Enterprise data centers deploying OpenClaw agent ecosystems face a novel threat: prompt worms propagating silently across MCP-connected agent networks without exploiting any software vulnerability. We propose OpenClaw-Shield, a three-component network-layer framework driving the basic reproduction number R0 below the epidemic threshold via C1 (Trust Policy Manager), C2 (MCP Gateway Filter), and C3 (Propagation Risk Monitor). Evaluation on a 100-agent data center overlay network (n = 30) shows: all topologies yield median > \(92\%\) compromise without protection (R0 ≈ 16.2); C1 reduces compromise to \({\lt }5\%\) at zero infrastructure cost; and C2 achieves ∼ \(99\%\) reduction as the most effective single component.

Large language models (LLMs) are increasingly deployed across the edge–cloud continuum, from browser-local runtimes to local edge servers and cloud services. However, these backends expose different interfaces and deployment assumptions. We present WebGate, a Chromium-based browser-native edge gateway that routes prompts to either in-browser WebLLM inference or configurable HTTP endpoints such as local Ollama servers and remote APIs. We describe its architecture, CORS-aware communication, and preliminary latency evaluation. Results show that API/Ollama execution is about 10 times faster than WebLLM on the tested laptop, while WebLLM offers client-side execution and lower setup effort.

In recent years, Large Language Models (LLMs) have transformed computer science education by offering instant code generation, yet these tools often encourage passive completion where students rely on trial-and-error rather than deep algorithmic design. To address this limitation, we propose Design Before Code, a novel interactive framework that enforces a validated visual plan before implementation to bridge the gap between high-level intent and low-level syntax. Our system utilizes a Visual Control Flow Graph analysis powered by Personalized PageRank, which identifies the structurally critical regions of an algorithm by assigning higher weights to complex decision and loop structures. To mitigate dependency, we introduce an interactive agent that gates the code editor based on these structural weights, providing inquiry-based hints instead of full solutions. In a controlled study with 120 students across five diverse algorithmic tasks, Design Before Code achieved an approximately 80% Logic Preservation Rate, exhibiting a significant improvement of 24% compared to the LLM-only baseline. Furthermore, our approach reduced unproductive debugging cycles by 60% while achieving an 86% score on independent transfer tasks, demonstrating that structural scaffolding fosters superior long-term conceptual retention compared to traditional methods.

Learning competitive programming and advanced data structures presents a significant challenge for students due to the high cognitive load and the need for long-term concept retention. Traditional online judge platforms provide a static, one-size-fits-all experience that fails to adapt to individual student skill levels or address the exploration-exploitation tradeoff inherent in learning. In this paper, we introduce MyCodeWeapon, a novel learning platform that provides a personalized and adaptive educational environment for competitive programming. MyCodeWeapon integrates three algorithmic components: (1) a dynamic difficulty calibration model using an Elo rating system with an adaptive K-factor to jointly estimate student ability and problem difficulty; (2) a contextual multi-armed bandit based on Thompson Sampling for adaptive problem recommendation that balances exploration of new topics with exploitation of existing skills; and (3) a spaced repetition scheduler inspired by the FSRS algorithm to promote long-term concept retention. We describe the architecture of the MyCodeWeapon platform---built on Next.js, Supabase, and Judge0---and report a pilot study showing that the adaptive group achieved higher post-test scores, attempted more problems, and demonstrated greater learning efficiency. This work aims to demonstrate a more effective and engaging paradigm for scaling programming education.

The widespread use of AI-generated text has introduced significant security concerns, driving the need for reliable detection systems. However, recent studies reveal that neural network-based detectors are vulnerable to adversarial examples. To improve the robustness of such classifiers, a number of adversarial attack strategies have been developed, particularly in the context of text sentiment classification. Most existing adversarial attack methods focus on the semantics of individual words or sentences, often neglecting the broader contextual semantics of the entire text-particularly in the case of long AI-generated text. This limitation frequently results in adversarial examples that lack fluency and coherence. In this paper, we propose a novel method calledSentence-based Adversarial attack on AI-Generated Text detectors (SAGT), which generates linguistically fluent adversarial examples by inserting model-generated sentences into the original text. To ensure contextual semantic consistency, we extract important keywords from the original text-selected based on changes in the detector's confidence score-and incorporate them into the generated sentences. Extensive experimental results demonstrate that adversarial examples crafted bySAGTcan effectively evade AI-generated text detectors.

Intelligent air-ground integration communication is an emerging technology. Uncrewed aerial vehicles (UAVs) serve as mobile edge computing (MEC) servers in large-scale Internet of Things (IoT) applications, alleviating the computational load on ground users. Existing multi-UAV MEC approaches struggle with the complex computation and large data sizes of deep neural network tasks. To address these challenges, we propose a Deep Reinforcement Learning (DRL)-based DNN Partitioning and Dynamic Trajectory Selection (DPDTS) method, which reduces end-to-end latency and system energy consumption through task offloading and collaborative inference. Specifically, we propose an Optimal Partition Point Selection (OPPS) algorithm to minimize transmission overhead by selecting optimal partition points for DNN tasks. Then, we design a fairness-based matching algorithm to optimize user offloading and resource allocation. Finally, OPPS and matching algorithms are integrated to optimize UAV flight trajectories and user transmission power via DRL. The simulation results show that DPDTS outperforms existing benchmark methods in terms of delay and energy efficiency.

Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living. Meanwhile, vehicle crowdsensing (VCS) has emerged as a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities. In particular, ride-hailing vehicles can effectively facilitate flexible data collection and contribute towards urban intelligence, despite resource limitations. Therefore, this work explores a promising scenario, where edge-assisted vehicles perform joint tasks of order serving and the emerging foundation model finetuning using various urban data. However, integrating the VCS AI task with the conventional order serving task is challenging, due to their inconsistent spatio-temporal characteristics: (i) The distributions of ride orders and data point-of-interests (PoIs) may not coincide in geography, both following a priori unknown patterns; (ii) they have distinct forms of temporal effects, i.e., prolonged waiting makes orders become instantly invalid while data with increased staleness gradually reduces its utility for model fine-tuning. To overcome these obstacles, we propose an online framework based on multi-agent reinforcement learning (MARL) with careful augmentation. A new quality-of-service (QoS) metric is designed to characterize and balance the utility of the two joint tasks, under the effects of varying data volumes and staleness. We also integrate graph neural networks (GNNs) with MARL to enhance state representations, capturing graph-structured, time-varying dependencies among vehicles and across locations. Extensive experiments on our testbed simulator, utilizing various real-world foundation model fine-tuning tasks and the New York City Taxi ride order dataset, demonstrate the advantage of our proposed method.

This paper introduces a deep reinforcement learning-based block coordinate descent (DRL-based BCD) algorithm to address the nonconvex weighted sum-rate maximization (WSRM) problem with a total power constraint. Firstly, we present an efficient block coordinate descent (BCD) method to solve the problem. While this method may not always achieve globally optimal solutions, it provides a pathway for integrating machine learning and domain-specific techniques with theoretical analysis of the underlying convexity of the subproblems. We then integrate deep reinforcement learning (DRL) techniques into the BCD method and propose the DRL-based BCD algorithm. This approach combines the data-driven learning capability of machine learning techniques with the navigational and decision-making characteristics of the optimization-theoretic-based BCD method. This combination significantly improves the algorithm’s performance by reducing its sensitivity to initial points and mitigating the risk of entrapment in local optima. The primary advantages of the proposed DRL-based BCD algorithm lie in its ability to adhere to the constraints of the WSRM problem and significantly enhance accuracy, potentially achieving the exact optimal solution. Moreover, unlike many pure machine-learning approaches, the DRL-based BCD algorithm capitalizes on the underlying theoretical analysis of the WSRM problem’s structure. This enables it to be easily trained and computationally efficient while maintaining a level of interpretability. Moreover, the DRL-based BCD framework demonstrates strong extensibility and can effectively be applied to other scenarios, such as joint beamforming for sum rate maximization, as demonstrated in this paper. Through numerical experiments, the DRL-based BCD algorithm demonstrates substantial advantages in effectiveness, efficiency, robustness, and interpretability for maximizing sum rates, which also provides valuable potential for designing resource-constrained AI-native wireless optimization strategies in next-generation wireless networks.

Novelty is a critical attribute in academic publishing, guiding researchers toward genuinely groundbreaking contributions and shaping influential research trajectories. Motivated by the need to better quantify novelty, this paper presents an analysis of research novelty and impact in scholarly work. We develop a quantitative metric that considers both the originality and influence of research work to measure the contributions of each publication to novelty within the constructed citation network and perform a content similarity analysis to assess how closely each work builds upon its predecessors. As a preliminary study, we apply this methodology to a set of seminal publications in the domain of artificial intelligence, tracking their current citation outcomes. Results show that the inaugural work exhibits the highest novelty and garners the most citations, whereas later, more incremental works achieve varied levels of influence. We also observe that content similarity between a citing work and the work it references tends to be inversely related to novelty, reflecting whether the new work represents a significant departure or a refinement of prior research. This combined analysis offers insights into the relationship between novelty and impact in the evolution of research publications.

In remote or high-demand wireless cellular networks, efficient data collection from ground users (GUs) with fixed infrastructure poses a significant challenge. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their flexible deployment and cost-effectiveness. This paper focuses on a UAV-aided wireless cellular communication system comprising a UAV and multiple adjacent GUs, where the mission of the UAV is to collect data from these GUs. The objective is to minimize UAV propulsion energy consumption while ensuring fair data uploading among all GUs. Due to the non-convex and intractable nature of the above problem, we propose a novel real-time waypoint localization method based on the parallel projection method from a geometric perspective. By enhancing the projection process, this approach achieves energy-efficient and fair data collection, along with an efficient trajectory design algorithm. Further, considering the scenario of densely distributed GUs in large-scale areas, a GU-clustering algorithm is proposed based on Mean Shift. Additionally, this paper categorizes GUs into homogeneous and heterogeneous scenarios and designs distinct trajectory designing algorithms to accommodate diverse real-world situations. Simulations and comparisons validate the effectiveness and efficiency of the proposed algorithms in tackling the UAV trajectory design challenges.

Sharding presents an enticing path toward improving blockchain scalability. However, the consensus mechanism within individual shards faces mounting security challenges due to the restricted number of consensus nodes and the reliance on conventional, unchanging nodes for consensus. Common strategies to enhance shard consensus security often involve increasing the number of consensus nodes per shard. While effective in bolstering security, this approach also leads to a notable rise in consensus delay within each shard, potentially offsetting the scalability advantages of sharding. Hence, it becomes imperative to strategically select nodes to form dedicated consensus groups for each shard. These groups should not only enhance shard consensus security but also do so without exacerbating consensus delay. In this article, we propose a novel consensus group selection based on transmission delay between nodes (CGSTD) to address this challenge, with the goal of minimizing the overall consensus delay across the system. CGSTD intelligently selects nodes from various shards to form distinct consensus groups for each shard, thereby enhancing shard security while maintaining optimal system-wide consensus efficiency. We conduct a rigorous theoretical analysis to evaluate the security properties of CGSTD and derive approximation ratios under various operational scenarios. Simulation results validate the superior performance of CGSTD compared to baseline algorithms, showcasing reductions in total consensus delay, mitigated increases in shard-specific delay, optimized block storage utilization per node, and streamlined participation of nodes in consensus groups.

Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model. Although various methods exist for detecting such clients in FL, identifying malicious clients requires sufficient model updates, and hence by the time malicious clients are detected, FL models have already been poisoned. Thus, a method is needed to recover an accurate global model after malicious clients are identified. Current recovery methods rely on (i) all historical information from participating FL clients and (ii) the initial model unaffected by the malicious clients, both leading to a high demand for storage and computational resources. In this paper, we show that highly effective recovery can still be achieved based on 1) selective historical information rather than all historical information and 2) a historical model that has not been significantly affected by malicious clients rather than the initial model. In this scenario, we can accelerate the recovery speed and decrease memory consumption while maintaining comparable recovery performance. Following this concept, we introduce Crab (Certified Recovery from Poisoning Attacks and Breaches), an efficient and certified recovery method, which relies on selective information storage and adaptive model rollback. Theoretically, we demonstrate that the difference between the global model recovered by Crab and the one recovered by train-from-scratch can be bounded under certain assumptions. Our experiments, performed across four datasets with multiple machine learning models and aggregation methods, involving both untargeted and targeted poisoning attacks, demonstrate that Crab is not only accurate and efficient but also consistently outperforms previous approaches in recovery speed and memory consumption.

Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data are remembered by AI models during the machine learning process. It enables the removal of specific data influences from trained models, aligning with the growing emphasis on the “right to be forgotten.” While extensively studied in horizontal federated learning, unlearning in vertical federated learning (VFL) remains challenging due to the distributed feature architecture. VFL unlearning includes sample unlearning that removes specific data points’ influence and label unlearning that removes entire classes. Since different parties hold complementary features of the same samples, unlearning tasks require cross-party coordination, creating computational overhead and feature interdependencies. To address such challenges, we propose FedORA (Federated Optimization for data Removal via primal-dual Algorithm), designed for sample and label unlearning in VFL. FedORA formulates the removal of certain samples or labels as a constrained optimization problem solved using a primal-dual framework. Our approach introduces a new unlearning loss function that promotes classification uncertainty rather than misclassification. An adaptive step size enhances convergence, while an asymmetric batch design handles unlearning and retained data efficiently to reduce computational costs, considering the prior influence of the remaining data on the model. We provide theoretical analysis proving that the model difference between FedORA and Train-from-scratch is bounded, establishing guarantees for unlearning effectiveness. Experiments on tabular and image datasets demonstrate that FedORA achieves unlearning effectiveness and utility preservation comparable to Train-from-scratch with reduced computation and communication overhead.

Deep hashing has been widely used in image retrieval tasks, while deep hashing networks are vulnerable to adversarial example attacks. To improve the deep hashing networks’ robustness, it is essential to investigate adversarial attacks on the networks, especially targeted attacks. Among the existing targeted attacks for hashing, the generation-based targeted attack methods have attracted increasing attention due to their efficiency in generating adversarial examples. However, these methods supervise the generation of adversarial examples solely with the hash codes of positive samples, without employing the hash codes of all points in the training set to directly participate in supervisory training, thereby making the attack less effective. Since the hash codes of the training set samples are generated by a well-trained hashing model, these hash codes retain rich semantic information of their corresponding samples, highlighting the necessity of sufficiently utilizing them. Therefore, in this paper, we propose a targeted attack method that utilizes all points’ hash codes in the training set to guide the generation of adversarial attack examples directly. Specifically, we first decode the target label to obtain the corresponding feature map. Then, we concatenate the feature map with the query image and feed them into an encoder-decoder network that employs a skip-connection strategy to obtain a perturbed example. Furthermore, to guide adversarial example generation, we introduce a loss function that exploits the similarities between the perturbed example’s hash code and all points’ hash codes in the training set, thereby making sufficient utilization of the rich semantic information in these hash codes. Experimental results illustrate that our method outperforms the state-of-the-art targeted attack methods in targeted attack effectiveness and transferability. The code is available athttps://github.com/rongxintu3/APGA.

Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients’ data on the global model in federated learning (FL), thereby granting individuals the "Right to be Forgotten". The most straightforward approach to achieve unlearning is to train the model from scratch, excluding clients who request data removal, but it is resource-intensive. Current state-of-the-art FU methods extend traditional FL frameworks by leveraging stored historical updates, enabling more efficient unlearning than training from scratch. However, the use of stored updates introduces significant privacy risks. Adversaries with access to these updates can potentially reconstruct clients’ local data, a well-known vulnerability in the privacy domain. While privacy-enhanced techniques exist, their applications to FU scenarios that balance unlearning efficiency with privacy protection remain underexplored. To address this gap, we propose FedADP, a method designed to achieve both efficiency and privacy preservation in FU. Our approach incorporates an adaptive differential privacy (DP) mechanism, carefully balancing privacy and unlearning performance through a novel budget allocation strategy tailored for FU. FedADP also employs a dual-layered selection process, focusing on global models with significant changes and client updates closely aligned with the global model, reducing storage and communication costs. Additionally, a novel calibration method is introduced to facilitate effective unlearning. Extensive experimental results demonstrate that FedADP effectively manages the trade-off between unlearning efficiency and privacy protection.

Federated Learning (FL) is a distributed machine learning paradigm that achieves a globally robust model through decentralized computation and periodic model synthesis, primarily focusing on the global model’s accuracy over aggregated datasets of all participating clients. Personalized Federated Learning (PFL) instead tailors exclusive models for each client, aiming to enhance the accuracy of clients’ individual models on specific local data distributions. Despite of their wide adoption, existing FL and PFL works have yet to comprehensively address the class-imbalance issue, one of the most critical challenges within the realm of data heterogeneity in PFL and FL research. In this paper, we propose FedReMa, an efficient PFL algorithm that can tackle class-imbalance by 1) utilizing an adaptive inter-client co-learning approach to identify and harness different clients’ expertise on different data classes throughout various phases of the training process, and 2) employing distinct aggregation methods for clients’ feature extractors and classifiers, with the choices informed by the different roles and implications of these model components. Specifically, driven by our experimental findings on inter-client similarity dynamics, we develop critical co-learning period (CCP), wherein we introduce a module named maximum difference segmentation (MDS) to assess and manage task relevance by analyzing the similarities between clients’ logits of their classifiers. Outside the CCP, we employ an additional scheme for model aggregation that utilizes historical records of each client’s most relevant peers to further enhance the personalization stability. We demonstrate the superiority of our FedReMa in extensive experiments. The code is available at https://github.com/liangh68/FedReMa.

Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

We consider the problem of identifying the source of a rumor in a network. Given a snapshot observation of the network, in which a rumor has been spreading for some time, how to identify the source from which the rumor started to spread? In this paper, we point out the limitations of existing estimators in the literature. As a remedy, we put forth a new estimator by incorporating an independent random observation time. To capture the structure of information flow beyond graphs, our formulations consider rate constraints on the rumor and the multicast capacities for cyclic polylinking networks.

Federated learning is a machine learning framework that facilitates training a shared model from distributed clients. However, challenges persist in optimizing communication efficiency. In this paper, we focus on hierarchical federated learning and model its global aggregation as a network function computation problem, where the central server desires to compute the arithmetic sum of the clients' gradients. Inspired by network coding, we propose two Coded Hierarchical Federated Learning (CHFL) approaches to enhance communication efficiency. The first approach, Separated CHFL (S-CHFL), involves transmitting divided segments separately to relays using a greedy algorithm. We establish the upper and lower bounds of the computing rate, showing that S-CHFL can achieve perfect balance in reverse combination network and reach the upper bound in certain networks. The second approach is Mixed CHFL (M-CHFL) where divided segments are mixed into linear combinations for transmission. We show that M-CHFL may be more efficient when data comes from a sufficiently large alphabet and analyze its upper bound for the computing rate.

Federated learning facilitates collaborative machine learning, enabling multiple participants to collectively develop a shared model while preserving the privacy of individual data. The growing importance of the “right to be forgotten” calls for effective mechanisms to facilitate data removal upon request. In response, federated unlearning (FU) has been developed to efficiently eliminate the influence of specific data from the model. Current FU methods primarily rely on approximate unlearning strategies, which seek to balance data removal efficacy with computational and communication costs, but often fail to completely erase data influence. To address these limitations, we propose FedUHB, a novel exact unlearning approach that leverages the Polyak heavy ball optimization technique, a first-order method, to achieve rapid retraining. In addition, we introduce a dynamic stopping mechanism to optimize the termination of the unlearning process. Our extensive experiments show that FedUHB not only enhances unlearning efficiency but also preserves robust model performance after unlearning. Furthermore, the dynamic stopping mechanism effectively reduces the number of unlearning iterations, conserving both computational and communication resources. FedUHB can be proved as an effective and efficient solution for exact data removal in federated learning settings.

Cognitive satellite-terrestrial networks (CSTNs) are a promising technology that optimize satellite efficiency and coverage. In this paper, we present a novel QoS-aware, deep learning (DL) algorithm that integrates with space-air-ground channels and exploits beam utilization by maximizing user admittance. To this end, we characterize the spectral radius as a critical concept that is indicative of resource utilization in the multibeam cluster. Our findings show that a smart use of the spectral radius, learned with ordinary differential equation networks (ODE-Nets), could maximize user admittance and outperform baselines like overlay and underlay, and predict the optimal power allocation vector.

Since data science is traditionally an advanced field taught at the college or university level, introducing its concepts to K-12 students can present unique learning challenges. As educational environments increasingly adopt data science curricula for K-12 students, the need for scalable, personalized teaching tools becomes critical. While the integration of large language models (LLMs) in educational environments offers significant potential for scalability and automation, it is important to note that the generated language output may not always be highly suitable for K-12 students. In this paper, we introduce the DSRAG, a novel educational automatic feedback generation framework that leverages Retrieval-Augmented Generation (RAG) and cloud-based LLMs to provide automated and personalized feedback for K-12 students engaged in data science education. DSRAG employs Langchain question-answering and RAG systems to manage educational content and generate feedback on the top of GPT-4. We also demonstrate the framework's capability to simplify complex concepts and align its responses to be pedagogically appropriate and understandable for K-12 students.

Artificial intelligence (AI) permeates modern society and is poised for further integration across various domains. However, there exists a notable deficiency in equipping K-12 students with foundational AI understanding. This paper introduces a novel learning framework that leverages large language models (LLMs) and strategic gaming to teach K-12 students about the inner workings of AI. The framework consists of a chatbot programming and testing IDE that enables K-12 students to construct AI from scratch, engage in strategic gameplay to generate instant training data, and improve the AI heuristics with a data-driven learning mechanism. With a tiered curriculum catering to diverse proficiency levels and fostering synchronous collaboration, this framework efficiently adapts learning experiences to suit various groups of students, thereby facilitating learning at scale. Preliminary experiments validate the feasibility and vast potential of this approach, promising to revolutionize AI education in K-12 education.

Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learning, large-scale models have garnered significant attention due to their exceptional performance. However, a key challenge in FL is the limitation imposed by clients with constrained computational and communication resources, which hampers the deployment of these large models. The Mixture of Experts (MoE) architecture addresses this challenge with its sparse activation property, which reduces computational workload and communication demands during inference and updates. Additionally, MoE facilitates better personalization by allowing each expert to specialize in different subsets of the data distribution. To alleviate the communication burdens between the server and clients, we propose FedMoE-DA, a new FL model training framework that leverages the MoE architecture and incorporates a novel domain-aware, fine-grained aggregation strategy to enhance the robustness, personalizability, and communication efficiency simultaneously. Specifically, the correlation between both intra-client expert models and inter-client data heterogeneity is exploited. Moreover, we utilize peer-to-peer (P2P) communication between clients for selective expert model synchronization, thus significantly reducing the server-client transmissions. Experiments demonstrate that our FedMoE-DA achieves excellent performance while reducing the communication pressure on the server.

The Electric Vehicle (EV) market is rapidly expanding as more individuals shift from traditional fuel-powered vehicles to EVs. This shift requires the broad deployment of Electric Vehicle Supplement Equipment (EVSE), including charging stations, to support the growing EV infrastructure. This paper introduces a semi-supervised learning approach to optimize EVSE charging station deployment. We propose a label-propagation algorithm to consider structural similarities across distant nodes in EVSE points in the electric vehicle charging networks, enabling more accurate similarity extraction. Our results demonstrate that this approach has potential for improving the accuracy and efficiency of EVSE charging station deployment. Our semi-supervised method can be applied to optimize efficient charging station deployment in a simulation for digital twin use.

Sharding is a promising strategy to enhance blockchain scalability. However, the surge in transactions has led to heightened relations between nodes in the system, reflecting the volume of transactions between them. The increase in related nodes engaging in identical transactions across diverse shards leads to substantial cross-shard transactions, contributing to communication delays and impeding enhancements in throughput. Current methods typically employ greedy or heuristic approaches to organize nodes into shards, resulting in marginal reductions in the total relation between related nodes in different shards (i.e., the number of cross-shard transactions), while causing shard imbalance. Hence, there is a crucial need for periodic shard reorganization based on node relations to minimize the total relation between related nodes across different shards while ensuring shard balance. In this article, we investigate the reorganization of nodes into shards based on node relations in sharding blockchains, aiming to minimize the total relation between related nodes in different shards. We formulate the shard reorganization problem and introduce the shard reorganization algorithm based on the relation between nodes (SRRN) to address this issue. Theoretical analysis proves that SRRN is a$2\lambda M$-approximation algorithm, where$\lambda=({r_{\max}}/{r_{\min}})$, with$M$representing the number of shards, and$r_{\max}$and$r_{\min}$denoting the maximum and minimum nonzero relations between nodes, respectively. Simulation results demonstrate that SRRN outperforms baseline algorithms in terms of total relation, degree of relation reduction, differences in computing power between shards, cross-shard ratio, and throughput.

In this paper, we propose neural sum rate maximization, which is a neural network-based approach to tackle the nonconvex problem of maximizing the weighted sum rates with individual power constraints. Neural sum rate maximization combines both novel iterative optimization methods with data-driven models to deliver computationally efficient solution that learns the underlying statistics of the wireless network. Further-more, the solution can be refined by successive convex approximation and algorithm unrolling to accelerate the convergence of the neural sum rate maximization model training. We show that our algorithm is efficient for solving large-scale sum rate maximization problem. Numerical results validate the soundness and practicality of the proposed algorithm.

Compared to single unmanned aerial vehicle (UAV), multi-UAVs formation has garnered significant attention due to their advantages in collaborative task execution, task allocation, as well as heightened redundancy and reliability. This paper investigates a UAV rendezvous system comprising multiple UAVs with random positions and velocities. We introduce the concept of a virtual leader, with the objective of transforming the formation control problem of UAVs into tracking the positional movements of the virtual leader. We model the exploration of the optimal position for the virtual leader as a Markov decision process and propose a variablestep exploration algorithm combined with deep reinforcement learning techniques, guiding the virtual leader towards its optimal position from its initial location. The experimental simulation results ultimately demonstrate the effectiveness of our approach.

The integration of sensing and communication (ISAC) using millimeter-wave (mmWave) attracted significant attentions recently in the research on Internet of Vehicles (IoV). While the ISAC technique offers advantages with increased bandwidth and improved time-frequency resolution, it encounters challenges in achieving accurate beam alignments, especially in highly dynamic scenarios. To enhance the accuracy of narrow beam alignment while mitigating the overhead in vehicular networks, this paper proposes an improved predictive beam tracking scheme based on the transceiver division structure and the bistatic radar theory. Specifically, the proposed scheme utilizes the ISAC echo signals to measure the vehicles' states and adopts an unscented Kalman filter (UKF) to reduce the overhead while ensuring the quality of service (QoS) in complicated road conditions. Simulation results show that the proposed scheme improves the prediction accuracy of angle of departure (AoD), which consequently ensures higher achievable communication rates in the IoV context.

In large-scale parallel computing systems, machines and the network suffer from non-negligible faults, often leading to system crashes. The traditional method to increase reliability is to restart the failed jobs. To avoid unnecessary time wasted on reboots, we propose an optimal scheduling strategy to enable fault-tolerant reliable computation to protect the integrity of computation. Specifically, we determine the optimal redundancy-failure rate tradeoff to incorporate redundancy into parallel computing units running multiple-precision arithmetics, like the Chinese Remainder Theorem, that are useful for applications such as asymmetric cryptography and fast integer multiplication. Inspired by network coding in distributed storage for disk failures, we propose coding matrices to strategically map partial computation to available computing units, so that the central unit can reliably reconstruct the results of any failed machine without recalculations to yield the final correct computation output. We propose optimization-based algorithms to efficiently construct the optimal coding matrices subject to fault tolerance specifications. Performance evaluation demonstrates that the optimal scheduling effectively reduces the overall running time of parallel computing while resisting wide-ranging failure rates.

The COVID-19 pandemic brought not only global devastation but also an unprecedented infodemic of false or misleading information that spread rapidly through online social networks. Network analysis plays a crucial role in the science of fact-checking by modeling and learning the risk of infodemics through statistical processes and computation on mega-sized graphs. This article proposes MEGA, Machine Learning-Enhanced Graph Analytics, a framework that combines feature engineering and graph neural networks to enhance the efficiency of learning performance involving massive graphs. Infodemic risk analysis is a unique application of the MEGA framework, which involves detecting spambots by counting triangle motifs and identifying influential spreaders by computing the distance centrality. The MEGA framework is evaluated using the COVID-19 pandemic Twitter dataset, demonstrating superior computational efficiency and classification accuracy.

In this paper, we propose a novel chatbot-server computer programming framework for students to learn Artificial Intelligence (AI) by creating game AI chatbot applications, whilst conforming to a distributed frontend-backend application structure (e.g., client-server model). The chatbot interface allows students to share their work over online social networks and invite other human players to test-drive the game AI and to collect data for training of machine learning models by crowdsourcing. We introduce a few test cases in which the framework facilitates the online learning of AI, introduces full-stack software development to students and enables a progressive learning of machine learning education using crowdsourcing.

In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods,data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.

We propose an optimal scheduling strategy to enable fault-tolerant reliable computation to protect the integrity of computation. Specifically, we determine the optimal redundancy-failure rate tradeoff to incorporate redundancy into parallel computing units running multiple-precision arithmetic that are useful for applications such as asymmetric cryptography and fast integer multiplication. Inspired by network coding, we propose coding matrices to strategically map partial computation to available computing units, so that the central unit can reliably reconstruct the results of any failed machine without recalculations to yield the final correct computation output. We propose optimization-based algorithms to efficiently construct the optimal coding matrices subject to fault tolerance specifications. Performance evaluation demonstrates that the optimal scheduling effectively reduces the overall running time of parallel computing while resisting wide-ranging failure rates.

Blended learning often requires alternating between asynchronous pre-class and synchronous in-class activities using online technologies to enhance the overall learning experience. Subject to constraints on desired learning outcome specifications and individual student preference, can we jointly optimize pre-class and in-class tasks to improve the two-way interaction between students and the instructor? We leverage ideas of self-assessment in Just-In-Time Teaching and Peer Instruction to propose an optimization-theoretic framework to analyze the optimal trade-off between the time invested in two different learning tasks for each individual student. We show that the problem can be formulated as a linear program, which can be efficiently solved to determine the optimal amount of time for pre-class and in-class learning. We develop a mobile chatbot software integrated with feedback data analytics to blend asynchronous pre-class quiz assessment together with the synchronous in-class poll-quiz routine of Peer Instruction to achieve classroom flipping that can be used for remote and hybrid teaching and learning.

Thanks to the wide availability of the internet and personal computing devices, online teaching methods like Massive Open Online Courses (MOOC) are becoming an essential part of modern education. While these methods enable educators to reach much more students, the massive volume of assignments to grade places a heavy burden on the instructors. Most online courses remedy this by restricting the question types to simple forms or performing naive peer-grading. These approaches are either too restricted to capture students' learning level, or require heavy supervision from the instructors to ensure the grades are fair. In this paper, we propose a rank-aggregation-based peer-grading method that estimates the quality of each assignment and the probability that each student is grading unbiasedly. The estimation errors have theoretical upper-bounds, and the bounds can be proved to tighten when the problem size increases, which is confirmed by our numerical experiment.

Due to the flexible deployment and low cost of unmanned aerial vehicle (UAV), the integration of UAV and wireless cellular networks is widely regarded as a promising technology to enhance the performance of wireless cellular communications. This paper considers a UAV-aided wireless cellular communication system with multiple adjacent ground users (GUs), where the primary mission of UAV is to collect data from all of the GUs. We take the GUs as the topological nodes and combine their communication ranges to construct a ground topology structure (GTS), with the purpose of designing a reasonable trajectory for the UAV to execute the data collection tasks, while ensuring the fairness of transmission among all of GUs. In order to solve these problems, we utilize parallel projection algorithm onto homogeneous and heterogeneous GTS respectively to obtain a group of waypoints which construct the UAV trajectory, we formulate the fairness of data collection as a min-max problem. Finally, simulation experiments show the trajectory design results of the homogeneous and heterogeneous GTS respectively. Numerical results further vaildate the effectiveness of our proposed algorithms.

Proving or disproving an information inequality is a crucial step in establishing the converse results in coding theorems. However, an information inequality involving more than a few random variables is difficult to be proved or disproved manually. In 1997, Yeung developed a framework that uses linear programming for verifying linear information inequalities. Under the framework, this paper considers a few other problems that can be solved by using Lagrange duality and convex approximation. We will demonstrate how linear programming can be used to find an analytic proof of an information inequality or an analytic counterexample to disprove it if the inequality is not true in general. The way to automatically find a shortest proof or a smallest counterexample is explored. When a given information inequality cannot be proved, the sufficient conditions for a counterexample to disprove the information inequality are found by linear programming. Lastly, we propose a scalable algorithmic framework based on the alternating direction method of multipliers to accelerate solving a multitude of user-specific problems whose overall computational cost can be amortized with the number of users, and present its publicly-available software implementation for large-scale problems.

Growing mobile data usage has led to end users paying substantial data costs, while Internet service providers (ISPs) struggle to upgrade their networks to keep up with demand and maintain high quality-of-service (QoS). This problem is particularly severe for smaller ISPs with less capital. Instead of simply upgrading their network infrastructure, ISPs can pool their networks to provide a good QoS and attract more users. Such a vISP (virtual ISP), for example, Google's Project Fi, allows users to access any of its partner ISPs' networks. We provide the first systematic analysis of a vISP's economic impact, showing that the vISP provides a viable solution for smaller ISPs attempting to attract more users, but may not maintain a positive profit if users' data demands evolve. To do so, we consider users' decisions of whether to defect from their current ISP to the vISP, as well as existing ISPs' decisions on whether to partner with the vISP. We derive the vISP's dependence on user behavior and partner ISPs: users with very light or very heavy usage are the most likely to defect, while ISPs with heavy-usage customers can benefit from declining to partner with the vISP. Our analytical results are verified with extensive numerical simulations.

Proving or disproving linear information theoretic inequalities is a fundamental task in information theory, and it has also been proved to be important in fields like cryptography and quantum communication theory. Manually proving information inequalities involving more than a few random variables can often be tedious or even intractable. In 1997, Yeung proposed a linear programming framework for verifying information inequalities, which was later extended to construct analytical proofs and disproofs. However, in practice this framework can be very slow for inequalities involving more than ten random variables, thus it is impossible to be applied to a wide range of practical problems. In this paper, we further extend this optimization-theoretic framework by reformulating the LPs and applying the Alternating Direction Method of Multipliers (ADMM) technique, where all the subproblems have closed-form solutions and thus can be solved efficiently. The proposed algorithm is also parallelizable so the performance can be further improved by running it on a GPU. An online web service is developed to allow users to prove or disprove their problem-specific inequalities without installing any software package or dependency.

Living in an information era where various online learning contents are rapidly available, students often learn with a combination of multiple learning tasks. In this work we explore the possibilities of using optimization theory to find the optimal trade-off between the time invested in two different completing learning tasks for each individual student. We show that the problem can be formulated as a linear programming problem, which can be efficiently solved to determine the optimal amount of time for each task. We also report our ongoing attempts to apply this theory to our Facebook Messenger chatbot software that can optimize the trade-off between learning and self-assessing in form of MCQs on the chatbot platform.

In the future communication applications, users may obtain their messages that have different importance levels distributively from several available sources, such as distributed storage or even devices belonging to other users. This scenario is the best modeled by the multilevel diversity coding systems (MDCS). To achieve perfect (information-theoretic) secrecy against wiretap channels, this paper investigates the fundamental limits on the secure rate region of the asymmetric MDCS (AMDCS), which include the symmetric case as a special case. Threshold perfect secrecy is added to the AMDCS model. The eavesdropper may have access to any one but not more than one subset of the channels but know nothing about the sources, as long as the size of the subset is not above the security level. The question of whether superposition (source separation) coding is optimal for such an AMDCS with threshold perfect secrecy is answered. A class of secure AMDCS (S-AMDCS) with an arbitrary number of encoders is solved, and it is shown that linear codes are optimal for this class of instances. However, in contrast with the secure symmetric MDCS, superposition is shown to be not optimal for S-AMDCS in general. In addition, necessary conditions on the existence of a secrecy key are determined as a design guideline.

Random linear network coding (RLNC) is a promising network coding solution when the network topology information is not fully available to all the nodes. However, in practice, nodes have partial knowledge of the network topology information. Motivated by this, we investigate the performance of RLNC and obtain different upper bounds on the failure probability of RLNC for the network constrained by different partial network topology information. These upper bounds not only improve the existing ones in the literature, but also show that the partial network topology information can bring benefits to the performance analysis of RLNC. On the other hand, it is observed that if more network topology information can be utilized, tighter upper bounds can be obtained, as expected. The upper bounds on two classical networks are compared for demonstration. To obtain a deeper understanding about the performance of RLNC, the asymptotic behavior of RLNC as the field size goes to infinity is also investigated.

Rate adaptation and power control are two key resource allocation mechanisms in multiuser wireless networks. In the presence of interference, how do we jointly optimize end-to-end source rates and link powers to achieve weighted max-min rate fairness for all sources in the network? This optimization problem is hard to solve as physical layer link rate functions are nonlinear, nonconvex, and coupled in the transmit powers. We show that the weighted max-min rate fairness problem can, in fact, be decoupled into separate fairness problems for flow rate and power control. For a large class of physical layer link rate functions, we characterize the optimal solution analytically by a nonlinear Perron-Frobenius theory through solving a conditional eigenvalue problem that captures the interaction of multiuser interference. We propose an iterative algorithm to compute the optimal flow rate that converges geometrically fast without any parameter configuration. Numerical results demonstrate that our iterative algorithm is computationally fast for the Shannon capacity, CDMA, and piecewise link rate functions.

Finding information source in viral spreading has important applications such as to root out the culprit of a rumor spreading in online social networks. In particular, given a snapshot observation of the rumor graph, how to accurately identify the initial source of the spreading? In the seminal work by Shah and Zaman in 2011, this statistical inference problem was formulated as a maximum likelihood estimation problem and solved using a rumor centrality approach for graphs that are degree-regular. This however is optimal only if there are no boundary effects, e.g., the underlying number of susceptible vertices is countably infinite. In general, all practical real world networks are finite or exhibit complex spreading behavior, and therefore these boundary effects cannot be ignored. In this paper, we solve the constrained maximum likelihood estimation problem by a generalized rumor centrality for spreading in graphs with boundary effects. We derive a graph-theoretic characterization of the maximum likelihood estimator for degree-regular graphs with a single end vertex at its boundary and propose a message-passing algorithm that is near-optimal for graphs with more complex boundary consisting of multiple end vertices.

Energy efficiency is one important consideration in designing wireless mobile networks to support a large number of battery-powered mobile terminals. To provider a wider coverage to serve more users, wireless carrier infrastructure from various mobile network operators (MNOs) can be pooled together. For example, a cellular system user can access different kinds of cellular broadband networks with multiple transceivers. MNOs however have different heterogeneous rate function characteristics, and thus present new challenges to wireless network optimization. In this paper, we study the design of energy-efficient power control algorithms in a network with multiple MNOs using a unique log- convexity property of the standard interference function approach. Furthermore, the power control algorithms can be enhanced using the congestion level at each MNO.

Growing mobile data usage has led to end users paying substantial data costs, while Internet service providers (ISPs) struggle to upgrade their networks to keep up with demand and maintain high quality-of-service (QoS). This problem is particularly severe for smaller ISPs with less capital. Instead of simply upgrading their network infrastructure, ISPs can pool their networks to provide a good QoS and attract more users. Such a vISP (virtual ISP), for example, Google's Project Fi, allows users to access any of its partner ISPs' networks. We provide the first systematic analysis of a vISP's economic impact, showing that the vISP provides a viable solution for smaller ISPs attempting to attract more users, but may not maintain a positive profit if users' data demands evolve. To do so, we consider users' decisions of whether to defect from their current ISP to the vISP, as well as ISPs' decisions on whether to partner with the vISP. We derive the vISP's dependence on user behavior and partner ISPs: users with very light or very heavy usage are the most likely to defect, while ISPs with heavy-usage customers can benefit from declining to partner with the vISP. Our analytical results are verified with extensive numerical simulations.

Whether superposition (source separation) Is optimal for the asymmetric multilevel diversity coding systems (AMDCS) with perfect secrecy is answered in this paper by studying a non-trivial example. Threshold perfect secrecy is added to the AMDCS model. The eavesdropper may have access to any one but not more than one subset of the channels but can get nothing about the sources, as long as the size of the subset is not above the security level. The secure AMDCS (S-AMDCS) with five sources, four encoders and security level two is solved and it is shown that linear codes are optimal for this instance. However, in contrast with the secure symmetric multilevel diversity coding systems (S-SMDCS), superposition is shown to be not optimal for S-AMDCS in general from this counterexample.

In conventional distributed storage exact repair problems, all sources are reconstructed when the decoder has access to a certain number of encoders (disks). So, the underlying reconstruction network is equivalent to a single-source problem. This paper considers a variant of the exact repair problem, where the underlying reconstruction network is the independent distributed source coding problem, a type of multi-source problem. As the first non-trivial case with two sources and three encoders, the storage-repair tradeoff regions are proved for all the 33 instances, and it is shown that binary codes are optimal.

Detecting information source in viral spreading has important applications such as to root out the culprit of a rumor spreading in online social networks. In particular, given a snapshot observation of the network topology of nodes having the rumor, how to accurately identify the initial source of the spreading? In the seminal work [Shah et el. 2011], this problem was formulated as a maximum likelihood estimation problem and solved using a rumor centrality approach for graphs that are degree-regular trees. The case of graphs with cycles is an open problem. In this paper, we address the maximum likelihood estimation problem by a generalized rumor centrality for spreading in unicyclic graphs. In particular, we derive a generalized rumor centrality that leads to a new graph-theoretic design approach to inference algorithms.

Internet service providers (ISPs) struggle to invest in upgrading their networks to catch up with growing mobile data demand, while users have to face significant data overage fees. Pooling ISPs' network infrastructures can potentially enable better user experience and lower prices. For example, Google recently launched a cross-carrier MVNO (mobile virtual network operator) data plan called Project Fi, where users' devices can automatically access either of two partner cellular networks or any available open WiFi network. We consider the economic impact of cross-carrier MVNOs on the mobile data market. We begin by analyzing a network selection strategy that optimizes cross-carrier users' costs. We then study ISPs' behavior, deriving the prices that partner ISPs charge the cross-carrier MVNO and that the cross-carrier MVNO charges its end users. Although the cross-carrier MVNO may lose money from selling data, it can offset this loss with side revenue, e.g., advertisement revenue when users consume more content. We derive conditions under which the cross-carrier MVNO achieves a profit and its users reduce their costs. Finally, we use a real-world network quality dataset to simulate users' network selection behavior and demonstrate the benefits of the ISP competition brought by the cross-carrier MVNO.

The growing volume of mobile data traffic has led many Internet service providers (ISPs) to cap the monthly data usage of their users and to charge overage fees, when the data caps are exceeded. Yet data caps imperfectly capture the reality of heterogeneous data usage over a month-even the same user may have varied requirements from month to month. In response, some ISPs are providing alternative avenues for users to customize data plans to their needs. In this paper, we examine a secondary data market, as for example created by China Mobile Hong Kong, in which users can buy and sell leftover data caps from one another. While similar to an auction in that users submit bids to buy and sell data, it differs from traditional double auctions in that the ISP serves as the middleman between buyers and sellers. Such a market faces two questions. First, can users learn each others' trading behavior well enough for the market to function, and second, do ISPs have a financial incentive to offer such a market? Different users' abilities to trade data depend on others, thus forcing users to not only optimize the amounts of data they bid, but also to learn and adjust for other users' trading behavior. We derive users' optimal behavior and propose an algorithm for ISPs to match buyers and sellers. We compare the optimal matchings for different ISP objectives and derive conditions under which the secondary market increases ISP revenue: while the ISP loses revenue from overage fees, it can assess administration fees and profit from the differences between the buyer and seller prices. Finally, we use one year of usage data from 100 U.S. mobile users to simulate the market dynamics and to illustrate that sustainable conditions for a revenue increase for the ISP can hold in practice.

This paper studies the joint beamforming and power control in a multiuser multi-input single-output network by utilizing the only statistical channel distribution information. Such information consists of slowly varying covariance matrices in the beamforming network that can be employed to reduce instantaneous feedback overhead in transmission. Utilizing solely the statistical channel information, we study how to minimize the maximum outage probability under a weighted sum power constraint that guarantees max-min fairness to all users. This problem is, however, generally hard to solve due to the nonconvexity and nonlinear coupling between beamformer and power variables. First, assuming a fixed beamformer set, we use the nonlinear Perron-Frobenius theory to design a decentralized algorithm with provable geometrically fast convergence rate to compute the optimal power. Then, for the general case, we examine a certainty-equivalent margin counterpart with outage-mapped thresholds that incorporate the statistical channel information. We show that a network duality for this certainty-equivalent problem can be useful to decouple the coupling between the beamformer and power variables. This nonlinear Perron-Frobenius theory motivated approach yields a feasible beamformer and power allocation that are near-optimal as compared to Monte Carlo averaging simulations.

In this paper, we study network utility maximization over both routing choice and path rate assignment for any given path cardinality constraint. We provide a novel convex relaxation, which leads to a randomized algorithm with performance guarantees. The new relaxation also enables distributed algorithm design and allows us to obtain performance estimation for nonconvex routing optimization problems that is significantly better than previous work based on the multipath routing relaxation. Convergence and performance of the proposed randomized algorithm are characterized theoretically and further illustrated numerically through examples to demonstrate its superiority over existing work.

Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult for them to maintain consistent quality-of-service. Some CSPs try to stabilize user demands by offering sustained-use discounts to jobs that consume more instance-hours per month. These discounts present an opportunity for users to pool their usage together into a single ``job.'' In this paper, we examine the viability of a middleman, the cloud virtual service provider (CVSP), that rents cloud resources from a CSP and then resells them to users. We show that the CVSP's business model is only viable if the average job runtimes and thresholds for sustained-use discounts are sufficiently small; otherwise, the CVSP cannot simultaneously maintain low job waiting times while qualifying for a sustained-use discount. We quantify these viability conditions by modeling the CVSP's job scheduling and then use this model to derive users' utility-maximizing demands and the CVSP's profit-maximizing price, as well as the optimal number of instances that the CVSP should rent from the CSP. We verify our results on a one-month trace from Google's production compute cluster, through which we first validate our assumptions on the job arrival and runtime distributions, and then show that the CVSP is viable under these workload traces. Indeed, the CVSP can earn a positive profit without significantly impacting the CSP's revenue, indicating that the CSP and CVSP can coexist in the cloud market.

This paper presents a systematic approach for solving wireless max-min utility fairness optimization problems in multiuser wireless networks with general monotonic constraints. These problems are often challenging to solve due to their nonconvexity. By establishing a connection between this class of optimization problems and the class of conditional eigenvalue problems that can be addressed by a generalized nonlinear Perron-Frobenius theory, we show how these problems can be solved optimally using an iterative algorithm that converges geometrically fast. The mathematical development in this paper unifies previous work and allows us to handle a broader class of competitive utility functions with general nonlinear monotonic constraints. Several representative applications illustrate the effectiveness of the proposed framework, including the max-min quality-of-service subject to robust interference temperature constraints in cognitive radio networks, the min-max weighted mean-square error subject to signal-to-interference-and-noise ratio constraints in multiuser downlink systems, the max-min throughput subject to nonlinear power constraints in energyefficient wireless networks, the max-min sigmoid utility in multimedia wireless networks, and the min-max outage probability subject to outage constraints in heterogeneous wireless networks. Numerical results are presented to demonstrate the fast-convergence behavior of the algorithms to the optimal fixed-point solution characterized by our generalized nonlinear Perron-Frobenius theoretic framework.

The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.

Due to the rapid growth of real-time applications and the ubiquity of IEEE 802.11 MAC as a layer-2 protocol for wireless local area networks (WLANs), it becomes increasingly important to support delay-based quality of service (QoS) in such WLANs. In this paper, we develop a simple and accurate enough analytical model for predicting the queueing delay of real-time multimedia traffic flows in non-homogeneous random access based WLANs. This leads to tractable analysis for meeting queueing delay specifications of a number of flows. In particular, we address the feasibility problem of whether the mean delays required by a set of User Datagram Protocol (UDP) flows supporting real-time multimedia traffic can be guaranteed in WLANs. Based on the model and feasibility analysis, we further develop an optimization technique to minimize the delays for the traffic flows. Moreover, we present a decentralized algorithm and report its implementation and present extensive simulation and experimental trace-based results to demonstrate the accuracy of our model and the performance of the algorithms.

Heterogeneous wireless networks provide varying degrees of network coverage in a multi-tier configuration in which low-powered small cells are used to enhance performance. Due to the ad-hoc deployment of small cells, optimal resource allocation is important to provision fairness and enhance energy efficiency. We first study the worst outage probability problem in Rayleigh-fading channels, and solve this nonconvex stochastic program using mathematical tools from nonlinear Perron-Frobenius theory. As a by-product, we solve an open problem of convergence for a previously proposed algorithm in the interference-limited case. We then address a total power minimization problem with outage specification constraints and its feasibility issue. We propose a dynamic algorithm that adapts the outage probability specification in a heterogeneous wireless network to minimize the total energy consumption and to simultaneously provide fairness guarantees in terms of the worst outage probability. Finally, we provide numerical evaluation on the performance of the algorithms and the effectiveness of deploying closed-access small cells in heterogeneous wireless networks to address the tradeoff between energy saving and feasibility of users satisfying their outage probability specifications.

The growing volume of mobile data traffic has led many Internet service providers (ISPs) to cap their users' monthly data usage, with overage fees for exceeding their caps. In this work, we examine a secondary data market in which users can buy and sell leftover data caps from each other. China Mobile Hong Kong recently introduced such a market. While similar to an auction in that users submit bids to buy and sell data, it differs from traditional double auctions in that the ISP serves as the middleman between buyers and sellers. We derive the optimal prices and amount of data that different buyers and sellers are willing to bid in this market and then propose an algorithm for ISPs to match buyers and sellers. We compare the optimal matching for different ISP objectives and derive conditions under which an ISP can obtain higher revenue with the secondary market: while the ISP loses revenue from overage fees, it can assess administration fees and take the differences between the buyer and seller prices. Finally, we use one year of usage data from 100 U.S. mobile users to illustrate that the conditions for a revenue increase can hold in practice.

Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.

In this paper, we investigate the joint design of transmit beamforming and power control to maximize the weighted sum rate in the multiple-input single-output (MISO) cognitive radio network constrained by arbitrary power budgets and interference temperatures. The nonnegativity of the physical quantities, e.g., channel parameters, powers, and rates, is exploited to enable key tools in nonnegative matrix theory, such as the (linear and nonlinear) Perron-Frobenius theory, quasi-invertibility, and Friedland-Karlin inequalities, to tackle this nonconvex problem. Under certain (quasi-invertibility) sufficient conditions, we propose a tight convex relaxation technique that relaxes multiple constraints to bound the global optimal value in a systematic way. Then, a single-input multiple-output (SIMO)-MISO duality is established through a virtual dual SIMO network and Lagrange duality. This SIMO-MISO duality proved to have the zero duality gap that connects the optimality conditions of the primal MISO network and the virtual dual SIMO network. Moreover, by exploiting the SIMO-MISO duality, an algorithm is developed to optimally solve the sum rate maximization problem. Numerical examples demonstrate the computational efficiency of our algorithm, when the number of transmit antennas is large.

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links, and particularly reveal a new tradeoff between the network energy efficiency and fairness by comparing the MEP with the existing algorithms.

This paper presents a unifying and systematic framework to solve wireless max-min utility fairness optimization problems in multiuser wireless networks with generalized monotonic constraints. These problems are often challenging to solve due to their nonlinearity and non-convexity. Our framework leverages a general result in nonlinear Perron-Frobenius theory to characterize the global optimal solution of these problems analytically, and to design scalable and fast-convergent algorithms for the computation of the optimal solution. This work advances the state-of-the-art in handling wireless utility optimization problems with nonlinear monotonic constraints, which existing methodologies cannot handle, and also unifies previous works in this area. Several representative applications are considered to illustrate the effectiveness of the proposed framework, including max-min quality of service subject to robust interference temperature constraints in cognitive radio networks, min-max outage subject to outage constraints in heterogeneous networks, and min-max weighted MSE subject to SINR constraints in multiuser downlink system.

Proving an information inequality is a crucial step in establishing the converse results in coding theorems. However, an information inequality involving many random variables is difficult to be proved manually. In [1], Yeung developed a framework that uses linear programming for verifying linear information inequalities. Under this framework, this paper considers a few other problems that can be solved by using Lagrange duality and convex approximation. We will demonstrate how linear programming can be used to find an analytic proof of an information inequality. The way to find a shortest proof is explored. When a given information inequality cannot be proved, the sufficient conditions for a counterexample to disprove the information inequality are found by linear programming.

Increasingly, more spectrum bands are utilized for unlicensed use in wireless cognitive networks. It is important to study how information-theoretic secrecy capacity is affected in large-scale cognitive networks. We consider two scenarios: (1) non-colluding case, where eavesdroppers decode messages individually. In this case, we propose a new secure protocol model to analyze the transmission opportunities of secondary nodes. We show that the secrecy capacity of the primary network is not affected, while the secondary network can achieve the same performance as a standalone network in the order sense. Since our analysis is general as we only make a few relaxed assumptions on both networks, the conclusions hold when both networks are classic static networks, networks with i.i.d mobility, multicast networks etc. (2) colluding case where eavesdroppers can collude to decode a message. In that case, we show that the lower bound of per-node secrecy capacity of the primary network is Ω(1/√n φe-2/α-1(n)) when the eavesdropper density is φe(n)=Ω(log2n). Interestingly the existence of secondary nodes increases the secrecy capacity of the primary network.

This paper addresses the problem of a single rumor source detection with multiple observations, from a statistical point of view of a spreading over a network, based on the susceptible-infectious model. For tree networks, multiple sequential observations for one single instance of rumor spreading cannot improve over the initial snapshot observation. The situation dramatically improves for multiple independent observations. We propose a unified inference framework based on the union rumor centrality, and provide explicit detection performance for degree-regular tree networks. Surprisingly, even with merely two observations, the detection probability at least doubles that of a single observation, and further approaches one, i.e., reliable detection, with increasing degree. This indicates that a richer diversity enhances detectability. For general graphs, a detection algorithm using a breadth-first search strategy is also proposed and evaluated. Besides rumor source detection, our results can be used in network forensics to combat recurring epidemic-like information spreading such as online anomaly and fraudulent email spams.

A fundamental challenge of a smart grid is: to what extent can moving energy through space and time be optimized to benefit the power network with large-scale energy storage integration? With energy storage, there is a possibility to generate more energy when the demand is low and store it for later use. In this paper, we study a dynamic optimal power flow (OPF) problem with energy storage dynamics in purely resistive power networks. By exploiting the recently discovered zero duality gap property in the OPF problem, we apply optimization decomposition techniques to decouple the coupling energy storage constraints and obtain the global optimal solution using distributed message passing algorithms. The decomposition methods offer new interesting insights on the equilibrium load profile smoothing feature over space and time through the relationship between the optimal dual solution in the OPF and the energy storage dynamics. We evaluate the performance of the distributed algorithms in several IEEE benchmark systems and show that they converge fast to the global optimal solution by numerical simulations.

We study the feasibility of the total power minimization problem subject to power budget and Signal-to-Interference-plus-Noise Ratio (SINR) constraints in cognitive radio networks. As both the primary and the secondary users are allowed to transmit simultaneously on a shared spectrum, uncontrolled access of secondary users degrades the performance of primary users and can even lead to system infeasibility. To find the largest feasible set of secondary users (i.e., the system capacity) that can be supported in the network, we formulate a vector-cardinality optimization problem. This nonconvex problem is however hard to solve, and we propose a convex relaxation heuristic based on the sum-of-infeasibilities in optimization theory. Our methodology leads to the notion of admission price for spectrum access that can characterize the tradeoff between the total energy consumption and the system capacity. Price-driven algorithms for joint power and admission control are then proposed that quantify the benefits of energy-infeasibility balance. Numerical results are presented to show that our algorithms are theoretically sound and practically implementable.

A key challenge in wireless cognitive radio networks is to maximize the total throughput also known as the sum rates of all the users while avoiding the interference of unlicensed band secondary users from overwhelming the licensed band primary users. We study the weighted sum rate maximization problem with both power budget and interference temperature constraints in a cognitive radio network. This problem is nonconvex and generally hard to solve. We propose a reformulation-relaxation technique that leverages nonnegative matrix theory to first obtain a relaxed problem with nonnegative matrix spectral radius constraints. A useful upper bound on the sum rates is then obtained by solving a convex optimization problem over a closed bounded convex set. It also enables the sum-rate optimality to be quantified analytically through the spectrum of specially-crafted nonnegative matrices. Furthermore, we obtain polynomial-time verifiable sufficient conditions that can identify polynomial-time solvable problem instances, which can be solved by a fixed-point algorithm. As a by-product, an interesting optimality equivalence between the nonconvex sum rate problem and the convex max-min rate problem is established. In the general case, we propose a global optimization algorithm by utilizing our convex relaxation and branch-and-bound to compute an ε-optimal solution. Our technique exploits the nonnegativity of the physical quantities, e.g., channel parameters, powers and rates, that enables key tools in nonnegative matrix theory such as the (linear and nonlinear) Perron-Frobenius theorem, quasi-invertibility, Friedland-Karlin inequalities to be employed naturally. Numerical results are presented to show that our proposed algorithms are theoretically sound and have relatively fast convergence time even for large-scale problems

We study the network utility maximization problems in wireless networks for service differentiation that optimize the Signal-to-Interference-plus-Noise Radio (SINR) and reliability under Rayleigh fading. Though seemingly nonconvex, we show that these problems can be decomposed into an optimization framework where each user calculates a payment for a given resource allocation, and the network uses the payment to optimize the performance of the user. We study three important examples of this utility maximization, namely the weighted sum logarithmic SINR maximization, the weighted sum inverse SINR minimization and the weighted sum logarithmic reliability maximization. These problems have hitherto been solved suboptimally in the literature. By exploiting the positivity, quasi-concavity and homogeneity properties in these problems and using the nonlinear Perron-Frobenius theory, we propose fixed-point algorithms that converge geometrically fast to the globally optimal solution. Numerical evaluations show that our algorithms are stable (free of parameter configuration) and computationally fast.

This paper studies the joint beamforming and power control in a multiuser distributed antenna uplink network, wherein the number of users and the number of separately located antennas grow large with ratio being bounded. We consider the SINR fairness problem under individual power constraint and present a distributed iterative algorithm. This algorithm, though converging to the instantaneous optimal solution, requires instantaneous power update. In order to design a low complexity algorithm that achieves optimality in the asymptotic sense, we leverage the large system structure to derive an asymptotic solution requiring only channel statistics. In this algorithm, the asymptotic power is slowly updated and the asymptotic beamformer can be obtained in a non-iterative manner. The convergence property of the proposed algorithm is also studied.

Suppose that a rumor originating from a single source among a set of suspects spreads in a network, how to root out this rumor source? With the a priori knowledge of suspect nodes and a snapshot observation of infected nodes, we construct a maximum a posteriori (MAP) estimator to identify the rumor source using the susceptible-infected (SI) model. We propose to use a notion of local rumor center to characterize Pc(n), the correct detection probability of the source estimator upon observing n infected nodes, in both the finite and asymptotic regimes, for regular trees of node degree δ. First, when all nodes are suspects, limn→∞Pc(n) grows from 0.25 to 0.307 as δ increases from three to infinity, a result first established in Shah and Zaman (2011, 2012) via a different approach; furthermore, Pc(n) monotonically decreases with n and increases with δ even in the finite-n regime. Second, when the suspect nodes form a connected subgraph of the network, limn→∞Pc(n) significantly exceeds the a priori probability if δ ≥ 3, and reliable detection is achieved as δ becomes sufficiently large; furthermore, Pc(n) monotonically decreases with n and increases with δ. Third, when there are only two suspect nodes, limn→∞Pc(n) is at least 0.75 if δ ≥ 3; and Pc(n) increases with the distance between the two suspects. Fourth, when there are multiple suspect nodes, among all possible connection patterns, that all the suspects form a single connected subgraph yields the smallest Pc(n). Our analysis leverages ideas from the Pólya's urn model in probability theory and sheds insight into the behavior of the rumor spreading process not only in the asymptotic regime but also for the general finite-n regime.

Epidemic evolution is the spread of a computer or biological virus over a network. The goal is to control the speed of the epidemic evolution with limited network control resources and to study how users in the network can be infected. The epidemic evolution can be modeled by a probabilistic dynamical system over a connected graph. We consider several epidemic evolution models in the literature, and formulate their evolution control under a common framework that requires solving a non convex optimization problem with an objective that is the spectral radius function of a nonnegative matrix. We propose two algorithms to tackle this optimization problem. The first one is a suboptimal but computation ally fast algorithm based on successive convex relaxation, while the second one can compute a global optimal solution using branch-and-bound techniques that leverage some key inequalities in non negative matrix theory.

We study a utility maximization framework for spectrum sharing among cognitive secondary users and licensed primary users in cognitive radio networks. All the users maximize the network utility by adapting their signal-to-interference-plus-noise ratio (SINR) assignment and transmit power subject to power budget constraints and additional interference temperature constraint for the secondary users. The utility maximization problem is challenging to solve optimally in a distributed manner due to the nonconvexity and the tight coupling between the power budget and interference temperature constraint sets. We first study a special case where egalitarian SINR fairness is the utility, and a tuning-free distributed algorithm with a geometric convergence rate is developed to solve it optimally. Then, we answer the general utility maximization question by developing a cognitive radio network duality to decouple the SINR assignment, the transmit power and the interference temperature allocation. This leads to a utility maximization algorithm that leverages the egalitarian fairness power control as a submodule to maintain a desirable separability in the SINR assignment between the secondary and primary users. This algorithm has the advantage that it can be distributively implemented, and the method converges relatively fast. Numerical results are presented to show that our proposed algorithms are theoretically sound and practically implementable.

In this paper, we consider a wireless network where interference is treated as noise, and we study the nonconvex problem of sum rate maximization by power control. We focus on finding approximately optimal solutions that can be efficiently computed to this NP-hard problem by studying the solutions to two related problems, the sum rate maximization using a signal-to-interference-plus-noise ratio (SINR) approximation and the max-min weightedSINRoptimization. We show that these two problems are intimately connected, can be solved efficiently by algorithms with fast convergence and minimal parameter configuration, and can yield high-quality approximately optimal solutions to sum rate maximization in the low interference regime. As an application of these results, we analyze the connection-level stability of cross-layer utility maximization in the wireless network, where users arrive and depart randomly and are subject to congestion control, and the queue service rates at all the links are determined by the sum rate maximization problem. In particular, we determine the stability region when all the links solve the max-min weightedSINRproblem, using instantaneous queue sizes as weights.

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

This paper studies joint beamforming and power control in a coordinated multicell downlink system that serves multiple users per cell to maximize the minimum weighted signal-to-interference-plus-noise ratio. The optimal solution and distributed algorithm with geometrically fast convergence rate are derived by employing the nonlinear Perron-Frobenius theory and the multicell network duality. The iterative algorithm, though operating in a distributed manner, still requires instantaneous power update within the coordinated cluster through the backhaul. The backhaul information exchange and message passing may become prohibitive with increasing number of transmit antennas and increasing number of users. In order to derive asymptotically optimal solution, random matrix theory is leveraged to design a distributed algorithm that only requires statistical information. The advantage of our approach is that there is no instantaneous power update through backhaul. Moreover, by using nonlinear Perron-Frobenius theory and random matrix theory, an effective primal network and an effective dual network are proposed to characterize and interpret the asymptotic solution.

This paper studies joint beamforming and power control in a multiuser MISO interference network with statistical channel information. Such information consists of the slow-varying covariance matrices in the beamforming network, and can be employed to reduce instantaneous feedback needs. With the outage event induced by the utilization of statistical channel information, we optimize signal transmission strategies to minimize the maximum outage probability under weighted sum power constraint to achieve outage balancing in the interference network. Under the condition of fixed beamformer, we use nonlinear Perron-Frobenius theory to present a decentralized algorithm with provable geometrically fast convergence rate to compute the optimal power. Since the joint beamformer and power optimization problem is non-convex, we examine its certainty-equivalent margin counterpart. By leveraging nonlinear Perron-Frobenius theory and the established network duality, we present a near-optimal decentralized algorithm to jointly optimize the beamformer and power. The algorithm converges quickly and the convergence rate of the algorithm is proven to be geometrical.

This paper studies efficient signal transmission strategies in a multiuser MIMO network where multiple source nodes form a large virtual MIMO array to perform cooperative beamforming. In order to reduce instantaneous feedback needs, we investigate joint power control and cooperative beamforming techniques to maximize the minimum weighted average SINR based on statistical channel information, which consists of the covariance matrices in the beamforming network. Since the joint optimization problem over power, transmit beamformer and receive beamformer is non-convex, we analyze two sub-problems: the joint optimization of power and transmit beamformer under fixed receive beamformer, and the joint optimization of power and receive beamformer under fixed transmit beamformer. For both sub-problems, we use nonlinear Perron-Frobenius theory to characterize the optimal solution and present decentralized algorithms with provable geometrically fast convergence rate. By combining the developed techniques and the network duality, we provide an effective iterative algorithm for the joint optimization problem.

Rate adaptation and power control are two key resource allocation mechanisms in multiuser wireless networks. In the presence of interference, how do we jointly optimize end-to-end source rates and link powers to achieve weighted max-min rate fairness for all sources in the network? This optimization problem is hard to solve as physical layer link rate functions are nonlinear, nonconvex, and coupled in the transmit powers. We show that the weighted max-min rate fairness problem can, in fact, be decoupled into separate fairness problems for flow rate and power control. For a large class of physical layer link rate functions, we characterize the optimal solution analytically by a nonlinear Perron-Frobenius theory (through solving a conditional eigenvalue problem) that captures the interaction of multiuser interference. We give an iterative algorithm to compute the optimal flow rate that converges geometrically fast without any parameter configuration. Numerical results show that our iterative algorithm is computationally fast for both the Shannon capacity, CDMA, and piecewise linear link rate functions.

In this paper, we investigate the maximum achievable sum-rate of the two-user Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that capture the use of Gaussian coding and successive decoding. In the deterministic channel problem, we find the constrained sum-capacity and its achievable schemes with the minimum number of messages, first in symmetric channels, and then in general asymmetric channels. We show that the constrained sum-capacity oscillates as a function of the cross link gain parameters between the information theoretic sum-capacity and the sum-capacity with interference treated as noise. Furthermore, we show that if the number of messages of either of the two users is fewer than the minimum number required to achieve the constrained sum-capacity, the maximum achievable sum-rate drops to that with interference treated as noise. We provide two algorithms to translate the optimal schemes in the deterministic channel model to the Gaussian channel model. We also derive two upper bounds on the maximum achievable sum-rate of the Gaussian Han-Kobayashi schemes, which automatically upper bound the maximum achievable sum-rate using successive decoding of Gaussian codewords. Numerical evaluations show that, similar to the deterministic channel results, the maximum achievable sum-rate with successive decoding in the Gaussian channels oscillates between that with Han-Kobayashi schemes and that with single message schemes.

The deployment of cognitive radio networks enables efficient spectrum sharing and opportunistic spectrum access. It also presents new challenges to the classical problem of interference management in wireless networks. This paper develops an axiomatic framework for power allocation in cognitive radio networks based on four goals: QoS protection to primary users, opportunism to secondary users, admissibility to secondary users, and autonomous operation by individual users. Two additional goals, licensing and versatility, which are desirable rather than essential, are also presented. A general class of Duo Priority Class Power Control (DPCPC) policies that satisfy such goals is introduced. Through theoretical analysis and simulation, it is shown that a specific interference-aware power-control algorithm reaches such goals.

Typical formulations of max-min weighted SIR problems involve either a total power constraint or individual power constraints. These formulations are unable to handle the complexities in multicell networks where each base station can be subject to its own sum power constraint. This paper considers the max-min weighted SIR problem subject to multiple weighted-sum power constraints, where the weights can represent relative power costs of serving different users. First, we derive the uplink-downlink duality principle by applying Lagrange duality to the single-constraint problem. Next, we apply nonlinear Perron-Frobenius theory to derive a closed-form solution for the multiple-constraint problem. Then, by exploiting the structure of the closed-form solution, we relate the multiple-constraint problem with its single-constraint subproblems and establish the dual uplink problem. Finally, we further apply nonlinear Perron-Frobenius theory to derive an algorithm which converges geometrically fast to the optimal solution.

Due to the rapid growth of real-time applications and the ubiquity of IEEE 802.11 MAC as a layer-2 protocol for wireless local area networks (WLANs), it is of increasing interest to support quality of service (QoS) in such WLANs. In this paper, we develop a simple but accurate enough analytical model for predicting queueing delay in non-homogeneous random access based WLANs. This leads to tractable solutions for meeting queueing delay specifications of a number of flows. Using this model, we address the feasibility problem of whether the mean delays required by a set of inelastic flows can be guaranteed in WLANs. Based on the model and feasibility analysis, we further develop an optimization technique to minimize the delays for inelastic flows. We present extensive simulation results to demonstrate the accuracy of our model and the performance of the algorithms.

Heterogeneous wireless networks employ varying degrees of network coverage using power control in a multi-tier configuration, where low-power femtocells are used to enhance performance, e.g., optimize outage probability. We study the worst outage probability problem under Rayleigh fading. As a by-product, we solve an open problem of convergence for a previously proposed algorithm in the interference-limited case. We then address a total power minimization problem with outage specification constraints and its feasibility condition. We propose a dynamic algorithm that adapts the outage probability specification in a heterogeneous network to minimize the total energy consumption and simultaneously guarantees all the femtocell users a min-max fairness in terms of the worst outage probability.

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

In this paper, we investigate the sum-capacity of the two-user Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that capture the use of Gaussian coding and successive decoding. In the deterministic channel problem, we show that the constrained sum-capacity oscillates as a function of the cross link gain parameters between the information theoretic sum-capacity and the sum-capacity with interference treated as noise. Furthermore, we show that if the number of messages of either user is fewer than the minimum number required to achieve the constrained sum-capacity, the maximum achievable sum-rate drops to that with interference treated as noise. We translate the optimal schemes in the deterministic channel model to the Gaussian channel model, and also derive two upper bounds on the constrained sum-capacity. Numerical evaluations show that the constrained sum-capacity in the Gaussian channels oscillates between the sum-capacity with Gaussian Han-Kobayashi schemes and that with single message schemes.

This paper studies the optimization of a multicell multiple-input-single-output (MISO) downlink system in which each base station serves multiple users, and each user is served by only one base station. First, we consider the problem of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) of all users subject to a single weighted-sum power constraint, where the weights can represent relative power costs of serving different users in each cell. We apply concave Perron-Frobenius theory to propose a joint power control and linear beamforming algorithm which converges geometrically fast to the optimal solution. As a by-product, we resolve an open problem of convergence of a previously proposed algorithm by Wiesel, Eldar, and Shamai in 2006. Next, we study the max-min weighted SINR problem subject to multiple weighted-sum power constraints and we show that it can be decoupled into its associated single-constrained subproblems.

Spectrum management is used to improve performance in multiuser communication system, e.g., cognitive radio or femtocell networks, where multiuser interference can lead to data rate degradation. We study the nonconvex NP-hard problem of maximizing a weighted sum rate in a multiuser Gaussian interference channel by power control subject to affine power constraints. By exploiting the fact that this problem can be restated as an optimization problem with constraints that are spectral radii of specially crafted nonnegative matrices, we derive necessary and sufficient optimality conditions and propose a global optimization algorithm based on the outer approximation method. Central to our techniques is the use of nonnegative matrix theory, e.g., nonnegative matrix inequalities and the Perron-Frobenius theorem. We also study an inner approximation method and a relaxation method that give insights to special cases. Our techniques and algorithm can be extended to a multiple carrier system model, e.g., OFDM system or receivers with interference suppression capability.

This paper studies the performance difference of joint routing and congestion control when either single-path routes or multipath routes are used. Our performance metric is the total utility achieved by jointly optimizing transmission rates using congestion control and paths using source routing. In general, this performance difference is strictly positive and hard to determine-in fact an NP-hard problem. To better estimate this performance gap, we develop analytical bounds to this “cost of not splitting” in routing. We prove that the number of paths needed for optimal multipath routing differs from that of optimal single-path routing by no more than the number of links in the network. We provide a general bound on the performance loss, which is independent of the number of source-destination pairs when the latter is larger than the number of links in a network. We also propose a vertex projection method and combine it with a greedy branch-and-bound algorithm to provide progressively tighter bounds on the performance loss. Numerical examples are used to show the effectiveness of our approximation technique and estimation algorithms.

Designing fast algorithms that adapt the transmit and receive power and beamformers to optimize performance for different users is important in wireless MIMO downlink systems. This paper studies the max-min weighted SIR problem in the downlink, where multiple users are weighted according to priority and are subject to a total power constraint. The difficulty of this nonconvex problem is compounded by the coupling in the transmit and receive beamformers, thereby making it hard to optimize in a distributed fashion. We first show that this problem can be optimally and efficiently computed using a fast algorithm when the channels are rank-one. The optimal transmit and receive power and beamformers are also derived analytically. We then exploit the MIMO uplink-downlink duality to adapt our algorithm to compute a local optimal solution for channels with general rank.

This paper investigates the network performance loss of using only single-path routing when multiple paths are available. The performance metric is the aggregate utility achieved by the joint optimization of congestion control and routing. As computing the exact loss for a general network topology is NP-hard, we develop analytical bounds on this "cost of not splitting". Our bound is independent of the number of source-destination pairs when the latter one is larger than the number of links in a network. We also propose a vertex projection method and combine it with branch-and-bound to provide progressively tighter bounds on the performance loss. Numerical examples are used to show the effectiveness of our approximation technique.

Sum rate maximization by power control is an important, challenging, and extensively studied problem in wireless networks. It is a nonconvex optimization problem and achieves a rate region that is in general nonconvex. We derive approximation ratios to the sum rate objective by studying the solutions to two related problems, sum rate maximization using an SIR approximation and max-min weighted SIR optimization. We also show that these two problems can be solved very efficiently, using much faster algorithms than the existing ones in the literature. Furthermore, using a new parameterization of the sum rate maximization problem, we obtain a characterization of the power controlled rate region and its convexity property in various asymptotic regimes. Engineering implications are discussed for IEEE 802.11 networks.

Maximizing the minimum weighted SIR, minimizing the weighted sum MSE and maximizing the weighted sum rate in a multiuser downlink system are three important performance objectives in joint transceiver and power optimization, where all the users have a total power constraint. We show that, through connections with the nonlinear Perron-Frobenius theory, jointly optimizing power and beamformers in the max-min weighted SIR problem can be solved optimally in a distributed fashion. Then, connecting these three performance objectives through the arithmetic-geometric mean inequality and nonnegative matrix theory, we solve the weighted sum MSE minimization and weighted sum rate maximization in the low to moderate interference regimes using fast algorithms.

In a MIMO multiple-access channel where users employ Space-Time Block Codes (STBC), interference cancellation can be used to suppress co-channel interference and recover the desired signal of each user at the receiver. Leveraging the special properties of Alamouti matrices, we first show that spatial multiplexing of Alamouti signals retains the space-time diversity gain of Alamouti signaling using our proposed low-complexity Alamouti BLAST-MMSE (A-BLAST) Algorithm. Next, in contrast to traditional transmit diversity that focuses on STBC construction at the transmitter, this paper looks at transmit diversity from the perspective of the receiver. In other words, the receiver gets to choose the STBCiquests, which are favourable to the channel assuming a fixed BLAST receive algorithm. In a multiuserMAC setting, we first present a systematic methodology to exploit different decomposition structure in Alamouti matrices, each with different tradeoff between performance and decoding complexity using possibly different MIMO receive algorithms. We then demonstrate that the notion of angles (the inner product of two quaternionic vectors) between multiuser channels determines the performance of MIMO receive algorithms. As an application of the general theory, we transform the decoding problem for several types of Quasi-Orthogonal STBC (QOSTBC) into multiuser detection of virtual Alamouti users. Building upon our A-BLAST Algorithm, we propose new algorithms for decoding single-user and multiuser QOSTBC. In particular, we show that bit error probability is a function of the quaternionic angle between virtual users (for a single user) or multiple users. This angle varies with the type of QOSTBC and leads to a new form of adaptive modulation called code diversity, where feedback instructs the transmitter how to choose from a plurality of codes.

The multi-carrier interference channel where interference is treated as additive white Gaussian noise, is a very active topic of research, particularly important in the area of Dynamic Spectrum Management (DSM) for Digital Subscriber Lines (DSL). Here, multiple users optimize their transmit power spectra so as to maximize the total weighted sum of data rates. The corresponding optimization problem is however nonconvex and thus computationally intractable, i.e. a certificate of global optimality requires exponential time complexity algorithms. This paper shows that under certain channel conditions, this nonconvex problem can be solved in polynomial time with a certificate of global optimality. The channel conditions are discussed consisting of different interference models including synchronous and asynchronous DSL transmission. Simulations demonstrate its applicability to realistic DSL scenarios.

This is an overview of the ongoing FAST Copper project, which is aimed at substantial improvements in rate, reach, reliability, and quality in copper-last-mile broadband access through fiber/DSL deployment, engineering innovations, and fundamental research. The project is funded by NSF, and is currently pursued jointly by Princeton University, Stanford University, and Fraser Research Lab. In this article, we outline the motivations, challenges, and research issues associated with the project, and report some of the recent results by the Princeton team in each of the four dimensions: frequency, amplitude, space, and time.

Most previous work in statistical multiplexing only considered the case where the link transmission rates are fixed. In this paper, we consider statistical multiplexing in networks with adaptive transmission rates, with focus on DSL broadband access networks. This requires a jointly optimized allocation of buffer space and transmission bandwidth to traffic flows, which takes the flow traffic characteristics, the user QoS requirements, and the user interactions at the physical layer into consideration. Using the effective bandwidth concept, we propose a class of alternate maximization (AM) algorithms (AM-D and AM-M), which solve the statistical multiplexing problem for both delay insensitive data traffic and delay sensitive multimedia traffic. With low complexity as a design goal, the AM algorithms incorporate our recently proposed autonomous spectrum balancing (ASB) algorithm, which was originally designed for DSL physical layer spectrum management. Our numerical results show that the AM algorithms combines the gain due to statistical multiplexing and that due to spectrum management.

A systematic approach to solve seemingly nonconvex resource allocation problems in wireless cellular networks is studied in this paper. By revealing and exploiting the hidden convexity in the problem formulations, we obtain solutions that can tackle a variety of objective functions, provide robustness to resource allocations such as power, and be obtained often through distributed algorithms. The advantages of such flexibility and robustness are demonstrated through comparisons with the state-of-the-art in recent research literature. First we show how to distributively solve a variety of resource allocation problems in CDMA and interference limited CDMA channels with quality of service constraints, such as meeting minimum queueing delay or energy per bit requirement. Then, for uplink transmission in a CDMA cellular network, we propose an optimal power control scheme with congestion-aware active link protection. In particular, the tradeoff between power expenditure and the protection margin of the SIR-balancing power algorithm is optimized.

Public-access networks need to handle persistent congestion and overload caused by high bandwidth aggregates that may occur during times of flooding-based DDoS attacks or flash crowds. The often unpredictable nature of these two activities can severely degrade server performance. Legitimate user requests also suffer considerably when traffic from many different sources aggregates inside the network and causes congestion. This paper studies a family of algorithms that "proactively" protect a server from overload by installing rate throttles in a set of upstream routers. Based on an optimal control setting, we propose algorithms that achieve throttling in a distributed and fair manner by taking important performance metrics into consideration, such as minimizing overall load variations. Using ns-2 simulations, we show that our proposed algorithms 1) are highly adaptive by avoiding unnecessary parameter configuration, 2) provide max-min fairness for any number of throttling routers, 3) respond very quickly to network changes, 4) are extremely robust against extrinsic factors beyond the system control, and 5) are stable under given delay bounds.

In wireless cellular or ad hoc networks where Quality of Service (QoS) is interference-limited, a variety of power control problems can be formulated as nonlinear optimization with a system-wide objective, e.g., maximizing the total system throughput or the worst user throughput, subject to QoS constraints from individual users, e.g., on data rate, delay, and outage probability. We show that in the high Signal-to- interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimization problems in the form of geometric programming; hence they can be very efficiently solved for global optimality even with a large number of users. In the medium to low SIR regime, some of these constrained nonlinear optimization of power control cannot be turned into tractable convex formulations, but a heuristic can be used to compute in most cases the optimal solution by solving a series of geometric programs through the approach of successive convex approximation. While efficient and robust algorithms have been extensively studied for centralized solutions of geometric programs, distributed algorithms have not been explored before. We present a systematic method of distributed algorithms for power control that is geometric-programming-based. These techniques for power control, together with their implications to admission control and pricing in wireless networks, are illustrated through several numerical examples.

In Network Utility Maximization (NUM) problems, it is generally assumed that user utilities are uncoupled, i.e., each utility depends only on local variables. Then the coupling in constraint functions among users sharing common resources can be decoupled by standard methods such as dual decomposition. However, in problems where cooperation or competition is modeled through the objective function, such as rate allocation in clustered system and power control in interference limited system, each utility may depend not only on its local variables but also on the local variables of other utilities. Applications of this coupled utility model include wireless power control and DSL spectrum management, where the utilities are functions of the Signal-to-Interference Ratios (SIR) that depend on the transmit powers of other users. We present a systematic approach of consistency pricing to decouple NUM problems with coupled utilities, obtaining distributed algorithms that efficiently handle couplings in utilities with two alternative timescales, as well as a method to reduce message passing overhead in the case of interference-based coupling.

We propose a Probabilistic Preemptive Burst Segmentation (PPBS) scheduling scheme to provide priority classes with different Quality-of-service (QoS) requirements in WDM Optical Burst Switching (OBS) networks. The PPBS scheme enables high priority bursts to preempt and segment low priority bursts in a probabilistic fashion. The PPBS scheme achieves 100% isolation among priority classes, and burst loss probabilities can be controlled by tunable parameters. Our PPBS queueing model also implicitly enables us to obtain several results related to the Erlang's loss function which may be useful in the role of this function arising in some areas of queueing theory, as well as characterizing a loss rate region that work conserving burst scheduling techniques can achieve. As an application to providing service differentiation, we show how the PPBS scheme can minimize the total sum of loss rates and achieve proportional loss differentiation. Finally, we also demonstrate the effectiveness of the PPBS scheme empirically using realistic Internet traffic model, e.g., long range dependent traffic model.

High-bandwidth traffic aggregates may occur during times of flooding-based distributed denial-of-service attacks or flash crowds. Congestion control of these traffic aggregates is important to avoid congestion collapse of network services. This paper presents a class of feedback-control algorithms that proactively protect a network server from overload by installing rate throttles in a set of upstream routers. A control-theoretical framework is proposed to optimize the control setting such that throttling can be achieved in a distributed and fair manner. We develop control-theoretic algorithms that (1) are highly adaptive by avoiding the configuration of unnecessary control parameters, (2) provide max-min fairness for any number of throttling routers, (3) respond very quickly to network changes, (4) are extremely robust against extrinsic factors beyond the system control, and (5) are stable under given delay bounds.

In wireless cellular networks that are interference-limited, a variety of power control problems can be formulated as nonlinear optimization with a system-wide objective subject to many QoS constraints from individual users. Previous work have been done in the high SIR regime by solving these problems with nonlinear objectives and constraints as geometric programs. However, in the medium to low SIR regime, these problems cannot be transformed into tractable convex optimization problems. This paper makes two contributions: (1) In the low SIR regime, we propose a method with centralized computation to obtain the globally optimal solution by solving a series of geometric programs. (2) While efficient and robust algorithms have been extensively studied for centralized solutions of geometric programs, distributed algorithms have not been investigated before this paper. We present a systematic method of distributed algorithms for power control based on geometric programs in high SIR regime. These two contributions can be readily combined to distributively solve nonlinear power control problems in general SIR regime

We propose a probabilistic preemptive burst segmentation (PPBS) scheme to provide traffic classes with different quality-of-service (QoS) requirements in optical burst switching (OBS) networks. PPBS enables high priority bursts to preempt and segment low priority bursts in a probabilistic fashion. By tuning a preemptive parameter that can be instrumented locally on an OBS node, our scheme provides a flexible method to achieve service differentiation on an OBS switch for multiple prioritized traffic classes. We develop an analytical queueing model that captures the notion of increased bandwidth and parallel service using multiple wavelengths. It achieves 100% isolation between high and low priority classes and low loss probabilities in the single and multiple wavelength case. Specifically, our queueing model can achieve a proportional differentiated service (PDS) in terms of loss. Finally, we also compare the performance of PPBS with previous heuristic methods in achieving proportional loss differentiation using long range dependent traffic models.