unmanned aerial vehicles (UAVs) are widely utilized to capture and compress videos of the target area and then transmit the processed videos to the control station (CS) on the ground. The media transmissions in the UAV-aided network face many challenges due to the highly dynamic network topology and limited resources such as bandwidth and energy. This paper introduces a media transmission scheme in the UAV-aided network utilizing reinforcement learning algorithms to efficiently process and transmit the captured video, which is able to improve the quality-of-experience (QoE) and reduce the energy consumption. Exploiting the proposed reinforcement learning algorithm, the UAV dynamically selects the quantization parameter in the source coding process and determines the transmit power without knowing the video transmission model. Simulation results demonstrate that the proposed scheme is capable of achieving a higher video quality and utility with lower energy consumption compared with the state-of-the-art schemes.

The deployment of the unmanned aerial vehicle (UAV) has been foreseen as a promising technology for the next generation communication networks. The distance limitation imposed by the line of sight RF connection can be removed by using RF coverage from existing commercial cellular service. In this work, we consider a transmission mechanism that aims to improve the data rate between a terrestrial base station (BS) and user equipment (UE) through deploying multiple UAVs relaying the desired data flow. Considering the coexistence of this network with other established communication networks, we take into account the effect of interference, which is incurred by the existing nodes. Our primary goal is to optimize the three-dimensional (3D) trajectories and power allocation for the relaying UAVs to maximize the data flow while keeping the interference to existing nodes below a predefined threshold. An alternating-maximization strategy is proposed to solve the joint 3D trajectory design and power allocation for the relaying UAVs. To this end, we handle the information exchange within the network by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. Simulation results show that our approach can considerably improve the information flow while the interference threshold constraint is met.

As compared to the conventional cloud computing, the wireless offloading feature of the recently advocated mobile-edge computing (MEC) imposes a new risk of disclosing possibly private and sensitive user data to eavesdroppers. Physical-layer security approaches built on information theoretic methods are believed to provide a stronger notion of privacy than cryptography, and therefore, may be more suitable for defending eavesdropping in MEC. Nonetheless, incorporating a physical-layer security technique may fundamentally change the mobile users' offloading decisions. This suggests a compelling need for new judiciously designed offloading schemes that can jointly reap the benefits of both physical-layer security and MEC. With this consideration, a novel physical-layer assisted privacy-preserving offloading scheme is proposed in this work, in which the edge server proactively broadcasts jamming signals to impede eavesdropping and leverages full-duplex communication technique to effectively suppress the self-interference. Finding the optimal jamming power of the edge server and the corresponding optimal offloading ratio of the mobile user turns out to be a challenging bilevel optimization problem. By exploiting the structure of the considered problem, two efficient algorithms are developed for delay optimal and energy optimal privacy-preserving offloading, respectively. Numerical results are presented to validate the effectiveness of the proposed schemes.

We consider unmanned aerial vehicle (UAV)-assisted wireless communication employing UAVs as relay nodes to increase the throughput between a pair of transmitter and receiver. We focus on developing effective methods to position the UAV(s) in the sky in the presence of a major source of interference, the existence of which makes the problem non-trivial. First, we consider utilizing a single UAV, for which we develop a theoretical framework to determine its optimal position aiming to maximize the SIR of the system. To this end, we investigate the problem for three practical scenarios, in which the position of the UAV is: (i) vertically fixed, horizontally adjustable; (ii) horizontally fixed, vertically adjustable; (iii) both horizontally and vertically adjustable. Afterward, we consider employing multiple UAVs, for which we propose a cost-effective method that simultaneously minimizes the number of required UAVs and determines their optimal positions so as to guarantee a certain SIR of the system. We further develop a distributed placement algorithm, which can increase the SIR of the system given an arbitrary number of UAVs. Numerical simulations are provided to evaluate the performance of our proposed methods.

The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources. As a promising tool to efficiently extract useful information from such big data, machine learning has been pushed to the forefront and seen great success in a wide range of relevant areas such as computer vision, health care, and financial market analysis. To accommodate the large volume of data, there is a surge of interest in the design of distributed machine learning, among which stochastic gradient descent (SGD) is one of the mostly adopted methods. Nonetheless, distributed machine learning methods may be vulnerable to Byzantine attack, in which the adversary can deliberately share falsified information to disrupt the intended machine learning procedures. In this work, two asynchronous Byzantine tolerant SGD algorithms are proposed, in which the honest collaborative workers are assumed to store the model parameters derived from their own local data and use them as the ground truth. The proposed algorithms can deal with an arbitrary number of Byzantine attackers and are provably convergent. Simulation results based on a real-world dataset are presented to verify the theoretical results and demonstrate the effectiveness of the proposed algorithms.

RF-powered backscatter communication is a promising new technology that can be deployed for battery-free applications such as internet of things (IoT) and wireless sensor networks (WSN). However, since this kind of communication is based on the ambient RF signals and battery-free devices, they are vulnerable to interference and jamming. In this paper, we model the interaction between the user and a smart interferer in an ambient backscatter communication network as a game. We design the utility functions of both the user and interferer in which the backscattering time is taken into the account. The convexity of both sub-game optimization problems is proved and the closed-form expression for the equilibrium of the Stackelberg game is obtained. Due to lack of information about the system SNR and transmission strategy of the interferer, the optimal strategy is obtained using the Q-learning algorithm in a dynamic iterative manner. We further introduce hotbooting Q-learning as an effective approach to expedite the convergence of the traditional Q-learning. Simulation results show that our approach can obtain considerable performance improvement in comparison to random and fixed backscattering time transmission strategies and improves the convergence speed of Q-Learning by about 31%.

Vehicular ad-hoc networks (VANETs) have recently attracted a lot of attention due to their immense potentials and applications. Wide range of coverage and accessibility to end users make VANETs a good target for commercial companies. In this paper, we consider a scenario in which advertising companies aim to disseminate their advertisements in different areas of a city by utilizing VANETs infrastructure. These companies compete for renting the VANETs infrastructure to spread their advertisements. We partition the city map into different blocks, and consider a manager for all the blocks who is in charge of splitting the time between interested advertising companies. Each advertising company (AdC) is charged proportional to the allocated time. In order to find the best time splitting between AdCs, we propose a Stackelberg game scheme in which the block manager assigns the companies to the blocks and imposes the renting prices to different companies in order to maximize its own profit. Based on this, AdCs request the amount of time they desire to rent the infrastructure in order to maximize their utilities. To obtain the Stackelberg equilibrium of the game, a mixed integer nonlinear optimization problem is solved using the proposed optimal and sub-optimal algorithms. The simulation results demonstrate that the sub-optimal algorithm approaches the optimal one in performance with lower complexity.

Location-based services in vehicular ad hoc networks (VANETs) have to protect user privacy and address the challenge due to the disclosure of the vehicle movement trajectory. In this paper, we propose an reinforcement learning (RL) based differential privacy mechanism that randomizes the released vehicle locations to protect the semantic trajectory of the vehicle and uses RL to select the obfuscation policy. Based on the semantic location of the vehicle and the attack history, this scheme enables a vehicle to optimize the obfuscation policy in terms of the privacy gain and the quality of service loss without being aware of the current attack model in a dynamic privacy protection game. Simulation results show that this scheme can increase the privacy gain, decrease the quality of service loss, and thus improve the utility of the vehicle in comparison with a benchmark scheme.

Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.

The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set of influential nodes for fast content dissemination in a social network. In this paper, we propose a novel approach to solve the problem, where opinion maximization is achieved through efficient information spreading. In our model, multiple sources inject information continuously into the network, while the regular nodes with heterogeneous social learning abilities spread the information to their acquaintances through gossip mechanism. One of the sources employs smart information spreading and the rest spread information randomly. We model the social interactions and evolution of opinions as a dynamic Bayesian network (DBN), using which the opinion maximization is formulated as a sequential decision problem. Since the problem is intractable, we develop multiple variants of centralized and decentralized algorithms to obtain approximate solutions. Through simulations in synthetic and real-world networks, we demonstrate two key results: 1) the proposed methods perform better than random spreading by a large margin, and 2) even though the smart source (that spreads the desired content) is unfavorably located in the network, it can outperform the contending random sources located at favorable positions.

Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.

Using radio frequency (RF) coverage from the existing cellular networks is an attractive option to maintain beyond visual line-of-sight (BVLOS) connectivity with drones. These cellular drones can connect with a ground control station (GCS) for control and data delivery wherever cellular service is available. However, RF coverage for these cellular networks has been optimized for the ground users and while they do leak upwards, reliable RF coverage exists to only about 400 feet high. As the drones are becoming more commonly used for activities ranging from emergency responses to aerial deliveries, the base stations covering users both on the ground and in the air with RF transmission cannot scale to the capacity needed to support increasing number of drones. Use of directional beams that are necessary for millimeter wave (mmWave) transmission in the 5G cellular system can reduce interference among users, and hence increase capacity compared to sector based transmissions used for 4G systems without massive MIMO (mMIMO). In this paper, considering the multiplicative capacity gains needed to support large number of drones, we propose a separate mmWave cellular network with optimized coverage for a ''drone corridor'' in the air. We present our current findings in the following areas critical to validating the effectiveness of this proposed network: 1) RF propagation characteristics towards drones in the air compared with propagation towards users on the ground; 2) use of multiple access (MA) technology for increased spectral efficiency for a swarm of drones; 3) optimal design of protected zones and beamforming to secure drone specific wireless communications.

The rapid uptake of Internet-of-Things (IoT) devices imposes an unprecedented pressure for data communication and processing on the backbone network and the central cloud infrastructure. To overcome this issue, the recently advocated mobile-edge computing (MEC)-enabled IoT is promising. Meanwhile, driven by the growing social awareness of privacy, significant research efforts have been devoted to relevant issues in IoT; however, most of them mainly focus on the conventional cloud-based IoT. In this paper, a new privacy vulnerability caused by the wireless offloading feature of MEC-enabled IoT is identified. To address this vulnerability, an effective privacy-aware offloading scheme is developed based on a newly proposed deep post-decision state (PDS)-learning algorithm. By exploiting extra prior information, the proposed deep PDS-learning algorithm allows the IoT devices to learn a good privacy-aware offloading strategy much faster than the conventional deep Q-network. Theoretic analysis and numerical results are provided to corroborate the correctness and the effectiveness of the proposed algorithm.

The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a Vickrey-Clarke-Groves based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory suboptimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios.

Mobile edge computing helps healthcare Internet of Things (IoT) devices with energy harvesting provide satisfactory quality of experiences for computation intensive applications. We propose a reinforcement learning (RL)-based privacy-aware offloading scheme to help healthcare IoT devices protect both the user location privacy and the usage pattern privacy. More specifically, this scheme enables a healthcare IoT device to choose the offloading rate that improves the computation performance, protects user privacy, and saves the energy of the IoT device without being aware of the privacy leakage, IoT energy consumption, and edge computation model. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. A post-decision state learning method uses the known channel state model to further improve the offloading performance. We provide the performance bound of this scheme regarding the privacy level, the energy consumption, and the computation latency for three typical healthcare IoT offloading scenarios. Simulation results show that this scheme can reduce the computation latency, save the energy consumption, and improve the privacy level of the healthcare IoT device compared with the benchmark scheme.

The inherent broadcast characteristics of the visible light communication (VLC) channel makes VLC downlinks susceptible to unauthorized terminals in many actual VLC scenarios, such as offices and shopping centers. This paper considers a multiple-input-single-output (MISO) VLC scenario with multiple light fixtures acting as the transmitter, a VLC receiver as the legitimate user, and an eavesdropper attempting to intercept the undisclosed information. To improve the confidentiality of VLC links, a physical-layer anti-eavesdropping framework is proposed to obscure the unauthorized eavesdroppers and diminishes their capability of inferring the information through smart beamforming over the MISO VLC wiretap channel. To cope with the intractable problem of finding the theoretically optimal solution of the secrecy rate and utility for the MISO VLC wiretapping channel, a reinforcement learning (RL)-based VLC beamforming control scheme is proposed to achieve the optimal beamforming policy against the eavesdropper. Furthermore, a deep RL-based VLC beamforming control scheme is proposed to handle the curse of dimensionality for both observation space and action space and avoid the quantization error of the RL-based algorithm. Simulation results show that the proposed learning-based VLC beamforming control schemes can significantly decrease the bit error rate of the legitimate receiver and increase the secrecy rate and utility of the anti-eavesdropping MISO VLC system, compared with the benchmark strategy.

To contest the rapidly developing cyber-attacks, numerous collaborative security schemes, in which multiple security entities can exchange their observations and other relevant data to achieve more effective security decisions, are proposed and developed in the literature. However, the security-related information shared among the security entities may contain some sensitive information and such information exchange can raise privacy concerns, especially when these entities belong to different organizations. With such consideration, the interplay between the attacker and the collaborative entities is formulated as Quantitative Information Flow (QIF) games, in which the QIF theory is adapted to measure the collaboration gain and the privacy loss of the entities in the information sharing process. In particular, three games are considered, each corresponding to one possible scenario of interest in practice. Based on the game-theoretic analysis, the expected behaviors of both the attacker and the security entities are obtained. In addition, the simulation results are presented to validate the analysis.

We study the optimal design of interdependent networks against cascading failures with node protections. Different from existing literature which only considers the interdependency link design, we also take node protection strategy into account, where the attacked nodes have a fixed probability of surviving. The system designer is armed with finite protection resources and wants to maximize the number of survival nodes after initial attacks and subsequent cascading failures. Based on well-adopted analytical models for cascading networks, we propose optimal and near-optimal interdependent network designs for several node protection strategies. The one-to-one structure with complete interdependency and partial interdependency, together with the one-to-many structure, are considered. Numerical simulations are carried out to support the theoretical results.

While providing unprecedented convenience to people's daily life, location-based services (LBSs) may cause serious concerns on users' location privacy, when the system is compromised. Although various location privacy protection mechanisms have been developed for LBSs, the ambient physical environment often imposes some fundamental limitations on their performances. As a result, mobile users may experience a spatial diversity in the achievable location privacy when traveling along their routes. However, to the best of our knowledge, an appropriate location privacy metric that can capture the influence of the ambient environment is still missing in the literature. Also, none of the existing location privacy protection methods can properly leverage such spatial diversity. With this consideration, new ambient environment-dependent location privacy metrics are proposed in this paper, together with a stochastic model that can capture their spatial variations along the user's route. Based on this modeling, a new optimal stopping-based LBS access scheme that allows mobile users to fully leverage the spatial diversity and achieve a substantially better performance is developed. The effectiveness of the proposed scheme is corroborated by both numerical results and simulations over real-world road maps.

Mobile crowdsensing (MCS) is vulnerable to faked sensing attacks, as selfish smartphone users sometimes provide faked sensing results to the MCS server to save their sensing costs and avoid privacy leakage. In this paper, the interactions between an MCS server and a number of smartphone users are formulated as a Stackelberg game, in which the server as the leader first determines and broadcasts its payment policy for each sensing accuracy. Each user as a follower chooses the sensing effort and thus the sensing accuracy afterward to receive the payment based on the payment policy and the sensing accuracy estimated by the server. The Stackelberg equilibria of the secure MCS game are presented, disclosing conditions to motivate accurate sensing. Without knowing the smartphone sensing models in a dynamic version of the MCS game, an MCS system can apply deep Q-network (DQN), which is a deep reinforcement learning technique combining reinforcement learning and deep learning techniques, to derive the optimal MCS policy against faked sensing attacks. The DQN-based MCS system uses a deep convolutional neural network to accelerate the learning process with a high-dimensional state space and action set, and thus improve the MCS performance against selfish users. Simulation results show that the proposed MCS system stimulates high-quality sensing services and suppresses faked sensing attacks, compared with a Q-learning-based MCS system.

Recently, mobile-edge computing (MEC) emerges as a promising paradigm to enable computation intensive and delay-sensitive applications at resource limited mobile devices by allowing them to offload their heavy computation tasks to nearby MEC servers through wireless communications. A substantial body of literature is devoted to developing efficient scheduling algorithms that can adapt to the dynamics of both the system and the ambient wireless environments. However, the influence of these task offloading schemes to the mobile users' privacy is largely ignored. In this work, two potential privacy issues induced by the wireless task offloading feature of MEC, location privacy and usage pattern privacy, are identified. To address these two privacy issues, a constrained Markov decision process (CMDP) based privacy-aware task offloading scheduling algorithm is proposed, which allows the mobile device to achieve the best possible delay and energy consumption performance while maintain a pre-specified level of privacy. Numerical results are presented to corroborate the effectiveness of the proposed algorithm.

Vehicular ad-hoc networks (VANETs) has recently attracted a lot of attention due to their great potentials for different applications such as collision avoidance, route finding and autonomous driving. A wide range of coverage and accessibility to the end users in VANETs make them a good target for commercial advertising. This paper addresses the problem of mobile advertising in VANETs. We consider a case where different advertisers compete for the VANET infrastructure. It is assumed that a city is partitioned into a grid of blocks and the central data center manager (CDM) sets the rental price for each block considering the geographical position and the predicted density of vehicles inside the block. The regret-based minimization method is adopted to tackle the problem in response to its dynamic nature. Regret bound of the proposed algorithm and its convergence to the best strategy are shown rigorously. Furthermore, a good potential of the proposed algorithm is revealed through simulations.

Determining the causality of abnormalities in a network is the prerequisite for developing countermeasures. In this paper, we focus on infection detection in heterogeneous networks. Given a snapshot of the network which demonstrates the condition of the nodes, the goal is to distinguish between random failures and epidemic scenarios. We model the network situation as a graph signal based on the nodes' status. Detection metrics motivated by graph signal processing are introduced for the infection detection problem in hand, and an effective algorithm is proposed to solve it. Simulation results indicate a dramatic improvement in terms of detection probability compared to the current state-of-the-art.

Vehicular ad-hoc networks (VANETs) have been an active area in the research community during the last decade with focus primarily on routing protocols, security aspects and safety. Recent advances in wireless communication and the inherent dynamic nature of VANETs provide excellent opportunity for advertisement dissemination. In this paper, we address the problem of dynamic advertising in VANETs. We consider a city divided into a grid, where the blocks have different vehicular densities that vary over time. Several advertising companies compete for the blocks to broadcast their advertisements in the network. The content dissemination in the network is controlled by a data management unit that receives requests from advertising companies for each block. To solve the problem of block allocation, we adapt the repeated auction scheme for the dynamic setting. Two new metrics are defined to better represent the real- world scenario and fairness in repeated auctions. We propose an algorithm which is a combination of adaptive linear prediction and nonparametric Bayesian belief update, enabling smart bidding and improving the utilities of the competing advertising companies significantly in the long- run. Through simulations, we show that the proposed algorithm achieves better performance than two baselines approaches.

In this work, we focus on the problem of obtaining the optimal interlink structures, which maximizes the robustness of networks against random node failures, in a cost constrained setting. Using percolation theory based system equations, we have formulated our objective as a constrained optimization problem and designed algorithms serving two key purposes: i) obtaining the budget limits, Bland Bu, defined as the minimum budget guaranteeing the existence of a feasible and optimal interlink structure, respectively; and ii) obtaining interlink structures for intermediate budgets. Through these algorithms and associated simulation results, we demonstrate the importance of cost in network design. Furthermore, the designed algorithms have close to optimal performance while being much cheaper than cost agnostic network designs.

Deception has been widely considered in literature as an effective means of enhancing security protection when the defender holds some private information about the ongoing rivalry unknown to the attacker. However, most of the existing works on deception assume static environments and thus consider only myopic deception, while practical security games between the defender and the attacker may happen in dynamic scenarios. To better exploit the defender's private information in dynamic environments and improve security performance, a stochastic deception game (SDG) framework is developed in this work to enable the defender to conduct foresighted deception. To solve the proposed SDG, a new iterative algorithm that is provably convergent is developed. A corresponding learning algorithm is developed as well to facilitate the defender in conducting foresighted deception in unknown dynamic environments. Numerical results show that the proposed foresighted deception can offer a substantial performance improvement as compared to the conventional myopic deception.

With growing demands for cloud computing services, the idea of managing limited cloud resources for making a profit has arisen as an important problem. Auction theory is recently considered as a viable way to solve the problem of cloud resource allocation. In this paper, we consider a model for Cloud of Clouds Networks (CCNs) with different types of servers along with customers with heterogeneous demands, in which customers and cloud servers may join and leave the CCN at will. We propose an options-based sequential auction that not only provides a good match with the dynamic structure of the problem, but also solves the entrance time problem and possesses the truthfulness property. We study both first-price and second-price options-based sequential auctions, and model the price matching processes in those auctions as Markov chains. We provide mathematically tractable methods to find the expected value of the CCN manager's revenue, and further show how the proxy agents' patience time affects the CCN manager's revenue.

In this work, we investigate information spreading in multiplex networks, adopting the gossip (random-walk) based model. Two key features of multiplex networks allow potentially much faster information spreading: availability of multiple channels and communication actions for each user, and more choices on neighbor contacting. As a first work in this area, we explore the impact of layer number, layer similarity, and average node degree on the efficiency of information spreading, and theoretically prove our results. Another observation is that multiplex network structure can improve network connectivity. Simulation results are provided to support and complement theoretical analysis.

Multiple-input multiple-output (MIMO) systems are threatened by smart attackers, who apply programmable radio devices such as software defined radios to perform multiple types of attacks such as eavesdropping, jamming and spoofing. In this paper, MIMO transmission in the presence of smart attacks is formulated as a noncooperative game, in which a MIMO transmitter chooses its transmit power level and a smart attacker determines its attack type accordingly. A Nash equilibrium of this secure MIMO transmission game is derived and conditions assuring its existence are provided to reveal the impact of the number of antennas and the costs of the attacker to launch each type of attack. A power control strategy based on Q-learning is proposed for the MIMO transmitter to suppress the attack motivation of smart attackers in a dynamic version of MIMO transmission game without being aware of the attack and the radio channel model. Simulation results show that our proposed scheme can reduce the attack rate of smart attackers and improve the secrecy capacity compared with the benchmark strategy.

In this work, we study the information spreading time in multiplex networks, adopting the gossip (random-walk) based information spreading model. A new metric called multiplex conductance is defined based on the multiplex network structure and used to quantify the information spreading time in a general multiplex network in the idealized setting. Multiplex conductance is then evaluated for some interesting multiplex networks to facilitate understanding in this new area. Finally, the tradeoff between the information spreading efficiency improvement and the layer cost is examined to explain the user's social behavior and motivate effective multiplex network designs.

In various resource sharing networks, opportunistic resources with dynamic quality are often present for the users to exploit. As many user tasks are delay-tolerant, this favorably allows the network users to wait for and access the opportunistic resource at the time of its best quality. For such delay-tolerant and opportunistic resource sharing networks, the resource accessing strategies developed in the literature suffer from three limitations. First, they mainly focused on single-user scenarios, whereas the competition from other users is ignored. Second, the influence from the resource seller who may take actions to manipulate the resource sharing procedure is not considered. Third, the impact of the actions from both the network users and the resource seller on the resource quality dynamics is not considered either. To overcome these limitations, a leader-follower controlled Markov stopping game (LF-C-MSG) is developed in this paper. The derived Stackelberg equilibrium strategy of the LF-C-MSG can be used to guide the behaviors of both the network users and the resource seller for better performance and resource utilization efficiency. Two exemplary applications of the proposed LF-C-MSG are presented, along with corresponding numerical results to verify the effectiveness of the proposed framework.

In this paper, we consider the optimal design of interlinks for an interdependent system of networks. In contrast to existing literature, we explicitly exploit the information of intra-layer node degrees to design interdependent structures such that their robustness against cascading failures, triggered by randomized attacks, is maximized. Utilizing percolation theory-based system equations relating the robustness of the network to its degree sequence, we characterize the optimal design for the one-to-one structure, with complete interdependence and partial interdependence, under randomized attack. We also extend our study to the one-to-many interdependence structure and the targeted attack model. The theoretically derived optimal interdependence structures have been verified using simulations on scale-free networks.

Interference alignment (IA) can remarkably improve the spectral efficiency of dense small cell networks (SCNs) underlaying a macrocell, but its feasibility condition and implementation complexity are restricted by the number of small cell equipments (SUEs). Moreover, the SUEs performing IA may have unsatisfactory quality of service (QoS) requirements as IA only eliminates interference while neglecting the gain of desired signals. In this paper, we propose a centralized efficient subchannel allocation scheme based on IA with similarity clustering in dense SCNs underlaying a macrocell, which aims at maximizing the number of QoS guaranteed SUEs performing IA. The corresponding problem is formulated as a combinatorial optimization problem which is NP-hard. So a low-complexity solution is proposed which includes three phases: similarity clustering for SUEs through graph partitioning, further adjustments of cluster sizes to make IA feasible in each cluster, and subchannel allocation for the formed clusters, each of which is performed with a notably reduced computational complexity. Moreover, the proposed solution greatly reduces the signaling overhead incurred by channel state information estimation. Numerical results show that the proposed solution not only outperforms other related schemes, but also achieves a performance close to the optimal solution.

Spreading a large file consisting of many packets over a mobile network is challenging due to the short meeting duration for each transmission. Moreover, two typical causes of inefficient file spreading are duplicate packet reception at the destination nodes and excessive overhead exchanges. We propose to employ fountain codes at the source node to jointly addresses the three issues: 1) each coded packet can be small enough to fit into the meeting duration; 2) duplicate packet reception is significantly reduced since each coded packet is innovative; and 3) overhead is greatly saved by using file-level ACK instead of packet-level ACK. We conduct performance analysis in terms of the source-to-destination file delay and source-to-destination file spreading time in both non-relaying and relaying scenarios. While packet duplication can be eliminated in the former scenario, there is still a non-trivial duplication probability if relaying is allowed. Therefore, we propose a fountain-coded two-hop relaying (FTTR) protocol to further reduce the packet duplication ratio so that the spreading performance does not degrade with network size. The file spreading time and packet duplication ratio of FTTR are derived in closed form and verified through simulations.

We consider a partially interdependent network and develop mathematical equations relating the fractional size of the connected component of the network, surviving the cascading failure, to the intra-layer degree distribution of the nodes. We show that these system equations can be mathematically analyzed and closed form expressions for the metrics of robustness can be obtained for the Erdos-Renyi (ER) model of random graph generation. We have described the application of our analysis technique to networks with general degree distributions. In our analysis, we consider the two extremes of the attack model: randomized attack, where nodes are attacked at random without any knowledge of intra-layer degrees and perfect targeted attack, where nodes are attacked based on the strict descending order of their intra-layer degrees. Our results can enable researchers to gain a better understanding of the robustness of interdependent networks.

As information systems become ubiquitous, Intrusion Detection Systems (IDSs) have assumed increasing importance. As a result, substantial amount of research efforts have been devoted to developing various intrusion detection algorithms. However, there is still no single detection algorithm that can catch all possible attacks. On the other hand, it is infeasible for practical IDSs to run all the detection algorithms simultaneously due to resource limitation, leaving potential opportunities for the adversaries to explore. This resource scarcity problem becomes more severe when the system is in an ill state (e.g., partially compromised). Enabling collaboration among multiple IDSs may be a viable way to mitigate this problem. Particularly, IDSs in the healthy state can share some of their idle computational resources to those in ill states, so as to improve the overall intrusion detection performance. Considering this, the collaborative IDS configuration problem is formulated as a two-layer stochastic game (SG) in this work and a new algorithm is proposed to solve this two-layer SG. Simulation results show that the proposed algorithm can provide an effective collaborative configuration scheme, leading to significant detection performance gain. Some performance analysis has also been given, and the conditions under which there is a guaranteed improvement in expected system performance have been derived.

We study the connectivity of overlaid wireless networks where two users can communicate if the signal-to-interference ratio is larger than a threshold subject to an outage constraint. By using percolation theory, we first specify a 2-dimensional connectivity region defined as the set of density pairs-the density of secondary users and the density of primary users- within which the secondary network is percolated. Several interesting properties of this region are also revealed. Our work provides a new perspective for better understanding of the connectivity of large-scale overlaid networks.

Opportunistic resources are often present in various resource sharing networks for the users to exploit, but their qualities often change over time. Fortunately, many user tasks are delay-tolerant, which offers the network users a favorable degree of freedom in waiting for and accessing the opportunistic resource at the time of its best quality. For such delay-tolerant and opportunistic resource sharing networks (DT-ORS-Net), the corresponding optimal accessing strategies developed in existing literature mainly focus on the single-user scenarios, while the potential competition from other peer users in practical multi-user DT-ORS-Net is often ignored. Considering this, a multi-player Markov stopping game (M-MSG) is developed in this work, and the derived Nash equilibrium (NE) strategy of this M-MSG can guide network users to properly handle the potential competition from other peers and thus exploit the time diversity of the opportunistic resource more effectively, which in turn further improves the resource utilization efficiency. Applications in the cloud-computing and the mobile crowdsourcing networks are demonstrated to verify the effectiveness of the proposed method, and simulation results show that using the NE strategy of the proposed M-MSG can provide substantial performance gain as compared to using the conventional single-user optimal one.

Zero-determinant (ZD) strategies that allow a player to unilaterally control the linear combinations of its own and other players' expected rewards in iterated games have recently found wide applications. However, existing ZD strategies mainly focus on some specific scenarios with restrictions on the number of players or actions a player can take. Targeting wider applications and better performance, the ZD strategies along with corresponding existence conditions for general multi-player multi-action iterated games are developed in this work, including existing ones as special cases. In addition, an interesting fact that every player can have at most one master player (that can control the expected reward of the given player) is revealed.

Motivated by information-theoretic security, link signature (LS)-based security mechanisms exploit the ample channel characteristics between wireless devices for security establishment. Nevertheless, LS is originated from wireless environments and hence may exhibit potential vulnerabilities that can be exploited by adversary in the vicinity. As to this, it is widely believed in existing literature on LS that, a half-wavelength guard zone is sufficient to decorrelate the adversary channel from the legitimate one and thereby secures the legitimate LS. However, such an assumption may not hold universally - in some environments, high channel correlations have been observed for much larger spatial separations. Considering this, a comprehensive understanding of channel correlation in different wireless environments is needed for more confident deployment of LS-based security mechanisms. To this end, various well-established channel correlation models are investigated in this work. A set of important physical factors that have significant influence on LS security are identified, and with the obtained insights, extensive simulations are conducted to explore suitable guard zone sizes for LS in several typical indoor and outdoor environments. Experimental results based on universal software radio peripheral (USRP) platforms and GNURadio are also presented to further support the analysis.

In this paper, we jointly consider the user selection and feedback design problems in a virtual cellular network (VCN), where multiple base stations (BSs) share a user set. In many practical systems, the uplink feedback channel is generally shared by multiple users. Thus, the feedback budget allocated to unselected users not only wastes the feedback resources, but harms the system throughput by decreasing the available feedback budget for the selected users. We optimize both the user selection and feedback bit allocation based on long-term average channel information of the users. We first analyze the effects of the quantization error on the average achievable rate of the VCN system. Next, we propose a user selection and feedback bit allocation protocol under each BS's sum feedback rate constraint as well as the sparsity constraint on all users' feedback sizes. We show that the joint optimization problem can be decoupled into several NP-hard subproblems, one for each BS. We describe the brute-force searching algorithm for the optimal solution, and propose an efficient algorithm with significantly reduced computational complexity by relaxing the sparsity constraint on the feedback sizes. As a result, only the selected users exploit the uplink feedback budget, and the system performance is improved.

An important application for modern large-scale networks is to spread the information efficiently to the largest audience. To better understand the theoretical underpinnings, a novel graph metric named mobile conductance was proposed in our previous work to evaluate the information spreading time of a connected mobile network. By capturing the details of both network structure and mobility pattern, this metric essentially determines the network bottleneck for conducting information flow under general network mobility. Despite major relaxation on node mobility, only slight relaxation on network connectivity was made in our previous work. In this paper, we make another major relaxation on the network connectivity by extending the mobile-conductance based analytical model to the sparse setting, hence offering a unified view. Interestingly, a penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. By jointly considering mobility and connectivity, we derive the mobile conductance for various mobility models with general connectivity. Using these analytical results, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to compensate for network connectivity deficiency.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range, and therefore, accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the information propagation speed (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

Jamming attacks that can further lead to denial of service attacks have thrown serious threats to underwater sensor networks (UWSNs). However, due to the narrow bandwidth of underwater acoustic signals and time variant propagation environments, jamming in UWSNs cannot be fully addressed by spread spectrum techniques, one type of widely-used antijamming methods in wireless networks for decades. In this work, we investigate jamming attacks in underwater sensor networks. More specifically, the interactions between the underwater sensors and jammers in UWSNs are formulated as an underwater jamming game, in which the players choose their transmit power levels to maximize their individual utilities based on the signal to interference plus noise ratio of the legal signals and transmission costs. The Nash equilibrium (NE) of a static jamming game is presented in a closed-form expression for the jamming scenario with known acoustic channel gains. For the dynamic and unknown underwater environments, we propose a reinforcement learning-based anti-jamming method for UWSNs, in which each sensor chooses its transmit power without knowing the channel gain of the jammers. Simulations are performed to evaluate the NE in the static jamming game in underwater sensor networks and to validate the efficacy of the proposed anti-jamming power control scheme against jamming in dynamic environments.

In this work we consider the optimal design of interconnection links for an interdependent system of networks. In contrast to existing literature, we explicitly exploit the information of intra- layer node degrees to design more robust interdependency structure against cascading failures triggered by random attacks. Built on solid mathematical models, we characterize the optimal design for the one-to-one structure, with complete interdependence and partial interdependence. We also extend the study to the one-to-many structure and targeted attack model. Simulation results are provided to corroborate the theoretical results.

Intrusion detection systems (IDSs) assume increasingly importance in past decades as information systems become ubiquitous. Despite the abundance of intrusion detection algorithms developed so far, there is still no single detection algorithm or procedure that can catch all possible intrusions; also, simultaneously running all these algorithms may not be feasible for practical IDSs due to resource limitation. For these reasons, effective IDS configuration becomes crucial for real-time intrusion detection. However, the uncertainty in the intruder's type and the (often unknown) dynamics involved with the target system pose challenges to IDS configuration. Considering these challenges, the IDS configuration problem is formulated as an incomplete information stochastic game in this work, and a new algorithm, Bayesian Nash-Q learning, that combines conventional reinforcement learning with a Bayesian type identification procedure is proposed. Numerical results show that the proposed algorithm can identify the intruder's type with high fidelity and provide effective configuration.

In this paper, we consider a beamforming game of the transmitters in a two-user multiple-input single- output interference channel using limited feedback and investigate how each transmitter should find a strategy from the quantized channel state information (CSI). In the beamforming game, each transmitter (a player) tries to maximize the achievable rate (a payoff function) via a proper beamforming strategy. In our case, each transmitter's beamforming strategy is represented by a linear combining factor between the maximum-ratio transmission (MRT) and the zero-forcing (ZF) beamforming vectors, which is shown to be a Pareto optimal achieving strategy. With the perfect CSI, each transmitter can know the exact achievable rate region, and hence can find the beamforming strategy corresponding to any point in the achievable rate region. With limited feedback, however, the transmitters can only conjecture the achievable rate region from the quantized CSI, so their optimal strategies may not be optimal anymore. Considering the quantized CSI at the transmitter, we first find the Nash equilibrium in a non-cooperative game. Then, in a cooperative (Nash bargaining) game, we find a Nash bargaining solution and test its validity. Finally, we propose three bargaining solutions that improve the validity of the cooperation or the average Nash product. Our proposed bargaining solutions utilize the codebook structure; instead of each quantized channel itself, its Voronoi region is considered.

To ensure continuous functioning and satisfactory performance, a wireless communication system has to not only learn and adapt to the unknown and ever-changing wireless environment, but also strategically deal with the usually unfamiliar peers. Incomplete information stochastic game (SG) is a promising model for the corresponding analysis and strategy design. In this work, an exemplary multi-channel spectrum access game (SAG) with unknown environment dynamics and limited information of the other player is considered to illustrate the proposed solution for the corresponding incomplete information SG. To find the best communication strategy in the face of uncertainty, a joint reinforcement learning and type identification algorithm is developed, which is provably convergent under certain technical conditions. Numerical results show that using the proposed algorithm, a wireless user can gradually achieve the same performance as that in the corresponding complete information game.

Connectivity is crucial to ensure information availability and survivability for wireless networks. In this paper, we propose a unified framework to study the connectivity of wireless networks under a general type of interference attack, which can address diverse applications including Cognitive Radio, Jamming attack and shadowing effect. By considering the primary users, jammers and deep fading as unified Interferers, we investigate a 3-dimensional connectivity region, defined as the set of key system parameters - the density of users, the density of Interferers and the interference range of Interferers - with which the network is connected. Further we study the impact of the Interferers' settings on node isolation probability, which is a fundamental local connectivity metric. Through percolation theory, the sufficient and necessary conditions for global connectivity are also derived. Our study is supported by simulation results.

With the advancement of modern technologies, the security battle between a legitimate system (LS) and an adversary is becoming increasingly sophisticated, involving complex interactions in unknown dynamic environments. Stochastic game (SG), together with multi-agent reinforcement learning (MARL), offers a systematic framework for the study of information warfare in current and emerging cyber-physical systems. In practical security games, each player usually has only incomplete information about the opponent, which induces information asymmetry. This work exploits information asymmetry from a new angle, considering how to exploit local information unknown to the opponent to the player's advantage. Two new MARL algorithms, termed minimax-PDS and WoLF-PDS, are proposed, which enable the LS to learn and adapt faster in dynamic environments by exploiting its private local information. The proposed algorithms are provably convergent and rational, respectively. Also, numerical results are presented to show their effectiveness through two concrete anti-jamming examples.

Green communications have been attracting increased research interest recently. Equipped with a rechargeable battery, a source node can harvest energy from ambient environments and rely on this free and regenerative energy supply to transmit packets. Due to the uncertainty of available energy from harvesting, however, intolerably large latency and packet loss could be induced, if the source always waits for harvested energy. To overcome this problem, one Reliable Energy Source (RES) can be resorted to for a prompt delivery of backlogged packets. Naturally, there exists a tradeoff between the packet delivery delay and power consumption from the RES. In this paper, we address the delay optimal scheduling problem for a bursty communication link powered by a capacity-limited battery storing harvested energy together with one RES. The proposed scheduling scheme gives priority to the usage of harvested energy, and resorts to the RES when necessary based on the data and energy queueing processes, with an average power constraint from the RES. Through two-dimensional Markov chain modeling and linear programming formulation, we derive the optimal threshold-based scheduling policy together with the corresponding transmission parameters. Our study includes three exemplary cases that capture some important relations between the data packet arrival process and energy harvesting capability. Our theoretical analysis is corroborated by simulation results.

In order to improve the spectrum efficiency, spectrum sharing systems allow multiple systems to utilize the same spectrum with different priorities. Typically, the primary network performs as a stand-alone network while the secondary one accesses the spectrum only if it does no harm to the primary receivers. In this paper, the configuration of the secondary network is of our interest and we explore its single-hop transport throughput (STT) with outage constraints imposed on both networks. STT is a new metric that inherits the merits of both the traditional transport capacity and transmission capacity, incorporating transmission distance and outage probability into a uniform framework. Given the settings of the primary network, we first evaluate the limit of the secondary STT, single-hop transport capacity (STC). Then, we investigate STT with secondary receivers randomly located in the field of interest. To provide a comprehensive view of achievable secondary network throughputs, three models regarding the selection of receivers are considered: optimally selected, randomly selected, and the nearest neighbors. Our theoretical analysis are well substantiated by numerical and simulation results.

Cooperative cognitive radio networks (CCRNs) incorporates cooperative communication into cognitive radio networks, in which, primary users lease their spectrum to secondary users, and in exchange, the primary users leverage secondary users as cooperative relays to enhance their own throughput. Mobile operators offload their Internet traffic to privately owned Wi-Fi access points (APs), much to the inconvenience of non-cellular users served by the APs. However, by employing the CCRN scheme, the mobile operator can lease a licensed channel to the AP, effectively doubling its capacity. In this paper, we propose an implementation of the CCRN framework applied to IEEE 802.11 WLANs. The cooperation is cast as a two-player bargaining game where the two players are the primary users (users of the mobile operator) and the secondary users (users of the AP before spectrum leasing) who bargain for either throughput share or channel access time share. The optimal resource allocation that ensures efficiency as well as fairness among users is provided by the Nash solution. Simulation results show that the users achieve higher throughput via the proposed CCRN scheme, thus providing the mobile operator (e.g., AT&T) and the private Wi-Fi provider (e.g., a Starbucks coffee shop) with incentives for cooperation.

In this paper, we consider a multiple access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. The users are supposed to share the channel via Time Division Multiple Access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for realtime information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first show that in the case when each node has an infinite-capacity battery, equal-power TDMA is optimal for throughput maximization. Using Markov chain modeling, we then study the system performance for the finite-capacity battery case under the equal-power TDMA framework. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, while equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities.

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.

In this paper, the mobility of network nodes is explored as a new promising approach for jamming defense. To fulfill it, properly designed node motion that can intelligently adapt to the jammer's action is crucial. In our study, anti-jamming mobility control is investigated in the context of the single and multiple commodity flow problems, in the presence of intelligent mobile jammers which can respond to the evasion of legitimate nodes as well. Based on spectral graph theory, two new spectral quantities, single- and multi-weighted Cheeger constants and corresponding eigenvalue variants, are constructed to direct motions of the defender and the attacker in this dynamic adaptive competition. Both analytical and simulation results are presented to justify the effectiveness of the proposed approach. Furthermore, the proposed scheme can also be applied in cognitive radio networks to reconfigure the secondary users in the presence of mobile primary users.

Jamming attacks can cause serious destruction to communication systems without much cost, especially when secret keys shared by the system are exposed. Uncoordinated Frequency Hopping (UFH) is an effective countermeasure to jamming attacks without dependence on pre-shared secret keys. In this paper we study the connectivity of a multi-channel network under jamming attacks, where each communication link can switch from regular transmission to UFH transmission when jamming attacks are detected. Under the framework of percolation theory, we show that as the jammer density increases, two phase transitions occur: from strong connection where there exists an infinite component composed of regular links, to weak connection where there exists an infinite component composed of both regular and UFH links, then to disconnection.

A fundamental assumption of link signature based security mechanisms is that the wireless signals received at two locations separated by more than half a wavelength are essentially uncorrelated. However, it has been observed that in certain circumstances (e.g., with poor scattering and/or a strong line-of-sight (LOS) component), this assumption is invalid. In this paper, a Correlation ATtack (CAT) is proposed to demonstrate the potential vulnerability of the link signature based security mechanisms in such circumstances. Based on statistical inference, CAT explicitly exploits the spatial correlations to reconstruct the legitimate link signature from the observations of multiple adversary receivers deployed in vicinity. Our findings are verified through theoretical analysis, well-known channel correlation models, and experiments on USRP platforms and GNURadio.

In this paper, we propose a general analytical framework for information spreading in mobile networks based on a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. We derive a general result for the information spreading time in mobile networks in terms of this new metric, and instantiate it through several popular mobility models. Large scale network simulation is conducted to verify our analysis.

This paper presents a novel mechanism, called Ally Friendly Jamming, which aims at providing an intelligent jamming capability that can disable unauthorized (enemy) wireless communication but at the same time still allow authorized wireless devices to communicate, even if all these devices operate at the same frequency. The basic idea is to jam the wireless channel continuously but properly control the jamming signals with secret keys, so that the jamming signals are unpredictable interference to unauthorized devices, but are recoverable by authorized ones equipped with the secret keys. To achieve the ally friendly jamming capability, we develop new techniques to generate ally jamming signals, to identify and synchronize with multiple ally jammers. This paper also reports the analysis, implementation, and experimental evaluation of ally friendly jamming on a software defined radio platform. Both the analytical and experimental results indicate that the proposed techniques can effectively disable enemy wireless communication and at the same time maintain wireless communication between authorized devices.

Collaborative spectrum sensing improves the spectrum state estimation accuracy but is vulnerable to the potential attacks from malicious secondary cognitive radio (CR) users, and thus raises security concerns. One promising malicious user detection method is to identify their abnormal statistical spectrum sensing behaviors. From this angle, two hidden Markov models (HMMs) corresponding to honest and malicious users respectively are adopted in this paper to characterize their different sensing behaviors, and malicious user detection is achieved via detecting the difference in the corresponding HMM parameters. To obtain the HMM estimates, an effective inference algorithm that can simultaneously estimate two HMMs without requiring separated training sequences is also developed. By using these estimates, high malicious user detection accuracy can be achieved at the fusion center, leading to more robust and reliable collaborative spectrum sensing performance (substantially enlarged operational regions) in the presence of malicious users, as compared to the baseline approaches. Different fusion methods are also discussed and compared.

Opportunistically sharing the white spaces, or the temporarily unoccupied spectrum licensed to the primary user (PU), is a practical way to improve the spectrum utilization. In this paper, we consider the fundamental problem of rate regions achievable for multiple secondary users (SUs) which send their information to a common receiver over such a white space channel. In particular, the PU activities are treated as on/off side information, which can be obtained causally or non-causally by the SUs. The system is then modeled as a multi-switch channel and its achievable rate regions are characterized in some scenarios. Explicit forms of outer and inner bounds of the rate regions are derived by assuming additional side information, and they are shown to be tight in some special cases. An optimal rate and power allocation scheme that maximizes the sum rate is also proposed. The numerical results reveal the impacts of side information, channel correlation and PU activity on the achievable rates, and also verify the effectiveness of our rate and power allocation scheme. Our work may shed some light on the fundamental limit and design tradeoffs in practical cognitive radio systems.

In this paper, we study distributed function computation in a noisy multihop wireless network. We adopt the adversarial noise model, for which independent binary symmetric channels are assumed for any point-to-point transmissions, with (not necessarily identical) crossover probabilities bounded above by some constant $(\epsilon)$. Each node takes an $(m)$-bit integer per instance, and the computation is activated after each node collects $(N)$ readings. The goal is to compute a global function with a certain fault tolerance in this distributed setting; we mainly deal with divisible functions, which essentially cover the main body of interest for wireless applications. We focus on protocol designs that are efficient in terms of communication complexity. We first devise a general protocol for evaluating any divisible functions, addressing both one-shot $((N = O(1)))$ and block computation, and both constant and large $(m)$ scenarios. We also analyze the bottleneck of this general protocol in different scenarios, which provides insights into designing more efficient protocols for specific functions. In particular, we endeavor to improve the design for two exemplary cases: the identity function, and size-restricted type-threshold functions, both focusing on the constant $(m)$ and $(N)$ scenario. We explicitly consider clustering, rather than hypothetical tessellation, in our protocol design.

Security concerns are raised for collaborative spectrum sensing due to its vulnerabilities to the potential attacks from malicious secondary users. Most existing malicious user detection methods are reputation-based, which become incapable when the malicious users dominate the network. On the other hand, although Markovian models characterize the spectrum state behavior more precisely, there is a scarcity of malicious user detection methods which fully explore this feature. In this paper, a new malicious user detection method using two proposed conditional frequency check (CFC) statistics is developed under the Markovian model for the spectrum state. With the assistance of one trusted user, the proposed method can achieve high malicious user detection accuracy (≥ 95%) for arbitrary percentage of malicious users that may even be equipped with more advanced sensing devices, and can thus improve the collaborative spectrum sensing performance significantly. Simulation results are provided to verify the theoretical analysis and effectiveness of the proposed method.

In this paper, we analyze the effect of mobility on information spreading in geometric networks through natural random walks. Specifically, our focus is on epidemic propagation via mobile gossip, a variation from its static counterpart. Our contributions are twofold. Firstly, we propose a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. Secondly, we utilize geometrical properties to explore this metric for several popular mobility models, and offer insights on the corresponding results. Large scale network simulation is conducted to verify our analysis.

In wireless cellular systems, it is an important and challenging task to reliably multicast to numerous users that require the same contents at one transmission. In this paper, we propose a network coding based multicast scheme for wireless cellular OFDM systems. The base station encodes source packets with linear network coding and multicasts the coded packets to the target users over multicarriers. Thus, the users can correctly recover the source message as long as they successfully receive a certain number of packets. The reliability of coded wireless multicast is characterized by the user outage probability, which can be greatly reduced by efficiently exploiting frequency diversity gain via network coding. We show that the BS shall adjust the data transmission rate per carrier to strike a good balance between the reliability at each subcarrier and the redundancy among coded packets. It is also found that full diversity gain and no diversity gain should be exploited in the high and low SNR regimes, respectively. Simulation results also reveal that network coding generally provides great advantage for reliable wireless multicasting to a large number of users.

Collaborative spectrum sensing is vulnerable to the Byzantine attack. Existing reputation based countermeasures will become incapable when malicious users dominate the network. Also, there is a scarcity of methods that fully explore the Markov property of the spectrum states to restrain sensors' statistical misbehaviors. In this paper, a new malicious user detection method based on two proposed Conditional Frequency Check (CFC) statistics is developed with a Markovian spectrum model. With the assistance of one trusted sensor, the proposed method can achieve high malicious user detection accuracy in the presence of arbitrary percentage of malicious users, and thus significantly improves collaborative spectrum sensing performance.

To support the multicast and unicast services in the orthogonal frequency division multiplexing system simultaneously, a channel-aware adaptive resource allocation algorithm is proposed to maximise the total throughput of the unicast service while guaranteeing the required quality of service (QoS) for the multicast service. The two-step optimisation scheme is developed to solve the problem based on the perfect channel state information at the base station: first, subcarriers are allocated to the multicast and the unicast services under the assumption that power is divided equally to every subcarrier. Especially, the noisy chaotic neural network with a new parameter set is applied to allocate the subcarriers to the unicast service by elaborately constructing the energy function to fully exploit the multiuser diversity gain, the optimal solution is found successfully through its rich neurodynamics; Secondy, the power averagely allocated to the unicast service is reallocated quickly in a linear water-filling fashion. Compared with existing algorithms the proposed algorithm achieves higher spectrum efficiency and better bit-error rate for the multicast service, also higher throughput for the unicast service.

We propose a jamming-resistant collaborative broadcast scheme for wireless networks, which utilizes the Un coordinated Frequency Hopping (UFH) technique to counteract jamming without preshared keys, and exploits node cooperation to achieve higher communication efficiency and stronger jamming resistance. In this scheme, nodes that already obtain the broadcast message serve as relays to help forward it to other nodes. Relying on the sheer number of relay nodes, our scheme provides a new angle for jamming countermeasure, which not only significantly enhances the performance of jamming-resistant broadcast, but can readily be combined with other existing or emerging antijamming approaches in various applications. We present the collaborative broadcast protocol, and analyze its successful packet reception rate and the corresponding cooperation gain for both synchronous and asynchronous relays for a snapshot scenario. We also investigate the full broadcast process based on a Markov chain model and derive a closed-form expression of the average broadcast delay. Simulation results in both single-hop and multihop networks indicate that our scheme is a promising antijamming technique in wireless networks.

We propose in [1] a collaborative broadcast scheme for wireless networks, which applies the Uncoordinated Frequency Hopping (UFH) technique to counteract jamming and exploits node cooperation to enhance broadcast efficiency. In this scheme, some nodes that already obtain the broadcast message are selected to relay the message to other nodes. In this paper, we extend the study to the generalized multihop network scenarios, and provide solutions for important related issues, such as the relay node selection, multiple access control, relay channel selection and packet scheduling.We also study the spatial and frequency (channel) diversity provided by the collaborative broadcast. Simulation results show that the collaborative broadcast achieves low broadcast delay, with low energy consumption and small computational overhead in multihop networks.

We propose a collaborative broadcast scheme for wireless networks, which is based on the Uncoordinated Frequency Hopping (UFH) technique and exploits the node cooperation to achieve higher communication efficiency and stronger jamming resistance. In the collaborative broadcast, nodes that already obtain the broadcast message help forward the message to other nodes. Relying on the sheer number of relay nodes, which grows with time surely, our scheme is fundamentally more powerful than most recent attempts for anti-jamming broadcast. Potential applications include emergency alert broadcast and distribution of key system information in the presence of jamming. We provide three relay channel selection strategies for collaborative broadcast, analyze the corresponding successful packet reception rates for both synchronous and asynchronous scenarios, and present the corresponding cooperation gain. Simulation results in a practical setting show that our scheme significantly reduces broadcast delay and energy consumption against the most powerful jamming, - responsive-sweep jamming.

A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming energy efficient clusters in a self-organized fashion while restricting interference to Primary User (PU) systems. The spectrum-aware clustered structure is presented where the communications consist of intra- cluster aggregation and inter-cluster relaying. In order to save communication power, the optimal number of clusters is derived and the idea of groupwise constrained clustering is introduced to minimize intra-cluster distance under spectrum-aware constraint. In terms of practical implementation, DSAC demonstrates preferable scalability and stability because of its low complexity and quick convergence under dynamic PU activity. Finally, simulation results are given to validate the proposed scheme.

Overlaid networks, typically composed of primary and secondary networks, are emerging as a viable candidate to resolve the conflict between increasing demand for spectrum and spectrum shortage. The essential purpose of overlaid networks is to improve the network spectrum efficiency, which, however, may not be achieved if the secondary network is inappropriately configured. In decentralized overlaid networks, the throughput of the primary network will decrease due to the extra interference from the secondary network, and its loss may not be compensated by the throughput gained by the secondary network. This motivates us to study the overall throughput of the overlaid networks. In particular, we examine the overall transmission throughput, which is a variation of the transmission capacity, a popular metric in the study of decentralized networks. Our study provides a sufficient condition for the secondary network setting such that the overall throughput is improved over that of a stand-alone primary network. In addition both the maximal allowable secondary density and the optimal secondary density which maximizes the overall throughput are derived.

Due to the emergence of Cognitive Radio, a special type of heterogeneous networks attracts increasing interest recently, in which a secondary network composed of cognitive users shares the same resources opportunistically with a primary network of licensed users. Network throughput in this setting is of essential importance. Some pioneer works in this area showed that this type of heterogeneous networks performs as well as two stand-alone networks, under the dense network model where the size of a network grows with the node density in a fixed area. A key assumption behind this conclusion is that the density of the secondary network is higher than that of the primary one in the order sense, which essentially decouples the two overlaid networks, as the secondary network dominates asymptotically. In this paper we endeavor to investigate this problem with a weaker condition that the dimensions of the two overlaid networks are on the same order, and consider the extended network model where the size of a network scales with the area with the node density fixed. Surprisingly, our analysis shows that this weaker (and arguably more practical) condition does not degrade either network throughput in terms of scaling law. Our result further reveals the potentials of CR technology in real applications.

Spectrum sharing systems such as cognitive radio networks have drawn much attention recently due to their potential to resolve the conflict between increasing demand for spectrum and spectrum shortage. Such systems are typically composed of primary and secondary networks; the configuration of the latter depends on spectrum opportunity unexploited in the former. In this paper we explore the characteristics of the single hop transport throughput (STT) of the secondary network with outage constraints imposed on both networks. STT is a new metric that inherits the merits of both the traditional transport capacity and another popular metric, transmission capacity, incorporating transmission distance and outage probability into a uniform framework. We first derive the limit of STT, single hop transport capacity (STC), together with a practical upper bound for it. Then we investigate STT with secondary receivers randomly located in the field of interest. Three models regarding the selection of receivers are considered: optimally selected, randomly selected, or the nearest. Study on these models provides a comprehensive view of achievable secondary network throughput, and offers insights into the configuration of secondary networks. In addition, the broadcast transport throughput (BTT) of the secondary networks is also investigated as an extension of STT, and its similarity with STT in the nearest neighbor model is revealed.

Spread spectrum techniques such as Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping (FH) have been commonly used for anti-jamming wireless communication. However, traditional spread spectrum techniques require that sender and receivers share a common secret in order to agree upon, for example, a common hopping sequence (in FH) or a common spreading code sequence (in DSSS). Such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the key from a compromised receiver and then disrupt the wireless communication. In this paper, we develop a novel Delayed Seed-Disclosure DSSS (DSD-DSSS) scheme for efficient anti-jamming broadcast communication. DSD-DSSS achieves its anti-jamming capability through randomly generating the spreading code sequence for each message using a random seed and delaying the disclosure of the seed at the end of the message. We also develop an effective protection mechanism for seed disclosure using content-based code subset selection. DSD-DSSS is superior to all previous attempts for anti-jamming spread spectrum broadcast communication without shared keys. In particular, even if a jammer possesses real-time online analysis capability to launch reactive jamming attacks, DSD-DSSS can still defeat the jamming attacks with a very high probability. We evaluate DSD-DSSS through both theoretical analysis and a prototype implementation based on GNU Radio; our evaluation results demonstrate that DSD-DSSS is practical and have superior security properties.

In this paper, we propose a novel distributed network decomposition algorithm with the aid of the factor graph model and the max-product algorithm, which aims to achieve minimum cut weight. Its effectiveness is testified for general graph partition as well as distributed inference in wireless networks. Our algorithm is fully distributed, simple in computation, and readily extensible, thus providing a potentially powerful, data-independent clustering scheme for a wide range of data processing and networking applications.

In this paper we study distributed function computation in a noisy multi-hop wireless network, in which n nodes are uniformly and independently distributed in a unit square. We adopt the adversarial noise model, for which independent binary symmetric channels are assumed for any point-to-point transmissions, with (not necessarily identical) crossover probabilities bounded above by some constant ¿. Each node holds an m-bit integer per instance and the computation is started after each node collects N readings. The goal is to compute a global function with a certain fault tolerance, in this distributed setting; we mainly deal with divisible functions, which essentially covers the main body of interest for wireless applications. We focus on protocol designs that are efficient in terms of communication complexity. We first devise a general protocol for evaluating any divisible functions, addressing both one-shot (N = O(1)) and block computation, and both constant and large m scenarios; its bottleneck in different scenarios is also analyzed. Based on this analysis, we then endeavor to improve the design for two special cases: identity function, and some restricted type-threshold functions, both focusing on the constant m and N scenario.

Jamming resistance is crucial for applications where reliable wireless communication is required. Spread spectrum techniques such as Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) have been used as countermeasures against jamming attacks. Traditional anti-jamming techniques require that senders and receivers share a secret key in order to communicate with each other. However, such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the shared key from a compromised or malicious receiver and disrupt the reception at normal receivers. In this paper, we propose a Randomized Differential DSSS (RD-DSSS) scheme to achieve anti-jamming broadcast communication without shared keys. RD-DSSS encodes each bit of data using the correlation of unpredictable spreading codes. Specifically, bit ``0'' is encoded using two different spreading codes, which have low correlation with each other, while bit ``1'' is encoded using two identical spreading codes, which have high correlation. To defeat reactive jamming attacks, RD-DSSS uses multiple spreading code sequences to spread each message and rearranges the spread output before transmitting it. Our theoretical analysis and simulation results show that RD-DSSS can effectively defeat jamming attacks for anti-jamming broadcast communication without shared keys.

In this paper we study two basic properties, capacity and connectivity, of Cognitive Radio networks. Our goal is to quantitatively characterize the relationship and tradeoff among key system parameters involved in these properties, incorporating channel randomness and interference into the performance analysis. In particular, we explore the characterization of a capacity metric, single-hop transport capacity, with respect to arbitrarily and randomly located receivers, and investigate a fundamental connectivity metric, node isolation probability. The tradeoff between capacity and connectivity is also revealed.

Spread spectrum techniques (e.g., Frequency Hopping (FH), Direct Sequence Spread Spectrum (DSSS)) have been widely used for anti-jamming wireless communications. Such techniques require that communicating devices agree on a shared secret before communication. However, it is non-trivial for two devices that do not share any secret to establish one in presence of a jammer. Recently, several schemes relying on Uncoordinated Frequency Hopping (UFH) were proposed to allow two devices to establish a secret key using Diffie-Hellman (DH) key establishment protocol in presence of jammers. Unfortunately, all these schemes are limited in efficiency. In this paper, we propose a novel scheme named USD-FH, which uses Uncoordinated Seed Disclosure in Frequency Hopping to establish a shared secret in presence of jammers. The basic idea is to transmit each DH key establishment message using a one-time pseudo-random hopping pattern and disclose the corresponding seed in an uncoordinated manner before the actual message. Due to the large number of channels available for wireless communication, the jammers cannot control all channels at the same time. When the receiver and the sender use the same channel during seed disclosure, the receiver can get the seed. If the jammer does not listen on the same channel (and thus it does not know the hopping pattern), the receiver can receive the actual message without being jammed. We validate USD-FH through both theoretical analysis and simulation. Our results show that USD-FH is much more efficient and robust than previous solutions.

To address the increasing demand for wireless bandwidth, cognitive radio networks (CRNs) have been proposed to increase the efficiency of channel utilization; they enable the sharing of channels among secondary (unlicensed) and primary (licensed) users on a non-interference basis. A secondary user in a CRN should constantly monitor for the presence of a primary user's signal to avoid interfering with the primary user. However, to gain unfair share of radio channels, an attacker (e.g., a selfish secondary user) may mimic a primary user's signal to evict other secondary users. Therefore, a secure primary user detection method that can distinguish a primary user's signal from an attacker's signal is needed. A unique challenge in addressing this problem is that Federal Communications Commission (FCC) prohibits any modification to primary users. Consequently, existing cryptographic techniques cannot be used directly. In this paper, we develop a novel approach for authenticating primary users' signals in CRNs, which conforms to FCC's requirement. Our approach integrates cryptographic signatures and wireless link signatures (derived from physical radio channel characteristics) to enable primary user detection in the presence of attackers. Essential to our approach is a {\em helper node} placed physically close to a primary user. The helper node serves as a "bridge" to enable a secondary user to verify cryptographic signatures carried by the helper node's signals and then obtain the helper node's authentic link signatures to verify the primary user's signals. A key contribution in our paper is a novel physical layer authentication technique that enables the helper node to authenticate signals from its associated primary user. Unlike previous techniques for link signatures, our approach explores the geographical proximity of the helper node to the primary user, and thus does not require any training process.

Existing works on distributed consensus explore linear iterations based on reversible Markov chains, which contribute to the slow convergence of the algorithms. It has been observed that by overcoming the diffusive behavior of reversible chains, certain nonreversible chains lifted from reversible ones mix substantially faster than the original chains. In this paper, the idea of Markov chain lifting is studied to accelerate the convergence of distributed consensus, and two general pseudoalgorithms are presented. These pseudoalgorithms are then instantiated through a class of location-aided distributed averaging (LADA) algorithms for wireless networks, where nodes' coarse location information is used to construct nonreversible chains that facilitate distributed computing and cooperative processing. Our first LADA algorithm is designed for grid networks; for ak×kgrid network, it achieves an ε-averaging time ofO(klog(ε-1)). Based on this algorithm, in a wireless network with transmission ranger, an ε-averaging time ofO(r-1log(ε-1)) can be attained through a centralized algorithm. Subsequently, a distributed LADA algorithm is presented, achieving the same scaling law in averaging time as the centralized scheme in wireless networks for allrsatisfying the connectivity requirement; the constructed chain also attains the optimal scaling law in terms of an important mixing metric, the fill time, in its class. Finally, a cluster-based LADA algorithm is proposed, which, requiring no central coordination, provides the additional benefit of reduced message complexity compared with the distributed LADA algorithm.

Quickest detection is applied in spectrum sensing in cognitive radio systems when multiple secondary users collaborate with limited communication time slots. When the transmissions of sensing results are not coordinated to avoid confliction, random broadcast is used to exchange information. A necessary condition for the optimal broadcast probability, as a function of the log likelihood ratio of local observation, is obtained using variational analysis. To alleviate the difficulty of computing the optimal broadcast probability, a simple threshold broadcast scheme is proposed. Simulation shows that the proposed threshold broadcast scheme can achieve substantial performance gain (less than 60% in detection delay for the same false alarm rate) over schemes of random broadcast without regulation and single-user spectrum sensing.

Quickest detection is applied in the spectrum sensing of cognitive radio systems when multiple secondary users collaborate with limited communication time slots. When transmissions are not coordinated to avoid collisions, random broadcast is used to exchange information. A necessary condition for optimal broadcast probability, as a function of the log likelihood ratio of local observation, is obtained using variational analysis. To alleviate the difficulty of computing optimal broadcast probability, a simple threshold broadcast scheme is proposed. Simulation shows that the proposed threshold broadcast scheme can achieve substantial performance gain (less than 60% in detection delay for the same false alarm rate) over random broadcast without regulation, as well as the single-user spectrum sensing.

Quickest detection of an abrupt distribution change with an unknown time varying parameter is considered. A novel adaptive approach is proposed to tackle this problem, which is shown to outperform the celebrated Parallel CUSUM Test. Performance is evaluated through theoretical analysis and numerical simulations.

In this paper, a variational message passing framework is proposed for Markov random fields, which is computationally more efficient and admits wider applicability compared to the belief propagation algorithm. Based on this framework, structured variational methods are explored to take advantage of both the simplicity of variational approximation (for inter-cluster processing) and the accuracy of exact inference (for intra-cluster processing). Its performance is elaborated on a Gaussian Markov random field, through both theoretical analysis and simulation results.

In this paper, the asymptotic performance of a recently proposed distributed inference framework, structured variational methods, is investigated. We first distinguish the intra- and inter-cluster inference algorithms as vertex and edge processes respectively. Their difference is illustrated, and convergence rate is derived for the intra-cluster inference procedure which is based on an edge process. Then, viewed as a mixed vertex-edge process, the overall performance of structured variational methods is characterized via the coupling approach. Tradeoff between complexity and performance of this algorithm is also addressed, which provides insights for network design and analysis.

The diversity-multiplexing tradeoff for multiple-input multiple-output (MIMO) point-to-point channels and multiple access channels were first proposed and studied by Zheng and Tse recently. While the optimal tradeoff curves for MIMO channels have been explicitly explored, those corresponding to some suboptimal and practical MIMO schemes are still open. One such important problem is the diversity-multiplexing tradeoff for a V-BLAST type system employing ordered successive interference cancellation (SIC) receivers with zero forcing (ZF) or minimum mean square error (MMSE) processing at each stage. In this paper, we take a novel geometrical approach and rigorously verify that under general settings, the optimal ordering rule for a V-BLAST SIC receiver will not improve its performance regarding diversity-multiplexing tradeoff in point-to- point channels. The same geometrical tool is then applied to MIMO spatial-division multiple access channels, leading to some first results in this area. Particularly, we reveal that when the rates of data streams are fixed (i.e., zero spatial multiplexing gain), the diversity order is not improved by user ordering.

In this paper, we incorporate clustering techniques into distributed consensus algorithms for faster convergence and better energy efficiency. Together with a simple distributed clustering algorithm, we design cluster-based distributed consensus algorithms in forms of both fixed linear iteration and randomized gossip. The time complexity of the proposed algorithms is presented in terms of metrics of the original and induced graphs, through which the advantage of clustering is revealed. Our cluster-based algorithms are also shown to achieve an Omega(log n) gain in message complexity over the standard ones.

We investigate three multihop fusion schemes for distributed detection in geographically dispersed sensor networks, multihop forwarding (MF) and Log-likelihood ratio Fusion (LF), which are different in the transmitted messages, fusion rules, and communication structure. Simulation results show that transmission energy is significantly reduced by multihop fusion schemes as compared to direct transmission, with LF outperforming the others. Moreover, it is shown that LF exhibits the most favorable energy scaling law with the network size among these schemes.

Cooperative transmission by base stations (BSs) can significantly improve the spectral efficiency of multiuser, multicell, multiple input multiple output (MIMO) systems. We show that contrary to what is often assumed in the literature, the multiuser interference in such systems is fundamentally asynchronous. Intuitively, perfect timing-advance mechanisms can be best only ensure that the desired signal components- but not also the interference components- are perfectly aligned at their intended mobile stations. We develop an accurate mathematical model for the asynchronicity, and show that it leads to a significant performance degradation of existing designs that ignore the asynchronicity of interference. Using three previously proposed linear precoding design methods for BS cooperation, we develop corresponding algorithms that are better at mitigating the impact of the asynchronicity of the interference. Furthermore, we also address timing-advance inaccuracies (jitter), which are inevitable in a practical system. We show that using jitter-statistics-aware precoders can mitigate the impact of these inaccuracies as well. The insights are critical for the practical implementation of BS cooperation in multiuser MIMO systems, a topic that is typically oversimplified in the literature.

In this paper, we propose cluster-based distributed averaging algorithms in forms of fixed iteration and random gossiping. Nodes within a cluster maintain the same value via broadcasting by the cluster-head, and information exchange occurs between neighboring clusters. Clustering essentially allows nodes in neighboring clusters to be joined, hence the resultant graph is well-connected and the algorithm converges much faster. Moreover, since the number of clusters is much smaller than the number of nodes, the communication and computation burden of the consensus algorithm is significantly reduced.

Cooperative transmission by base stations can significantly improve the spectral efficiency of multiuser, multi-cell multiple input multiple output systems. We show that in such systems the multiuser interference is asynchronous by nature, even when perfect timing-advance mechanisms ensure that the desired signal components arrive synchronously. We establish an accurate mathematical model for the asynchronism, and use it to show that the asynchronism leads to a significant performance degradation of existing linear preceding designs that assumed synchronous interference. We consider three different previously proposed precoding designs, and show how to modify them to effectively mitigate asynchronous interference.

Existing works on distributed averaging explore linear iterations based on reversible Markov chains. The convergence of such algorithms is bounded to be slow due to the diffusive behavior of the reversible chains. It has been observed that certain nonreversible chains lifted from reversible ones mix substantially faster than the original chains. We show that the idea of nonreversible lifting lends itself naturally to a fast distributed averaging algorithm, where each node maintains multiple estimates, corresponding to multiple lifted states in the Markov chain. We give a rigorous proof that it is possible to achieve an e-averaging time of Theta(k log(1/isin)) on a k times k grid. For a general wireless network, we propose a Location-Aided Distributed Averaging (LADA) algorithm, which utilizes local information to construct a fast-mixing nonreversible chain in a distributed manner. We show that using LADA, an e-averaging time of Theta(r-1log(1/isin)) is achievable in a wireless network with transmission radius r.

MIMO communications has been a highly active research area recently due to its remarkable capacity potential. The predicted capacity gain is nonetheless greatly limited in realistic propagation scenarios, especially when the number of antennas becomes large. This motivates us to investigate a generalized paradigm for multiple-antenna communications, called distributed MIMO, which has the potential to address many of the problems inherent in conventional co-located MIMO systems. In this paper, we demonstrate the advantages of distributed MIMO versus co-located MIMO in correlated fading and shadowing scenarios through asymptotic analysis.

Recent research reveals that information security and information-hiding capabilities can be enhanced by proper exploitation of space-time techniques. Meanwhile, intrinsic properties of ultra wideband (UWB) signals make it an outstanding candidate for secure applications. In this paper, we propose a unitary space-time coding scheme for impulse radio UWB systems. Its transmission secrecy, including low probability of intercept (LPI), low probability of detection (LPD) and anti-jamming performance, is analyzed. Theoretical and simulation results show its superiority in wireless secure communications over other concurrent schemes

The diversity-multiplexing tradeoff for MIMO point-to-point channels and multiple access channels are first proposed and studied in [4][5]. While the optimal tradeoff curves for MIMO channels have been explicitly explored, those corresponding to some practical MIMO schemes are still open. One such example, as mentioned in [4][5], is the diversity-multiplexing tradeoff problem for ordered successive interference cancellation (SIC) receivers, which is the focus of this paper. In literature, the impact of the optimal ordering on the diversity order for V-BLAST SIC receivers is analyzed for 2-layer scenarios [2][3][6], but only conjectured for larger number of layers through numerical results [2][7]. In this paper, based on a novel geometrical analysis, we prove that under general settings, any ordering rule for a V-BLAST SIC receiver will not improve its performance regarding diversity-multiplexing tradeoff. Furthermore, extending the study to multiple access channels, we show that the two extreme points of the tradeoff curve remain unchanged regardless of ordering, which motivates us to predict that the whole tradeoff curve is the same as that of fixed-order detectors.

Spatial diversity provided by multiple antennas implemented at the physical layer (PHY) can protect a wireless link from harmful fading, while opportunistic scheduling that exploits multiuser diversity at the media access control (MAC) layer can increase the system throughput with the aid of constructive fading in a multiuser wireless network. In this paper, by studying the average system capacity of opportunistic scheduling and scheduling gain (defined as the average system capacity difference between opportunistic scheduling and conventional round-robin scheduling), we investigate the cross-layer interaction between spatial diversity and multiuser diversity. Our analyses focus on the asymptotic scenarios, by allowing either the number of users or the number of antennas, or both, to go to infinity, for which some succinct closed-form expressions can be obtained and the connections among system parameters become clear.

In this paper, further in-depth asymptotic analysis on the interaction between spatial diversity and multiuser diversity in wireless networks is given, following our recent work [Zhou and Dai, ICC06] Rigorous proofs and necessarily stronger results in terms ot convergence are provided for some intuitions in this area. Equally important, explicit expressions of scheduling gain and average system capacity in various circumstances that reveal interesting inter-connections among key system parameters are given. Our results are general enough to cover many practical scenarios of interest

Distributed detection of a deterministic signal in correlated Gaussian noise in a one-dimensional sensor network is studied in this paper. In contrast to the traditional approach where a bank of dedicated parallel access channels (PAC) is used for transmitting the sensor observations to the fusion center, we explore the possibility of employing a shared multiple access channel (MAC), which significantly reduces the bandwidth requirement or detection delay. We assume that local observations are mapped according to a certain function subject to a power constraint and transmitted simultaneously to the fusion center. Using a large deviation approach, we demonstrate that with a specially-chosen mapping rule, MAC fusion achieves the same asymptotic performance as centralized detection under the average power constraint (APC), while there is always a loss in error exponents associated with PAC fusion. Under the total power constraint (TPC), MAC fusion still results in exponential decay in error exponents with the number of sensors, while PAC fusion does not. Finally, we derive an upper bound on the performance loss due to the lack of perfect synchronization over MAC, and show that the performance degradation is negligible when the phase mismatch among sensors is sufficiently small

Different from conventional multiuser scheduling schemes targeting on optimizing the physical layer performance criteria such as Shannon capacity and error probability, in this paper we propose a family of new scheduling methods that take the packet throughput in upper-layer protocols into considerations. This new performance metric appears to be an elegant combination of spectral efficiency and channel reliability, which are two usually competitive performance indicators for practical transmitters and receivers. Our results show that regarding the packet throughput, the proposed schedulers may result in great performance advantage over conventional schemes, especially at low to intermediate SNR. Furthermore, the amount of system delay for each user is investigated together with packet throughput for scheduling algorithm designs. A novel scheduler, considering both system performance and user delays, is then proposed, and the corresponding delay-throughput tradeoff curve is drawn, which actually provides a useful and flexible platform to evaluate any scheduling schemes, if both factors are under concern

In this paper, we propose a crosslayer approach that explores Tomlinson-Harashima precoding (THP) at the physical layer to reduce the multiuser scheduling burden at the MAC layer, and improves the sum rate of the downlink multiuser MIMO system. Our proposed scheme is further evaluated with imperfect feedback, obtained by the long range prediction (LRP) technique. Compared to some existing scheduling schemes, the proposed scheme approaches the performance upper bound in certain scenarios, while incurring much less computation complexity. Significant gains are still maintained with imperfect channel state information (CSI), fed back at a rate much lower than the data rate

In recent years, the remarkable ability of multiple-input-multiple-output (MIMO) wireless communication systems to provide spatial diversity or multiplexing gains has been clearly demonstrated. For MIMO diversity schemes, it is well known that antenna selection methods that optimize the postprocessing signal-to-noise ratio (SNR) can preserve the diversity order of the original full-size MIMO system. On the other hand, the diversity order achieved by antenna selection in spatial multiplexing systems, especially those exploiting practical coding and decoding schemes, has not thus far been rigorously analyzed. In this paper, a geometrical framework is proposed to theoretically analyze the diversity order achieved by transmit antenna selection for separately encoded spatial multiplexing systems with linear and decision-feedback receivers. When two antennas are selected from the transmitter, the exact achievable diversity order is rigorously derived, which previously only appears as conjectures based on numerical results in the literature. If more than two antennas are selected, we give lower and upper bounds on the achievable diversity order. Furthermore, the same geometrical approach is used to evaluate the diversity-multiplexing tradeoff in spatial multiplexing systems with transmit antenna selection

Energy efficiency is one of the most critical concerns for wireless sensor networks. While cooperative transmission strategies have the potential to significantly improve the system performance, they also incur additional energy cost and system overhead. In this paper, energy efficiency of relevant transmission strategies is studied both for wideband asymptotes and realistic system settings. Based on this analysis, general guidelines are presented for optimal transmission strategy selection in some typical scenarios, aiming at minimum energy consumption with a target BER. The proposed selection rules, especially those based on system-level metrics, are easy to implement for sensor applications. The framework provided here may also be readily extended to other scenarios or applications.

In the context of antenna selection for MIMO diversity systems, the problem of optimal diversity order is well addressed. On the other hand, the diversity order achieved by antenna selection in spatial multiplexing systems, especially those exploiting practical coding and decoding schemes, has not been rigorously analyzed thus far. In Zhang, H, et al. (2005), we propose a new geometrical framework for theoretically analyzing the achievable diversity order when L = 2 transmit antennas are selected for an N/sub R/ /spl times/ N/sub T/ SM system with linear receivers. In this paper, we extend it to the general scenarios with 2 /spl les/ L /spl les/ N/sub T/, and both linear and decision feedback receivers are considered. A diversity order of (N/sub T/-L+1)(N/sub R/-L+1) are rigorously shown to be achievable by the optimal selection, which was previously partly conjectured by other researchers through simulation results.

Linear multiuser detectors and receive antenna arrays enhance the received signal-to-noise ratio, thereby improving both the throughput and the energy efficiency of sensor networks. We assume Rayleigh flat-fading and no uplink channel state information, and derive analytically the throughput of large sensor networks with linear multiuser detectors and spatial diversity, for both deterministic scheduling and slotted ALOHA multiple access. We introduce the notion of effective energy, and show that it can be minimized by judiciously choosing the number of simultaneous transmissions and the transmission power.

For MIMO diversity schemes, it is well known that antenna selection methods that optimize the post-processing signal-to-noise ratio can preserve the diversity order of the full MIMO system. On the other hand, the diversity order achieved by antenna selection in spatial multiplexing (SM) systems, especially those exploiting practical coding and decoding schemes, has not thus far been rigorously analyzed. In this paper, from a geometrical standpoint, we propose a new framework to theoretically analyze the diversity order achieved by transmit antenna selection for independently encoded SM systems with linear receivers. Our results show that a diversity order of (NT-1)(NR-1) can be achieved for an NRtimesNTSM system in which L = 2 antennas are selected from the transmit side

The capacity of downlink cellular multiple-input multiple-output (MIMO) systems, where co-channel interference is the dominant channel impairment, is investigated in this paper, mainly from a signal-processing perspective. Turbo space-time multiuser detection (ST MUD) is employed for intracell communications and is shown to closely approach the ultimate capacity limits in Gaussian ambient noise for an isolated cell. Then, it is combined with various multiuser detection methods for combating intercell interference. Among various multiuser detection techniques examined, linear minimum-mean-square-error (MMSE) MUD and successive interference cancellation are shown to be feasible and effective. Based on these two multiuser detection schemes, one of which may outperform the other for different settings, an adaptive detection scheme is developed, which together with a Turbo ST MUD structure offers substantial performance gain over the well-known V-BLAST techniques with coding in this interference-limited cellular environment. The obtained multiuser capacity is excellent in the high to medium signal-to-interference ratio scenario. Nonetheless, numerical results also indicate that a further increase in system complexity, using base-station cooperation, could lead to further significant increases of the system capacity. The asymptotic multicell MIMO capacity with linear MMSE MUD preprocessing is also derived, and this analysis agrees well with the simulation results.

The spectral efficiency of multiple-input multiple-output (MIMO) systems operating in multicell frequency-flat fading environments is studied, for situations in which cochannel interference is the dominant channel impairment instead of ambient noise. The following multiuser detection methods are analyzed: a single-cell detector, the joint optimum detector, a group linear minimum-mean-square-error (MMSE) detector and its generalized version, with the focus on their large-system asymptotic (non-random) expressions. Analytical and numerical results based on these asymptotic multicell MIMO spectral efficiencies are explored to gain insights into the behavior of multicell MIMO systems.

Wireless CDMA cellular downlink communications with transmit antenna arrays in multipath fading channels is studied. Transmit diversity and various beamforming techniques are investigated and compared, in conjunction with power control. No instantaneous downlink channel information is assumed; however, the obtained results are also compared with results assuming ideal feedback. The study is carried out for both circuit-switched and packet-switched systems, for which different conclusions are drawn.

In a paper by Dai and Molisch (see Proc. IEEE VTC 2002 Spring, Birmingham, AL, May 2002), space-time layered architectures (BLAST) and turbo coding/processing techniques, which are effective for single-link transmission, are combined with multiuser detection (MUD) methods for combating intercell interference. It is shown that, depending on the channel configuration, linear MMSE or successive interference cancellation (SIC) can be the preferable MUD method. Based on this fact, an adaptive scheme is developed. T/sup h/e downlink capacity of interference-limited multiple-input multiple-output (MIMO) cellular systems is investigated. It is found that the obtained MUD capacity is excellent in high to medium SIR scenario, but still deteriorates in strong interference environments, leaving ample room for possible improvement through other techniques. The performance of the proposed adaptive MUD scheme in a standard cellular environment, both in the non-line-of-sight (NLOS) and the line-of-sight (LOS) case, is simulated, and the advantages over the standard V-BLAST scheme are quantified.

We consider the application of multiuser detection to the downlink of interference-limited multiple-input multiple-output (MIMO) cellular systems. MIMO systems have been shown to yield a tremendous capacity for a single link with white Gaussian noise. In a cellular environment, there will often be co-channel interference from other cells, which becomes the dominating channel impairment. Here space-time layered architectures and turbo processing techniques are combined with multiuser detection techniques for combating intercell interference. Among various multiuser detection methods examined, linear MMSE and successive interference cancellation have been shown to be feasible and effective. Based on these two multiuser detection schemes, one of which may outperform the other for different settings, an adaptive detection scheme is developed.

In recent years, iterative processing techniques with soft-in/soft-out components have received considerable attention. Such techniques, based on the so-called turbo principle, are exemplified through turbo decoding, turbo equalization, and turbo multiuser detection. Turbo multiuser detection is applied to a discrete multitone (DMT) very-high-rate digital subscriber line system to combat crosstalk signals and to obtain substantial coding gain. The proposed iterative DMT receiver is shown to achieve an overall 7.0 dB gain over the uncoded optimum receiver at a bit error rate of 10/sup -7/ for a channel with severe intersymbol interference and additive white Gaussian noise and with one dominant crosstalk signal. Impulse noise is detrimental to the proposed scheme but can be overcome through erasure decoding techniques, as is shown by example.

Multiuser detection and space-time processing are two advanced signal-processing techniques for the mitigation of multiple-access interference and intersymbol interference in wireless CDMA communications. The purpose of this work is to investigate techniques for efficient space-time multiuser detection (ST MUD). A companion paper considered batch iterative methods, which assume knowledge of all signals and channels. In this paper sample-by-sample adaptive methods, both data-aided (with training sequences) and blind, which require only the timing and training sequences (for data-aided) or the spreading codes (for blind) of the desired user(s), are considered. For data aided adaptive methods, a decentralized adaptive minimum-mean-square-error space-time multiuser detector and a centralized adaptive decision-feedback space-time multiuser detector are presented. Then a blind adaptive space-time multiuser receiver based on the linear constrained minimum variance criterion and min-max parameter estimation is developed, which is robustified with norm-constrained techniques in the case of signature waveform mismatch. A least mean square implementation of all these adaptive ST MUD receivers is given.