This article investigates the optimal tracking control problem for high-order uncertain nonlinear systems by developing a simplified reinforcement learning (RL) framework with minimal neural networks (NNs). In contrast to conventional RL-based schemes that rely on recursive backstepping and require $3n$ NNs (where $n$ is the system order), the proposed method leverages high-order fully actuated (HOFA) system theory to reformulate the dynamics into a compact normal form. This enables a unified, nonrecursive controller design that requires only three NNs regardless of the system order, thereby significantly reducing computational complexity and facilitating practical implementation. Furthermore, this work overcomes a critical theoretical deficiency in existing simplified RL strategies, where the vanishing minimum eigenvalue of the NN basis function correlation matrix often leads to invalid Lyapunov stability analysis. A novel critic-actor weight update law is designed to bypass this problematic matrix, rigorously guaranteeing the semiglobal uniform ultimate boundedness of the closed-loop system without requiring persistent excitation (PE) conditions. Simulation results on a representative example demonstrate the effectiveness and computational efficiency of the proposed approach compared with existing methods.

Cooperative Perception (CP) has been a promising paradigm to enhance single-vehicle awareness by enabling perception sharing among connected vehicles. However, existing studies often overlook the impact of constrained and heterogeneous edge resources, leading to synchronization bottlenecks and limited deployment efficiency. To address these challenges, this paper proposes EI-Cooper, an Edge Intelligence (EI)-enhanced cooperative framework for adaptive and efficient CP in heterogeneous vehicular networks. The novelty of EI-Cooper is fourfold. First, we leverage key EI techniques including selective cooperation, model pruning and bandwidth allocation to jointly coordinate the perception, computation, communication within the CP pipeline. To the best of our knowledge, EI-Cooper represents the first attempt to extend CP with EI capabilities. Secondly, we formulate aSynchronization-EfficientCooperativePerception (SECP) problem, which jointly determines edge selection, pruning ratios and bandwidths to balance end-to-end synchronization efficiency and perception accuracy. Thirdly, to tackle the closed-box nature and computational NP-hardness of SECP, we decompose it into two interpretable subproblems, respectively capturing macro-level spatial completeness and micro-level semantic retention. Finally, we develop aTwo-StageHierarchicalOptimization (TSHO) algorithm, where the first stage maximizes coverage via submodular node selection with a$(1-1/e)$approximation, and the second stage performs alternating optimization of pruning and bandwidth allocation under convergence guarantees. Extensive experiments on public datasets and a real-world prototype demonstrate the superiority of EI-Cooper.

In mobile edge computing (MEC) networks, mobile users generate diverse machine learning tasks dynamically over time. These tasks are typically offloaded to the nearest available edge server, by considering communication and computational efficiency. However, its operation does not ensure that each server specializes in a specific type of tasks and leads to severe overfitting or catastrophic forgetting of previous tasks. To improve the continual learning (CL) performance of online tasks, we are the first to introduce mixture-of-experts (MoE) theory in MEC networks and save MEC operation from the increasing generalization error over time. Our MoE theory treats each MEC server as an expert and dynamically adapts to changes in server availability by considering data transfer and computation time. Unlike existing MoE models designed for offline tasks, ours is designed to handle continuous streams of tasks in the MEC environment. We introduce an adaptive gating network in MEC to adaptively identify and route newly arrived tasks of unknown data distributions to available experts, enabling each expert to specialize in a specific type of task upon convergence. We first consider the MoE model with the simple but fundamental top-1 routing strategy to derive the minimum number of experts required to match each task with a specialized, available expert. Our MoE approach consistently reduces the overall generalization error over time, unlike the traditional MEC approach. Interestingly, when the number of experts is sufficient to ensure convergence, adding more experts delays the convergence time and worsens the generalization error. Furthermore, we extend our theoretical analysis to the more general MoE model with top-$k$routing to examine the effect of the number of selected experts$k$for each task. Our results demonstrate that, while top-$k$routing accelerates system convergence compared to the top-1 strategy, it may increase the overall generalization error due to the heavier workload on each expert. Finally, we perform extensive experiments on real datasets in deep neural networks (DNNs) to verify our theoretical results.

Mobile crowdsourcing platforms leverage a mass of mobile users and their vehicles to learn massive point-of-interest (PoI) information while traveling and share it as a public good. Given that the crowdsourced users mind their travel costs and have diverse usage preferences for PoI information along different paths, we formulate the problem as a novel non-atomic multi-path routing game. This game features positive network externalities from social information sharing, distinguishing it from the traditional routing game literature that primarily focuses on congestion control of negative externalities among users. Our price of anarchy (PoA) analysis shows that in the absence of any incentive design, users’ selfish routing on the lowest-cost path will significantly limit PoI diversity leading to an arbitrarily large efficiency loss from the social optimum with a PoA of 0. This motivates us to design effective incentive mechanisms to remedy while upholding desirable properties including individual rationality (IR), incentive compatibility (IC), and budget balance (BB) to ensure practical feasibility. Without knowing a specific user’s path preference, we first present a non-monetary mechanism called Adaptive Information Restriction (AIR) that satisfies all the desirable properties. Our AIR indirectly penalizes non-cooperative users by adaptively reducing their access to the public good, according to actual user flows along different paths. It achieves a significant PoA of 1/4 with low complexityO(klogk+ logm), wherekandmrepresent the numbers of paths and user preference types, respectively. When the system can support pricing/billing for users, we further propose a new monetary mechanism called Adaptive Side-Payment (ASP), which adaptively charges and rewards users based on their chosen paths. Our ASP achieves a better PoA of 1/2 with an even lower complexity ofO(klogk). Finally, our theoretical findings are well corroborated by our experimental results using a real-world dataset.

In the paradigm of decentralized learning, a group of agents collaborate to learn a global model using a distributed dataset without a central server; nevertheless, it is severely challenged by the heterogeneity of the data distribution across the agents. For example, the data may be distributed non-independently and identically, and even be noised or poisoned. To address these data challenges, we propose ROSS, a novel robust decentralized stochastic learning algorithm based on Shapley values, in this paper. Specifically, in each round, each agent leverages its local gradient and the cross-gradients from its neighbors (i.e., the derivative of its local loss function and the derivatives of its neighbors’ local loss functions evaluated at its local model) to update its local model in a momentum-like manner, while we innovate in weighting the derivatives according to their contributions measured by Shapley values. We perform solid theoretical analysis to reveal the linear convergence speedup of our ROSS algorithm. We also verify the efficacy of our algorithm through extensive experiments on public datasets. Our results demonstrate that, in face of the above variety of data challenges, our ROSS algorithm have oblivious advantages over existing state-of-the-art proposals in terms of both convergence and prediction accuracy.

Content caching at base stations (BS) can reduce backhaul traffic delays to deliver the requested files to users, but its effectiveness is limited by BS storage capacity. We propose a novel approach that integrates artificial intelligence-generated content (AIGC) into the BS operations. Instead of caching entire files, our AIGC-enhanced BS can cache smaller prompts, allowing files to be reconstructed on demand. We explore the challenge of jointly optimizing hybrid caching, AIGC computation, and communication resource allocation with the goal of minimizing average system latency. Given the non-convex nature and the complexity of mixed integer non-linear programming involved, we propose a divide-and-conquer algorithm that breaks down the problem into two timescale levels. Theoretical analysis and simulations confirms that our AIGC-enhanced hybrid content caching outperforms the conventional content caching.

Continual learning (CL) is a new online learning technique over sequentially generated streaming data from different tasks, aiming to maintain a small forgetting loss on previously-learned tasks. Existing work focuses on reducing the forgetting loss under a given task sequence. However, if similar tasks continuously appear to the end time, the forgetting loss is still huge on prior distinct tasks. In practical IoT networks, an autonomous vehicle to sample data and learn different tasks can route and alter the order of task pattern at increased travelling cost. To our best knowledge, we are the first to study how to opportunistically route the testing object and alter the task sequence in CL. We formulate a new optimization problem and prove it NP-hard. We propose a polynomial-time algorithm to achieve approximation ratios of $\frac{3}{2}$ for underparameterized case and $\frac{3}{2} + {r^{1 - T}}$ for overparameterized case, respectively. Simulation results verify our algorithm’s close-to-optimum performance.

Reinforcement learning from human feedback (RLHF) has become an essential step in fine-tuning large language models (LLMs) to align them with human preferences. However, human labelers are selfish and have diverse preferences. They may strategically misreport their online feedback to influence the system’s aggregation towards their own preferences. Current practice simply averages labelers’ feedback per time and fails to identify the most accurate human labeler, leading to linear regret ${\mathcal{O}}(T)$ for T time slots. To our best knowledge, we are the first to study online learning mechanisms against strategic human labelers in the LLM fine-tuning process. We formulate a new dynamic Bayesian game and dynamically adjust human labelers’ weights in the preference aggregation, ensuring their truthful feedback and sublinear regret ${\mathcal{O}}\left({{T^{1/2}}}\right)$. Simulation results demonstrate our mechanism’s great advantages over the existing benchmark schemes.

Continual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a major issue in CL, as the model adapts to new tasks. The Mixture-of-Experts (MoE) model has recently been shown to effectively mitigate catastrophic forgetting in CL, by employing a gating network to sparsify and distribute diverse tasks among multiple experts. However, there is a lack of theoretical analysis of MoE and its impact on the learning performance in CL. This paper provides the first theoretical results to characterize the impact of MoE in CL via the lens of overparameterized linear regression tasks. We establish the benefit of MoE over a single expert by proving that the MoE model can diversify its experts to specialize in different tasks, while its router learns to select the right expert for each task and balance the loads across all experts. Our study further suggests an intriguing fact that the MoE in CL needs to terminate the update of the gating network after sufficient training rounds to attain system convergence, which is not needed in the existing MoE studies that do not consider the continual task arrival. Furthermore, we provide explicit expressions for the expected forgetting and overall generalization error to characterize the benefit of MoE in the learning performance in CL. Interestingly, adding more experts requires additional rounds before convergence, which may not enhance the learning performance. Finally, we conduct experiments on both synthetic and real datasets to extend these insights from linear models to deep neural networks (DNNs), which also shed light on the practical algorithm design for MoE in CL.

Edge-based machine learning inference typically relies on models trained on static or historical datasets, making them susceptible to performance degradation when data distribution shifts. While model retraining (continuous learning) can alleviate this issue, the integration of real-time data acquisition and model updates remains challenging due to the inherently distributed nature of data across end devices. Unmanned aerial vehicles (UAVs) offer an efficient means of gathering end-device data in edge computing networks (ECNs), thereby enabling realtime retraining. However, existing approaches design UAV-based data collection and model training in isolation, hindering realtime data-augmented training. To address this gap while account for UAVs' limited computational capacity, we propose an integrated optimization framework that coordinates data collection and model retraining through joint UAV swarm deployment and bandwidth allocation, enabling real-time model updates at the edge server. To this end, our objective is to maximize both the collected data volume and the number of training iterations, determined by data volume, transfer time, and update time, within a constrained training window. We formulate this NP-hard problem with tightly coupled variables and develop RL-EDABA, a Reinforcement Learning algorithm Embedded with Device Association and Bandwidth Allocation, enhanced by greedy strategies and convex optimization. Experiments show that RLEDABA effectively mitigates model accuracy loss with lower computational complexity compared with baseline methods.

The distributed deployment of Large Language Models (LLM) on edge servers close to users has unlocked the service provider's potential to deliver low-latency inference. To obtain more benefit by serving more resource-demanding inference tasks based on resource-limited edge servers, it is critical for the service provider to allocate inference to suitable edge servers. Three new challenges hinder existing approaches from being implemented: the distributed LLM inference requires edge servers to collaborate following a novel workflow different from other tasks; the generative nature of LLM incurs uncertainty in task resource occupations; considering the heterogeneity in users' latency requirements and service benefit, merely minimizing the total user-perceived latency can not maximize the benefit. In this paper, we make the first attempt to study the edge inference allocation problem for distributed LLM deployment while conquering these challenges. Specifically, we propose a collaborative workflow for edge servers to conduct distributed LLM inferences. Then, we estimate the resource occupations by employing Exact Conic Reformulation (ECR). Based on this, with the objective of maximizing the total service benefit, we formulate the inference allocation problem as a binary integer programming problem which is NP-hard. An approximate algorithm is proposed to find approximate solutions efficiently. Extensive experiments based on a real-world dataset demonstrate the performance of our approach.

The binary exponential backoff scheme is widely used in WiFi 7 and still incurs poor throughput performance under dynamic channel environments. Recent model-based approaches (e.g., non-persistent and p-persistent CSMA) simply optimize backoff strategies under a known and fixed node density, still leading to a large throughput loss due to inaccurate node density estimation. This paper is the first to propose LLM transformer-based in-context learning (ICL) theory for optimizing channel access. We design a transformer-based ICL optimizer to pre-collect collision-threshold data examples and a query collision case. They are constructed as a prompt as the input for the transformer to learn the pattern, which then generates a predicted contention window threshold (CWT). To train the transformer for effective ICL, we develop an efficient algorithm and guarantee a near-optimal CWT prediction within limited training steps. As it may be hard to gather perfect data examples for ICL in practice, we further extend to allow erroneous data input in the prompt. We prove that our optimizer maintains minimal prediction and throughput deviations from the optimal values. Experimental results on NS-3 further demonstrate our approach's advantage.

Information sharing platforms like TripAdvisor and Waze involve human agents as both information producers and consumers. All these platforms operate in a centralized way to collect agents' latest observations of new options (e.g., restaurants, hotels, travel routes) and share such information with all in real time. However, after hearing the central platforms' live updates, many human agents are found selfish and unwilling to further explore unknown options for the benefit of others in the long run. To regulate the human-in-the-loop learning (HILL) game against selfish agents' free-riding, this paper proposes a paradigm shift from centralized to decentralized way of operation that forces agents' local explorations through restricting information sharing. When game theory meets distributed learning, we formulate our decentralized communication mechanism's design as a new multi-agent Markov decision process (MA-MDP), and derive its analytical condition to outperform today's centralized operation. As the optimal decentralized communication mechanism in MA-MDP is NP-hard to solve, we present an asymptotically optimal algorithm with linear complexity to determine the mechanism's timing of intermittent information sharing. Then we turn to non-myopic agents who may revert to even over-explore, and adapt our mechanism design to work. Simulation experiments using real-world dataset demonstrate the effectiveness of our decentralized mechanisms for various scenarios.

The development of wireless technology enables numerous applications of remote-controlled devices (e.g., unmanned aerial vehicles), yet their intrusion poses significant threats to restricted areas, including military bases, airports, and private spaces belonging to individuals and organizations. To effectively counter the intruders, we propose a novel sensing assisted jamming (SAJA) scheme in two-stage transmission protocol, where we are the first to employ beam scanning to enhance the jamming gain to neutralize intruders. In the first stage, we determine the number of sensing beamsLfor detecting intruders. We show that a largerLleads to a more accurate angle range, thus enabling a higher jamming beam gain. In the second stage, robust jamming beamforming is designed to disable the intruders within the estimated angle range. The problem facing a single intruder is already non-convex, and we decouple it into two subproblems and develop algorithms for both single- and multi-intruder scenarios. In single-intruder scenarios, we first derive the closed-form expression for robust beamforming design with fixedL, and then apply the bisection search method to determine the minimumL. Facing multiple possible intruders, we further design a multi-round anti-intruder algorithm to address power insufficiency. In each round, we check the problem feasibility withL=Lmax(Lmaxis the maximum number of sensing beams) and use a jamming-to-noise-ratio based method to selectively target intruders until the problem is feasible. Furthermore, we derive a semi-closed-form solution to the robust jamming beamforming vector using Lagrange duality theory. Finally, simulation results validate the effectiveness and robustness of the proposed SAJA scheme against existing schemes.

Today navigation applications (e.g., Waze and Google Maps) enable human users to learn and share the latest traffic observations, yet such information sharing simply aids selfish users to predict and choose the shortest paths to jam each other. Prior routing game studies focus on myopic users in oversimplified one-shot scenarios to regulate selfish routing via information hiding or pricing mechanisms. For practical human-in-the-loop learning (HILL) in repeated routing games, we face non-myopic users of differential past observations and need new mechanisms (preferably non-monetary) to persuade users to adhere to the optimal path recommendations. We model the repeated routing game in a typical parallel transportation network, which generally contains one deterministic path and$N$stochastic paths. We first prove that no matter under the information sharing mechanism in use or the latest routing literature’s hiding mechanism, the resultant price of anarchy (PoA) for measuring the efficiency loss from social optimum can approach infinity, telling arbitrarily poor exploration-exploitation tradeoff over time. Then we propose a novel user-differential probabilistic recommendation (UPR) mechanism to differentiate and randomize path recommendations for users with differential learning histories. We prove that our UPR mechanism ensures interim individual rationality for all users and significantly reduces$\text{PoA}=\infty$to close-to-optimal$\text{PoA}=1+\frac{1}{4N+3}$, which cannot be further reduced by any other non-monetary mechanism. In addition to theoretical analysis, we conduct extensive experiments using real-world datasets to generalize our routing graphs and validate the close-to-optimal performance of UPR mechanism.

In modern resource-sharing systems, multiple agents access limited resources with unknown stochastic conditions to perform tasks. When multiple agents access the same resource (arm) simultaneously, they compete for successful usage, leading to contention and reduced rewards. This motivates our theoretical study of competitive multi-armed bandit (CMAB) games. In this paper, we study a new$N$-player$K$-arm competitive MAB game, where non-myopic players (agents) compete with each other to form diverse private estimations of unknown arms over time. Their possible collisions on the same arms and the time-varying nature of arm rewards make the policy analysis here more involved than the existing studies for myopic players. We explicitly analyze the threshold-based structures of the social optimum and the existing selfish policy, showing that the latter causes prolonged convergence times$\Omega (\frac{K}{\eta ^{2}}\ln ({\frac{KN}{\delta }}))$, while the socially optimal policy with coordinated communication reduces it to$\mathcal {O}(\frac{K}{N\eta ^{2}}\ln {(\frac{K}{\delta })})$. Based on the policy comparison, we prove that the competition among selfish players for the best arm can result in an infinite price of anarchy (PoA), indicating an arbitrarily large efficiency loss compared to the social optimum. We further prove that no informational (non-monetary) mechanism (including Bayesian persuasion) can reduce the infinite PoA, as strategic misreporting by non-myopic players undermines such approaches. To address this, we propose a Combined Informational and Side-Payment (CISP) mechanism, which provides socially optimal arm recommendations with proper informational and monetary incentives to players according to their diverse and time-varying private beliefs. Our CISP mechanism keeps ex-post budget balanced for the social planner and ensures truthful reporting from players, thereby achieving the minimum$\text{PoA}=1$and the same convergence time as the social optimum.

In congestion games, selfish users behave myopically to crowd to the shortest paths, and the social planner designs mechanisms to regulate such selfish routing through information or payment incentives. However, such mechanism design requires the knowledge of time-varying traffic conditions and it is the users themselves to learn and report past road experiences to the social planner (e.g., Waze or Google Maps). When congestion games meet mobile crowdsourcing, it is critical to incentivize selfish users to explore non-shortest paths in the best exploitation-exploration trade-off. First, we consider a simple but fundamental parallel routing network with one deterministic path and multiple stochastic paths for users with an average arrival probability$\lambda $. We prove that the current myopic routing policy (widely used in Waze and Google Maps) misses both exploration (when strong hazard belief) and exploitation (when weak hazard belief) as compared to the social optimum. Due to the myopic policy’s under-exploration, we prove that the caused price of anarchy (PoA) is larger than$\frac {1}{1-\rho ^{\frac {1}{\lambda }}}$, which can be arbitrarily large as discount factor$\rho \rightarrow 1$. To mitigate such huge efficiency loss, we propose a novel selective information disclosure (SID) mechanism: we only reveal the latest traffic information to users when they intend to over-explore stochastic paths upon arrival, while hiding such information when they want to under-explore. We prove that our mechanism successfully reduces PoA to be less than 2. Besides the parallel routing network, we further extend our mechanism and PoA results to any linear path graphs with multiple intermediate nodes. In addition to the worst-case performance evaluation, we conduct extensive simulations with both synthetic and real transportation datasets to demonstrate the close-to-optimal average-case performance of our SID mechanism.

To promote viral marketing, major social platforms (e.g., Facebook Marketplace and Pinduoduo) repeatedly select and invite different users (as seeds) in online social networks to share fresh information about a product or service with their friends. Thereby, we are motivated to optimize a multi-stage seeding process of viral marketing in social networks, and adopt the recent notions of the peak and the average age of information (AoI) to measure the timeliness of promotion information received by network users. Our problem is different from the literature on information diffusion in social networks, which limits to one-time seeding and overlooks AoI dynamics or information replacement over time. As a critical step, we manage to develop closed-form expressions that characterize and trace AoI dynamics over any social network. For the peak AoI problem, we first prove the NP-hardness of our multi-stage seeding problem by a highly non-straightforward reduction from the dominating set problem, and then present a new polynomial-time algorithm that achieves good approximation guarantees (e.g., less than 2 for linear network topology). To minimize the average AoI, we also prove that our problem is NP-hard by properly reducing it from the set cover problem. Benefiting from our two-sided bound analysis on the average AoI objective, we build up a new framework for approximation analysis and link our problem to a much simplified sum-distance minimization problem. This intriguing connection inspires us to develop another polynomial-time algorithm that achieves a good approximation guarantee. Additionally, our theoretical results are well corroborated by experiments on a real social network.

The channel state information (CSI) of malicious users in the field of physical layer security is usually difficult to obtain due to the users’ passive listening. However, as sensing is integrating into the cellular network, it becomes feasible to acquire the CSI of passive eavesdroppers. Motivated by this, we propose a novel sensing-aided secure communication (SASC) scheme in this paper, which is implemented by a two-stage transmission protocol including beam sensing of the eavesdropper’s CSI and secure communication to the legitimate user against the eavesdropper. In the first stage, the base station (BS) needs to decide the number of sensing beams$L$, and we derive the closed-form Cramer-Rao bound (CRB) to establish the relationship between the estimated angle range for the eavesdropper’s location and$L$, where a larger$L$for beam sensing returns a tighter CRB to locate the eavesdropper. In the second stage, the BS designs the beamforming vector to transmit confidential data to the legitimate user and avoid leakage to the eavesdropper in the estimated range. As sensing affects the subsequent secure communication, we decouple the non-convex two-stage joint optimization problem into two subproblems and solve it by backward induction. In particular, in the secrecy beamforming subproblem given$L$, we investigate the worst-case information leakage by constructing a convex hull to approximate the angle range from sensing. Then we develop a semi-closed-form solution of robust secrecy beamforming vector, and use it for the other subproblem to decide initial$L$. To achieve this goal, we also obtain a semi-closed-form solution of$L$by considering all potential estimated results in the worst case. Finally, we present simulation results to show the effectiveness and robustness of the proposed SASC scheme as compared to various benchmarks.

Content caching is a promising solution to overcome the backhaul traffic delay issue by caching content at the base station (BS). However, the performance of content caching is restricted by the limited BS cache storage. In this paper, we are the first to employ artificial intelligence generated content (AIGC) to enhance the content caching performance by empowering the BS’s intelligence capability. Besides caching a popular file, our AIGC-enhanced BS can alternatively cache its prompt of smaller size to reconstruct the file whenever needed by mobile terminals. We reveal the fundamental tradeoff between caching storage saving and computation delay for AIGC to decide whether to cache a file or its prompt. In this regard, this paper investigates the new problem of joint hybrid caching, computation and communication resource allocation optimization to minimize the average system latency to serve mobile terminals. As the problem is a non-convex and involves mixed integer non-linear programming, we develop a divide-and-conquer algorithm to decompose the problem into two timescale levels. We theoretically prove that our AIGC-enhanced hybrid content caching outperforms the conventional content caching once the computation capacity is non-trivial. Extensive simulation results also validate the superiority of the proposed hybrid content caching.

Wireless federated learning (WFL) enables devices to collaboratively train a global model via local model training, uploading and aggregating. However, WFL faces the data scarcity/heterogeneity problem (i.e., data are limited and unevenly distributed among devices) that degrades the learning performance. In this regard, artificial intelligence generated content (AIGC) can synthesize various types of data to compensate for the insufficient local data. Nevertheless, downloading synthetic data or uploading local models iteratively takes a lot of time, especially for a large amount of devices. To address this issue, we propose to leverage non-orthogonal multiple access (NOMA) to achieve efficient synthetic data and local model transmission. This paper is the first to combine AIGC and NOMA with WFL to maximally enhance the learning performance. For the proposed NOMA+AIGC-enhanced WFL, the problem of jointly optimizing the synthetic data distribution, two-way communication and computation resource allocation to minimize the global learning error is investigated. The problem belongs to mixed integer nonlinear programming, whose optimal solution is intractable to find. We first employ the block coordinate descent method to decouple the complicated-coupled variables, and then resort to our analytical method to derive an efficient low-complexity local optimal solution with partial closed-form results. Extensive simulations validate the superiority of the proposed scheme compared to the existing and benchmark schemes such as the frequency/time division multiple access based AIGC-enhanced schemes.

Today mobile users learn and share their traffic observations via crowdsourcing platforms (e.g., Google Maps and Waze). Yet such platforms myopically recommend the currently shortest path to users, and selfish users are unwilling to travel to longer paths of varying traffic conditions to explore. Prior studies focus on one-shot congestion games without information learning, while our work studies how users learn and alter traffic conditions on stochastic paths in a distributed manner. Our analysis shows that, as compared to the social optimum in minimizing the long-term social cost via optimal exploration-exploitation tradeoff, the myopic routing policy leads to severe under-exploration of stochastic paths with the price of anarchy (PoA) greater than 2. Besides, it fails to ensure the correct learning convergence about users' traffic hazard beliefs. To mitigate the efficiency loss, we first show that existing information-hiding mechanisms and deterministic path-recommendation mechanisms in Bayesian persuasion literature do not work with even$\mathbf{PoA}=\infty$. Accordingly, we propose a new combined hiding and probabilistic recommendation (CHAR) mechanism to hide all information from a selected user group and provide state-dependent probabilistic recommendations to the other user group. Our CHAR successfully ensures PoA less than$\frac{5}{4}$, which cannot be further reduced by any other informational mechanism. Additionally, we experiment with real-world data to verify our CHAR's good average performance.

Clustered Federated Learning (FL) is an extension of FL that organizes participating devices into clusters or groups, aiming to train heterogeneous models by grouping devices with diverse data distributions, thus fostering local collaboration within clusters for improved model performance. The increased model diversity introduces challenges stemming from the inherent heterogeneity in functions being modeled (e.g. for targeted advertising in platforms like Facebook), leading to a more natural prevalence of non-independent, non-identically distributed (non-IID) data in clustered FL environments. Moreover, another notable security challenge is that malicious agents can not only change their model updates to the aggregator but also misreport their cluster associations/identities in the clustered FL process. This paper introduces an algorithm called Coordinate-wise Median Clustered (CMC) to address the two challenges of heterogeneous model generation across agents and being robust against malicious agents. This is an algorithm that combines clustering and robust techniques, taking ideas from co-ordinate wise median attack-robust to adapt them to clustered FL. We prove that FedAvg per cluster does not converge under adversarial attacks while our CMC algorithm converges quickly to achieve high accuracy.

As a symbol-level precoding scheme, constructive interference precoding (CIP) has been demonstrated its superiority in multi-antenna orthogonal multiple access (OMA). By utilizing both the channel state information (CSI) and data symbols, harmful multi-user interference can be converted into useful reception power via the well-designed CIP. When CIP meets non-orthogonal multiple access (NOMA) whose bottle-neck is usually at the weaker user, this paper is the first to propose CIP to enhance the downlink MISO-NOMA networks, by making the desired signal of the stronger user in a typical NOMA pair constructive to the weaker user. In our CIP-NOMA scheme, we properly design the CIP precoder for transmit power minimization at the base station (BS), subject to signal-to-interference-plus-noise ratio (SINR) requirements of NOMA users. We further derive its closed-form solutions with Karush-Kuhn-Tucker (KKT) conditions, and optimally obtain the desired CIP precoders. Moreover, as compared to conventional NOMA schemes, we theoretically prove that once two NOMA users possess distinct channel gains, our optimized CIP-NOMA scheme always uses lower transmit power to reach the SINR thresholds. To be robust against the channel estimation errors, we extend our CIP-NOMA scheme to the scenario of imperfect CSI, by further addressing the hidden CSI errors. Specifically, we first introduce some auxiliary variables to separate the coupled vectors, and then use S-Procedure and semi-definite relaxation (SDR) to further transform them into convex ones. Extensive simulations verify that our CIP-NOMA scheme greatly outperforms the benchmarks with both perfect and imperfect CSI.

Given the proximity of many wireless users and their diversity in consuming local resources (e.g., data-plans, computation and energy resources), device-to-device (D2D) resource sharing is a promising approach towards realizing a sharing economy. This paper adopts an easy-to-implement greedy matching algorithm with distributed fashion and only sub-linear$O(\log n)$parallel complexity (in user number$n$) for large-scale D2D sharing. Practical cases indicate that the greedy matching's average performance is far better than the worst-case approximation ratio 50% as compared to the optimum. However, there is no rigorous average-case analysis in the literature to back up such encouraging findings and this paper is the first to present such analysis for multiple representative classes of graphs. For 1D linear networks, we prove that our greedy algorithm performs better than 86.5% of the optimum. For 2D grids, though dynamic programming cannot be directly applied, we still prove this average performance ratio to be above 76%. For the more challenging Erdos-Rényi random graphs, we equivalently reduce to the asymptotic analysis of random trees and successfully prove a ratio up to 79%. Finally, we conduct experiments using real data to simulate realistic D2D networks, and show that our analytical performance measure approximates well practical cases.

Today mobile crowdsourcing platforms invite users to provide anonymous reviews about service experiences, yet many reviews are found biased to be extremely positive or negative. The existing methods find it difficult to learn from biased reviews to infer the actual service state, as the state can also be extreme and the platform cannot verify the truthfulness of reviews immediately. Further, reviewers can hide their (positive or negative) bias types and proactively adjust their anonymous reviews against the platform's inference. To our best knowledge, we are the first to study how to save mobile crowdsourcing from cheap-talk and strategically learn from biased users' reviews. We formulate the problem as a dynamic Bayesian game, including users' service-type messaging and the platform's follow-up rating/inference. Our closed-form PBE shows that an extremely-biased user may still honestly message to convince the platform of listening to his review. Such Bayesian game-theoretic learning obviously outperforms the latest common schemes especially when there are multiple diversely-biased users to compete. For the challenging single-user case, we further propose a time-evolving mechanism with the platform's commitment inferences to ensure the biased user's truthful messaging all the time, whose performance improves with more time periods to learn from more historical data.

In location-based services(LBSs), it is promising for users to crowdsource and share their Point-of-Interest(PoI) information with each other in a common cache to reduce query frequency and preserve location privacy. Yet most studies on multi-user privacy preservation overlook the opportunity of leveraging their service flexibility. This paper is the first to study multiple users’ strategic cooperation against an adversary's optimal inference attack, by leveraging mutual service flexibility. We formulate the multi-user privacy cooperation against the adversary as a max-min adversarial game and solve it in a linear program. Unlike the vast literature, even if a user finds the cached information useful, we prove it beneficial to still query the platform to further confuse the adversary. As the linear program's computational complexity still increases superlinearly with the number of users’ possible locations, we propose a binary obfuscation scheme in two opposite spatial directions to achieve guaranteed performance with only constant complexity. Perhaps surprisingly, a user with a greater service flexibility should query with a less obfuscated location to add confusion. Finally, we provide guidance on the optimal query sequence among LBS users. Simulation results show that our crowdsourced privacy protection scheme greatly improves users’ privacy as compared with existing approaches.

Today mobile users learn and share their traffic observations via crowdsourcing platforms (e.g., Google Maps and Waze). Yet such platforms simply cater to selfish users' myopic interests to recommend the shortest path, and do not encourage enough users to travel and learn other paths for future others. Prior studies focus on one-shot congestion games without considering users' information learning, while our work studies how users learn and alter traffic conditions on stochastic paths in a human-in-the-loop manner. In a typical parallel routing network with one deterministic path and multiple stochastic paths, our analysis shows that the myopic routing policy (used by Google Maps and Waze) leads to severe under-exploration of stochastic paths. This results in a price of anarchy (PoA) greater than 2, as compared to the socially optimal policy achieved through optimal exploration-exploitation tradeoff in minimizing the long-term social cost. Besides, the myopic policy fails to ensure the correct learning convergence about users' traffic hazard beliefs. To address this, we focus on informational (non-monetary) mechanisms as they are easier to implement than pricing. We first show that existing information-hiding mechanisms and deterministic path-recommendation mechanisms in Bayesian persuasion literature do not work with even$\text{PoA}=\infty$. Accordingly, we propose a new combined hiding and probabilistic recommendation (CHAR) mechanism to hide all information from a selected user group and provide state-dependent probabilistic recommendations to the other user group. Our CHAR mechanism successfully ensures PoA less than$\frac{5}{4}$, which cannot be further reduced by any other informational (non-monetary) mechanism. Besides the parallel network, we further extend our analysis and CHAR mechanism to more general linear path graphs with multiple intermediate nodes, and we prove that the PoA results remain unchanged. Additionally, we carry out experiments with real-world datasets to further extend our routing graphs and verify the close-to-optimal performance of our CHAR mechanism.

Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms and also show that the proposed method can reduce time and energy costs significantly compared with the benchmark strategies based on multi-hop and long-range flight.

As the sociological theory of homophily suggests, people tend to interact with those of similar preferences. Motivated by this well-established phenomenon, today's online sellers, such as Amazon, seek to learn a new buyer's private preference from his friends’ purchase records. Although such learning allows the seller to enable personalized pricing and boost revenue, buyers are also increasingly aware of these practices and may alter their social behaviors accordingly. This paper presents the first study regarding how buyers strategically manipulate their social interaction signals considering their preference correlations, and how a seller can take buyers’ strategic social behaviors into consideration when designing the pricing scheme. Starting with the basic two-buyer network, we propose and analyze a parsimonious model that uniquely captures the double-layered information asymmetry between the seller and buyers, integrating both individual buyer information and inter-buyer correlation information. Our analysis reveals that only high-preference buyers tend to manipulate their social interactions to evade the seller's personalized pricing, but surprisingly, their payoffs may actually worsen as a result. Additionally, we demonstrate that the seller can considerably benefit from the learning practice, regardless of whether the buyers are aware of this fact or not. Indeed, our analysis reveals that buyers’ learning-aware strategic manipulation has only a slight impact on the seller's revenue. In light of the tightening regulatory policies concerning data access, it is advisable for sellers to maintain transparency with buyers regarding their access to buyers’ social interaction data for learning purposes. This finding aligns well with current informed-consent industry practices for data sharing. Finally, we explore the seller's dynamic learning process across multiple interconnected buyers, and show that learning previous buyers’ preferences may not necessarily help infer other buyers’ preferences in the seller's subsequent learning phase.

Federated Learning (FL) has emerged as a prominent distributed machine learning framework that enables geographically discrete clients to train a global model collaboratively while preserving their privacy-sensitive data. However, due to the non-independent-and-identically-distributed (Non-IID) data generated by heterogeneous clients, the performances of the conventional federated optimization schemes such as FedAvg and its variants deteriorate, requiring the design to adaptively adjust specific model parameters to alleviate the negative influence of heterogeneity. In this paper, by leveraging entropy as a new metric for assessing the degree of system disorder, we propose an adaptive FEDerated learning algorithm based on ENTropy theory (FedEnt) to alleviate the parameter deviation among heterogeneous clients and achieve fast convergence. Nevertheless, given the data disparity and parameter deviation of heterogeneous clients, determining the optimal dynamic learning rate for each client becomes a challenging task as there is no communication among participating clients during the local training epochs. To enable a decentralized learning rate for each participating client, we first introduce the mean-field terms to estimate the components associated with other clients’ local parameters. Furthermore, we provide rigorous theoretical analysis on the existence and determination of the mean-field estimators. Based on the mean-field estimators, the closed-form adaptive learning rate for each client is derived by constructing the Hamilton equation. Moreover, the convergence rate of our proposed FedEnt is proved. The extensive experimental results on the real-world datasets (i.e., MNIST, EMNIST-L, CIFAR10, and CIFAR100) show that our FedEnt algorithm surpasses FedAvg and its variants (i.e., FedAdam, FedProx, and FedDyn) under Non-IID settings and achieves a faster convergence rate.

How to incentivize strategic workers using limited budget is a very fundamental problem for crowdsensing systems; nevertheless, since the sensing abilities of the workers may not always be known as prior knowledge due to the diversities of their sensor devices and behaviors, it is difficult to properly select and pay the unknown workers. Although the uncertainties of the workers can be addressed by the standardCombinatorial Multi-Armed Bandit(CMAB) framework in existing proposals through a trade-off between exploration and exploitation, we may not have sufficient budget to enable the trade-off among the individual workers, especially when the number of the workers is huge while the budget is limited. Moreover, the standard CMAB usually assumes the workers always stay in the system, whereas the workers may join in or depart from the system over time, such that what we have learnt for an individual worker cannot be applied after the worker leaves. To address the above challenging issues, in this paper, we first propose an off-lineContext-Aware CMAB-based Incentive(CACI) mechanism. We innovate in leveraging the exploration-exploitation trade-off in an elaborately partitioned context space instead of the individual workers, to effectively incentivize the massive unknown workers with a very limited budget. We also extend the above basic idea to the on-line setting where unknown workers may join in or depart from the systems dynamically, and propose an on-line version of the CACI mechanism. Specifically, by the exploitation-exploration trade-off in the context space, we learn to estimate the sensing ability of any unknown worker (even it never appeared in the system before) according to its context information. We perform rigorous theoretical analysis to reveal the upper bounds on the regrets of our CACI mechanisms and to prove their truthfulness and individual rationality, respectively. Extensive experiments on both synthetic and real datasets are also conducted to verify the efficacy of our mechanisms.

Traditional user profiling techniques rely on browsing history or purchase records to identify users’ willingness to pay. This enables sellers to offer personalized prices to profiled users while charging only a uniform price to non-profiled users. However, the emergence of privacy-enhancing technologies has caused users to actively avoid on-site data tracking. Today, major online sellers have turned to public platforms such as online social networks to better track users’ profiles from their product-related discussions. This paper presents the first analytical study on how users should best manage their social activities against potential personalized pricing, and how a seller should strategically adjust her pricing scheme to facilitate user profiling in social networks. We formulate a dynamic Bayesian game played between the seller and users under asymmetric information. The key challenge of analyzing this game comes from the double couplings between the seller and the users as well as among the users. Furthermore, the equilibrium analysis needs to ensure consistency between users’ revealed information and the seller’s belief under random user profiling. We address these challenges by alternately applying backward and forward induction, and successfully characterize the unique perfect Bayesian equilibrium (PBE) in closed form. Our analysis reveals that as the accuracy of profiling technology improves, the seller tends to raise the equilibrium uniform price to motivate users’ increased social activities and facilitate user profiling. However, this results in most users being worse off after the informed consent policy is imposed to ensure users’ awareness of data access and profiling practices by potential sellers. This finding suggests that recent regulatory evolution towards enhancing users’ privacy awareness may have unintended consequences of reducing users’ payoffs. Finally, we examine prevalent pricing practices where the seller breaks a pricing promise to personalize final offerings, and show that it only slightly improves the seller’s average revenue while introducing higher variance.

Though federated learning (FL) well preserves clients’ data privacy, many clients are still reluctant to join FL given the communication cost and energy consumption in their mobile devices. It is important to design pricing compensations to motivate enough clients to join FL and distributively train the global model. Prior pricing mechanisms for FL are static and cannot adapt to clients’ random arrival pattern over time. We propose a new dynamic pricing solution in closed-form by constructing the Hamiltonian function to optimally balance the client recruitment time and the model training time, without knowing clients’ actual arrivals or training costs. During the client recruitment phase, we offer time-dependent monetary rewards per client arrival to trade off between the total payment and the FL model’s accuracy loss. Such reward gradually increases when we approach to the recruitment deadline or have greater data aging, and we also extend the deadline if the clients’ training time per iteration becomes shorter. Further, we extend to consider heterogeneous client types in training data size and training time per iteration. We successfully extend our dynamic pricing solution and develop an optimal algorithm of linear complexity to monotonically select client types for FL. Finally, we also show robustness of our solution against estimation error of clients’ data sizes, and run numerical experiments to validate our results.

Rate splitting multiple access (RSMA) provides a flexible transmission framework that can be applied in mobile edge computing (MEC) systems. However, the research work on RSMA-assisted MEC systems is still at the infancy and many design issues remain unsolved, such as the MEC server and channel allocation problem in general multi-server and multi-channel scenarios as well as the user fairness issues. In this regard, we study an RSMA-assisted MEC system with multiple MEC servers, channels and devices, and consider the fairness among devices. A max-min fairness computation offloading problem to maximize the minimum computation offloading rate is investigated. Since the problem is difficult to solve optimally, we develop an efficient algorithm to obtain a suboptimal solution. Particularly, the time allocation and the computing frequency allocation are derived as closed-form functions of the transmit power allocation and the successive interference cancellation (SIC) decoding order, while the transmit power allocation and the SIC decoding order are jointly optimized via the alternating optimization method, the bisection search method and the successive convex approximation method. For the channel and MEC server allocation problem, we transform it into a hypergraph matching problem and solve it by matching theory. Simulation results demonstrate that the proposed RSMA-assisted MEC system outperforms current MEC systems under various system setups.

In congestion games, users make myopic routing decisions to jam each other, and the social planner with the full information designs mechanisms on information or payment side to regulate. However, it is difficult to obtain time-varying traffic conditions, and emerging crowdsourcing platforms (e.g., Waze and Google Maps) provide a convenient way for mobile users travelling on the paths to learn and share the traffic conditions over time. When congestion games meet mobile crowdsourcing, it is critical to incentive selfish users to change their myopic routing policy and reach the best exploitation-exploration trade-off. By considering a simple but fundamental parallel routing network with one deterministic path and multiple stochastic paths for atomic users, we prove that the myopic routing policy's price of anarchy (PoA) can be arbitrarily large as the discount factor approaches 1. To remedy such huge efficiency loss, we propose a selective information disclosure (SID) mechanism: we only reveal the latest traffic information to users when they intend to over-explore the stochastic paths, while hiding such information when they want to under-explore. We prove that our mechanism reduces PoA to less than 2. Besides the worst-case performance, we further examine our mechanism's average-case performance by using extensive simulations.

In federated learning (FL), clients cooperatively train a global model without revealing their raw data but gradients or parameters, while the local information can still be disclosed from local outputs transmitted to the parameter server. With such privacy concerns, a client may overly add artificial noise to his local updates to compromise the global model training, and we prove the selfish noise adding leads to an infinite price of anarchy (PoA). This paper proposes a novel pricing mechanism to regulate privacy-sensitive clients without verifying their parameter updates, unlike existing privacy mechanisms that assume the server's full knowledge of added noise. Without knowing the ground truth, our mechanism reaches the social optimum to best balance the global training error and privacy loss, according to the difference between a client's updated parameter and all clients' average parameter. We also improve the FL convergence bound by refining the aggregation rule at the server to account for different clients' noise variances. Moreover, we extend our pricing scheme to fit incomplete information of clients' privacy sensitivities, ensuring their truthful type reporting and the system's ex-ante budget balance. Simulations show that our pricing scheme greatly improves the system performance especially when clients have diverse privacy sensitivities.

As a symbol-level precoding scheme, constructive interference precoding (CIP) has been demonstrated its superiority in multi-antenna orthogonal multiple access (OMA) systems. By utilizing both the channel state information (CSI) and data symbols, harmful multi-user interference can convert to useful reception power via the well-designed CIP precoder. When CIP meets non-orthogonal multiple access (NOMA) whose bottleneck is usually at the weaker user side, this paper is the first to propose CIP to enhance the downlink MISO-NOMA networks, by making the desired signal of the stronger user in a typical NOMA pair constructive to the weaker user. In our CIP-NOMA scheme, we properly design the CIP precoder for transmit power minimization at the base station (BS), subject to signal-to-interference-plus-noise ratio (SINR) requirements of NOMA users. By replacing the non-convex constraints with convex linear matrix inequalities, we optimally obtain the precoding solutions for our CIP-NOMA scheme by semi-definite relaxation (SDR) method. Moreover, as compared to conventional NOMA schemes, we theoretically prove that once two NOMA users possess distinct channel gains, our optimized CIP-NOMA scheme always uses smaller transmit power to reach the target SINR thresholds. We further extend our CIP-NOMA scheme to the scenario of imperfect CSI, by further addressing the hidden CSI errors. Finally, we run extensive simulations to verify that our proposed CIP-NOMA scheme greatly outperforms zero-forcing (ZF), conventional NOMA and conventional CIP schemes.

To promote viral marketing, major social platforms (e.g., Facebook Marketplace and Pinduoduo) repeatedly select and invite different users (as seeds) in online social networks to share fresh information about a product or service with their friends. Thereby, we are motivated to optimize a multi-stage seeding process of viral marketing in social networks, and adopt the recent notions of the peak and the average age of information (AoI) to measure the timeliness of promotion information received by network users. Our problem is different from the literature on information diffusion in social networks, which limits to one-time seeding and overlooks AoI dynamics or information replacement over time. As a critical step, we manage to develop closed-form expressions that characterize and trace AoI dynamics over any social network. For the peak AoI problem, we first prove the NP-hardness of our multi-stage seeding problem by a highly non-straightforward reduction from the dominating set problem, and then present a new polynomial-time algorithm that achieves good approximation guarantees (e.g., less than 2 for linear network topology). For minimizing the average AoI, we also prove that our problem is NP-hard by properly reducing it from the set cover problem. Benefiting from our two-side bound analysis on the average AoI objective, we build up a new framework for approximation analysis and link our problem to a much simplified sum-distance minimization problem. This intriguing connection inspires us to develop another polynomial-time algorithm that achieves a good approximation guarantee. Additionally, our theoretical results are well corroborated by experiments on a real social network.

Today many users are invited by a crowdsourcing platform (e.g., TripAdvisor, and Waze) to provide their anonymous reviews about service experiences (e.g., in hotels, restaurants, and trips), yet many reviews are found biased to be extremely positive or negative. It is difficult to learn from biased users' reviews to infer actual service state, as the service state can also be extreme and the platform cannot verify immediately. Further, due to the anonymity, reviewers can hide their bias types (positive or negative) from the platform and adaptively adjust their reviews against the platform's inference. To our best knowledge, we are the first to study how to save crowdsourcing from cheap-talk and strategically learn the actual service state from biased users' reviews. We formulate the problem as a dynamic Bayesian game, including the unknown users' messaging and the platform's follow-up inference. Through involved analysis, we provide closed-form expressions for the Perfect Bayesian Equilibrium (PBE). Our PBE shows that the platform's strategic learning can successfully prevent biased users from cheap-talk in most cases, where a user even with extreme bias still honestly messages to convince the platform of listening to his review. We prove that the price of anarchy (PoA) is 2, telling that the social cost can at most be doubled in the worst-case. As the user number becomes large, our platform always learns the actual service state. Perhaps surprisingly, the platform's expected cost may be worse off after adding one more user.

Energy storage systems (ESSs)-based demand response (DR) is an appealing way to save electricity bills for consumers under demand charge and time-of-use (TOU) price. In order to counteract the high investment cost of ESS, a novel operator-enabled ESS sharing scheme, namely, the "operator-as-a-consumer (OaaC)," is proposed and investigated in this article. In this scheme, the users and the operator form a Stackelberg game. The users send ESS orders to the operator and apply their own ESS dispatching strategies for their own purposes. Meanwhile, the operator maximizes its profit through optimal ESS sizing and scheduling, as well as pricing for the users' ESS orders. The feasibility and economic performance of OaaC are further analyzed by solving a bilevel joint optimization problem of ESS pricing, sizing, and scheduling. To make the analysis tractable, the bilevel model is first transformed into its single-level mathematical program with equilibrium constraints (MPEC) formulation and is then linearized into a mixed-integer linear programming (MILP) problem using multiple linearization methods. Case studies with actual data are utilized to demonstrate the profitability for the operator and simultaneously the ability of bill saving for the users under the proposed OaaC scheme.

After upgrading to 5G, a network operator still faces congestion when providing the ubiquitous wireless service to the crowd. To meet users' ever-increasing demand, some other operators (e.g., Fon) have been developing another crowdsourced WiFi network to combine many users' home WiFi access points and provide enlarged WiFi coverage to them. While the 5G network experiences negative network externality, the crowdsourced WiFi network helps offload traffic from 5G and its service coverage exhibits positive externality with its subscription number. To our best knowledge, we are the first to investigate how these two heterogeneous networks of diverse network externalities co-exist from an economic perspective. We propose a dynamic game theoretic model to analyze the hybrid interaction among the 5G operator, the crowdsourced WiFi operator, and users. Our user choice model with WiFi's complementarity for 5G allows users to choose both services, departing from the traditional economics literature where a user chooses one over another alternative. Despite of non-convexity of the operators pricing problems, we prove that the 5G operator facing severe congestion may lower his price, and he benefits from the introduction of crowdsourced WiFi. However, 5G operator with mild congestion charges users more and all the users' payoffs may decrease.

Unmanned aerial vehicle (UAV) technology is a promising solution for rapidly providing wireless communication services to ground users, where a UAV has limited service coverage and needs to fly through users at different locations for serving them locally. The existing UAV deployment studies largely assume the users’ demands do not change during UAV deployment. When the users’ demands dynamically change over time, the key challenge is how to adapt the UAV deployment strategy to the partial and even outdated observations on the users’ activities given the UAV's flying speed limit. In this paper, we study dynamic UAV deployment to learn and adapt to the time-varying user activities, where the activity pattern of a user (if out of the UAV service coverage) is hidden from the UAV and follows a time-slotted Markov chain that switches between active and idle states. We formulate the learning-and-adaption based UAV deployment problem as a partially observable Markov decision process (POMDP) to maximize the total discounted hit rate of active users, where the UAV decides for itself whether to chase an active user in a distant location (with delayed reward) or to wait for the idle user in the current location to return to the active state (with smaller service probability) over time. We show there is a fundamental delay-reward tradeoff, and prove that the UAV will optimally follow a threshold-based policy by waiting at an idle user for a time threshold before moving to another user. We also show the UAV is more likely to move if the temporal correlation of each user's idling pattern is stronger or the travel distance between users is shorter. Furthermore, we extend to a more general scenario where the UAV does not even know the parameters of each user's temporal activity distribution, and apply Q-learning to develop another threshold-based deployment policy for a multi-user scenario.

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing and local caching) to ground users. How to dynamically determine a UAV swarm's cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question but unaddressed in the literature. Regarding a single UAV's path planning design, we manage to substantially simplify the traditional dynamic program and propose an optimal algorithm of low computation complexity. After coordinating a large number K of UAVs, this simplified dynamic optimization problem becomes intractable and we alternatively present a fast iterative cooperation algorithm with provable approximation ratio$1-(1-\frac{1}{K})^{K}$in the worst case. To relax UAVs' battery capacity limit for sustainable service provisioning, we further allow UAVs to travel to charging stations in the mean time and thus jointly design UAVs' path planning over users' locations and charging stations. We successfully transform the problem to an integer linear program by creating novel directed acyclic graph of the UAV-state transition diagram, and propose an iterative algorithm with constant approximation ratio.

While some mobile users in wireless networks may experience temporal scarcity of wireless network resources such as data plan, computation capacity and energy storage, some others may leave them underutilized. If the appropriate market existed, users connected locally with D2D links could exchange such resources with low communication cost and realize significant efficiency gains by reducing waste and achieving resource pooling. This paper proposes such a D2D trading market that scales for large numbers of users. Contrary to traditional resource allocation solutions that are mostly centralized, our double auction mechanism exploits local D2D connectivity and uses distributed computation to achieve near-optimal allocative efficiency. The final prices for each matched pair of buyer and seller are adjusted in a way to induce incentive compatibility and depend on their own declarations in terms of quantity and valuation. We prove that the overall mechanism has significant social welfare gains compared to other widely-used distributed pricing mechanisms. It is also individually rational, ex-ante budget balanced using a subscription fee, and robust to perturbations of the model parameters. To render the system fully manipulation-proof, we further propose a distributed auditing scheme that prevents users from altering the decentralized computation to increase their profits. Finally, we model the repeated execution of the mechanism and determine the best trading frequency by taking into account the arrivals and departures of new participants.

In location-based services (LBSs), it is promising for multiple users to cache and share their Point-of-Interest (PoI) information with each other to reduce overall query frequency and preserve location privacy. Yet most studies on multi-user privacy preservation overlook the opportunity of leveraging service flexibility, where many users are flexible and may add obfuscation to individual LBS query. This paper is the first to study how multiple users cooperate to query with obfuscation against the adversary’s optimal inference attack, by leveraging their mutual service flexibility. Unlike the literature, even if a user already finds the shared PoI information useful, we prove it beneficial for him to further query with obfuscated location to confuse the adversary. To save the computational complexity of the max-min adversarial game problem and derive the closed-form solution, we also propose a binary approximate solution, which is proved to guarantee good privacy performance for an average user. Perhaps surprisingly, the user with greater service flexibility should choose to query the LBS with less misreported location, to maximally confuse the adversary. Finally, we numerically compare our optimal and approximate solutions with the existing approaches to show our effective privacy improvement.

As the sociological theory of homophily suggests, people tend to interact with those of similar preferences. This motivates product sellers to learn buyers’ product preferences from the buyers’ friends’ purchase records. Although such learning allows sellers to enable personalized pricing to improve profits, buyers are also increasingly aware of such practices and may alter their behaviors accordingly. This paper presents the first study regarding how buyers may strategically manipulate their social interaction signals considering their preference correlations, and how an informed seller can take buyers’ strategic social behaviors into consideration when designing the pricing schemes. Our analytical results show that only high-preference buyers tend to manipulate their social interactions to hurdle the seller’s personalized pricing. Surprisingly, these high-preference buyers’ payoff may become worse after their strategic manipulation. Furthermore, we show that the seller can greatly benefit from the learning practice, no matter whether the buyers are aware of such learning or not. In fact, buyers’ learning-aware strategic manipulation only slightly reduces the seller’s revenue. Considering the increasingly stricter policies on data access by authorities, it is thus advisable for sellers to make buyers aware of their access and learning based on social interaction data. This justifies well with current regulatory policies and industry practices regarding informed consent for data sharing.

Proactive eavesdropping was used recently to efficiently intercept a suspicious wireless communication link, by jamming the suspicious destination node. However, While jamming helps weaken the suspicious link, it does not improve the eavesdropping channel from the source node to the legitimate eavesdropper. This work proposes to use intelligent reflecting surfaces (IRS) to enhance proactive eavesdropping, by jointly affecting both the suspicious and eavesdropping channels, and is the first to employ the cooperative passive beamforming in the double-IRS aided monitoring system. By considering the cooperative two single-reflection links and especially the double-reflection link, we jointly design the passive phase shift matrices of double IRSs to optimize the eavesdropping capability. Due to the coupled variables caused by double-IRS cooperation and the intractable unit modulus constraints of IRS elements, the non-convex optimization problem is difficult to solve. We first divide the problem into two subproblems, and apply the idea of minimization majorization to make each subproblem convex. We obtain the solution of reflective coefficient matrix in closed form, and then propose an alternating algorithm with low complexity to obtain the Karush-Kuhn-Tucker (KKT) solution. Finally, simulation results are presented to demonstrate the effectiveness of cooperative passive beamforming design over the traditional single-IRS and jamming assisted eavesdropping schemes.

Intelligent reflecting surface (IRS) is a promising technology to extend the wireless signal coverage and support the high performance communication. By intelligently adjusting the reflection coefficients of a large number of passive reflecting elements, the IRS can modify the wireless propagation environment in favour of signal transmission. Different from most of the prior works which did not consider any cooperation between IRSs, in this work we propose and study a cooperative double-IRS aided multiple-input multiple-output (MIMO) communication system under the line-of-sight (LoS) propagation channels. We investigate the capacity maximization problem by jointly optimizing the transmit covariance matrix and the passive beamforming matrices of the two cooperative IRSs. Although the above problem is non-convex and difficult to solve, we transform and simplify the original problem by exploiting a tractable characterization of the LoS channels. Then we develop a novel low-complexity algorithm whose complexity is independent of the number of IRS elements. Moreover, we analyze the capacity scaling orders of the double-IRS aided MIMO system with respect to an asymptotically large number of IRS elements or transmit power, which significantly outperform those of the conventional single-IRS aided MIMO system, thanks to the cooperative power gain brought by the double-reflection link and the spatial multiplexing gain harvested from the two single-reflection links. Extensive numerical results are provided to show that by exploiting the LoS channel properties, our proposed algorithm can achieve a desirable performance with low computational time. Also, our capacity scaling analysis is validated, and the double-IRS system is shown to achieve a much higher rate than its single-IRS counterpart as long as the number of IRS elements or the transmit power is not small.

When using location-based services (LBSs), a user obtains points-of-interest (PoI) information by providing the LBS platform with his current geo-location. Such a search leads to potential privacy leakage if an adversary has access to his geo-data. Traditionalk-anonymity mechanisms instruct a user to bear the overhead to report his current location together withk- 1 dummy locations to confuse the adversary, which only work well given a large numberk. Aware of the common practices that a user is actually flexible in service requirement (e.g., as long as the searched PoIs are within his walking distance), we propose a novel approach to help the user gain location privacy from service flexibility for the challenging case of a small numberk. By analyzing the strategic interaction between the user and the adversary in a dynamic Bayesian game, we prove that the user’s equilibrium strategy depends on the adversary’s capability of accessing geo-data. Takek= 2 for example, we find that if the adversary is not likely to access both geo-data, the user should report the two dummy locations at two different directions of his real location, and otherwise at the same direction. Perhaps surprisingly, the user may benefit from the adversary’s access to more geo-data. Furthermore, we extend the game-theoretic approach for multi-query and arbitrary user location distributions. Numerical results show that our approach obviously outperformsk-anonymity mechanisms especially under a small numberk.

Today many mobile users in various zones are invited to sense and send back real-time useful information to keep the freshness of the content updates in such zones. However, due to the sampling cost in sensing and transmission, a user may not have the incentive to contribute real-time information to help reduce the age of information (AoI). We propose dynamic pricing for each zone to offer age-dependent monetary returns and encourage users to sample information at different rates over time. This dynamic pricing design problem needs to well balance the monetary payments as rewards to users and the AoI evolution, and is challenging to solve especially under the incomplete information about users’ arrivals and their private sampling costs. After formulating the problem as a nonlinear constrained dynamic program, to avoid the curse of dimensionality, we first propose to approximate the dynamic AoI reduction as a time-average term and successfully solve the approximate dynamic pricing in closed-form. Further, we extend the AoI control from a single zone to many zones with heterogeneous user arrival rates and initial ages, where each zone cares not only its own AoI dynamics but also the average AoI of all the zones in a mean field game system to provide a holistic service. Accordingly, we propose decentralized mean field pricing for each zone to self-operate by using a mean field term to estimate the average age dynamics of all the zones, which does not even require many zones to exchange their local data with each other.

Due to its mobility and agility, unmanned aerial vehicle (UAV) has emerged as a promising technology for various tasks, such as sensing, inspection and delivery. However, a typical UAV has limited energy storage and cannot fly a long distance without being recharged. This motivates several existing proposals to use trucks and other ground vehicles to offer riding to help UAVs save energy and expand the operation radius. We present the first theoretical study regarding how UAVs should optimally hitch on ground vehicles, considering vehicles' different travelling patterns and supporting capabilities. For a single UAV, we derive closed-form optimal vehicle selection and hitching strategy. When vehicles only support hitching, a UAV would prefer the vehicle that can carry it closest to its final destination. When vehicles can offer hitching plus charging, the UAV may hitch on a vehicle that carries it farther away from its destination and hitch a longer distance. The UAV may also prefer to hitch on a slower vehicle for the benefit of battery recharging. For multiple UAVs in need of hitching, we develop the max-saving algorithm (MSA) to optimally match UAV-vehicle collaboration. We prove that the MSA globally optimizes the total hitching benefits for the UAVs.

A distributed machine learning platform needs to recruit many heterogeneous worker nodes to finish computation simultaneously. As a result, the overall performance may be degraded due to straggling workers. By introducing redundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first k (out of the total n) workers who finish computation. While existing studies focus on designing efficient coding schemes, the issue of designing proper incentives to encourage worker participation is still under-explored. This paper studies the platform's optimal incentive mechanism for motivating proper workers' participation in coded machine learning, despite the incomplete information about heterogeneous workers' computation performances and costs. A key contribution of this work is to summarize workers' multi-dimensional heterogeneity as a one-dimensional metric, which guides the platform's efficient selection of workers under incomplete information with a linear computation complexity. Moreover, we prove that the optimal recovery threshold k is linearly proportional to the participator number n if we use the widely adopted MDS codes for data encoding. We also show that the platform's increased cost due to incomplete information disappears when worker number is sufficiently large, but it does not monotonically decrease in worker number.

When using location-based services (LBSs), a user obtains points-of-interest $(\text{P}\text{o}\text{I})$ information by providing the LBS platform with his current geo-location. Such a search also leads to potential privacy leakage if an adversary has access to his geo-data. Traditional k-anonymity mechanisms instruct a user to bear the overhead to report his current location together with k-1 dummy locations to confuse the adversary, which only work well given a large number k. Aware of the common practices that a user is actually flexible in service requirement (e.g., as long as the searched PoIs are within his walking distance), we propose a novel approach to help the user gain location privacy from service flexibility for the case of a small number k. By analyzing the strategic interaction between the user and the adversary in a Bayesian game, we prove that the user with service flexibility should never report his real location for searching PoIs nearby. Instead, he should jointly use all k dummy locations to confuse the adversary’s inference of his real location. Take $k=2$ for example, we manage to show that if the adversary is not likely to access both dummy geo-data, the user should report the two dummy locations at two opposite directions of his real location, and otherwise at the same direction. Perhaps surprisingly, our approach may enable the user to benefit from the adversary’s access to more geo-data. Finally, extensive simulations using some real data show that our mechanism obviously outperforms k anonymity mechanism especially under a small number k.

Given the proximity of many wireless users and their diversity in consuming local resources (e.g., data-plans, computation and even energy resources), device-to-device (D2D) resource sharing is a promising approach towards realizing a sharing economy. In the resulting networked economy, n users segment themselves into sellers and buyers that need to be efficiently matched locally. This paper adopts an easy-to-implement greedy matching algorithm with distributed fashion and only sub-linear O(log n) parallel complexity, which offers a great advantage compared to the optimal but computational-expensive centralized matching. But is it efficient compared to the optimal matching? Extensive simulations indicate that in a large number of practical cases the average loss is no more than 10%, a far better result than the 50% loss bound in the worst case. However, there is no rigorous average-case analysis in the literature to back up such encouraging findings, which is a fundamental step towards supporting the practical use of greedy matching in D2D sharing. This paper is the first to present the rigorous average analysis of certain representative classes of graphs with random parameters, by proposing a new asymptotic methodology. For typical 2D grids with random matching weights we rigorously prove that our greedy algorithm performs better than 84.9% of the optimal, while for typical Erdős-Rényi random graphs we prove a lower bound of 79% when the graph is neither dense nor sparse. Finally, we use realistic data to show that our random graph models approximate well D2D sharing networks encountered in practice.

User profiling allows product sellers to identify users’ willingness to pay and enable personalized pricing. However, users’ information exploited in profiling is usually private and hard to obtain accurately due to users’ privacy concerns. With the increasing popularity of social networks, where users reveal their private information through social interactions, more sellers today profile users through their social data. This paper is the first to study how a seller optimizes personalized pricing through user profiling on social networks, where users proactively react by controlling their social activities and information leakage. We formulate and analyze a dynamic Bayesian game played between users and the seller. First, users decide their social activities by trading off the social network benefit against the potential risk of revealing private information. Then, the seller exploits users’ profiles to determine the personalized prices for the profiled users and a uniform price for the non-profiled users. It is challenging to analyze the Perfect Bayesian Equilibrium (PBE) of this game due to i) the randomness in user profiling, and ii) the coupling among users’ activity levels and that between the seller’s pricing decisions and users’ social activities. Despite the difficulty, we propose to alternate backward induction and forward induction to successfully solve the PBE. We show the surprising result that users’ activity levels do not monotonically decrease as the profiling technology improves. Instead, when user profiling is of high accuracy, the seller strategically chooses a high uniform price to stimulate their increased social activities to profile more users.

We study the mechanism design problem of a social planner for locating two facilities on a line interval [0, 1], where a set of n strategic agents report their locations and a mechanism determines the locations of the two facilities. We consider the requirement of a minimum distance 0 ≤ d ≤ 1 between the two facilities. Given the two facilities are heterogeneous, we model the cost/utility of an agent as the sum of his distances to both facilities. In the heterogeneous two-facility location game to minimize the social cost, we show that the optimal solution can be computed in polynomial time and prove that carefully choosing one optimal solution as output is strategyproof. We also design a strategyproof mechanism minimizing the maximum cost. Given the two facilities are homogeneous, we model the cost/utility of an agent as his distance to the closer facility. In the homogeneous two-facility location game for minimizing the social cost, we show that any deterministic strategyproof mechanism has unbounded approximation ratio. Moreover, in the obnoxious heterogeneous two-facility location game for maximizing the social utility, we propose new deterministic group strategyproof mechanisms with provable approximation ratios and establish a lower bound (7 − d)/6 for any deterministic strategyproof mechanism. We also design a strategyproof mechanism maximizing the minimum utility. In the obnoxious homogeneous two-facility location game for maximizing the social utility, we propose deterministic group strategyproof mechanisms with provable approximation ratios and establish a lower bound 4/3. Besides, in the two-facility location game with triple-preference, where each facility may be favorable, obnoxious, indifferent for any agent, we further motivate agents to report both their locations and preferences towards the two facilities truthfully, and design a deterministic group strategyproof mechanism with an approximation ratio 4.

A distributed machine learning platform needs to recruit many heterogeneous worker nodes to finish computation simultaneously. As a result, the overall performance may be degraded due to straggling workers. By introducing redundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first k (out of the total n) workers who finish computation. While existing studies focus on designing efficient coding schemes, the issue of designing proper incentives to encourage worker participation is still under-explored. This paper studies the platform's optimal incentive mechanism for motivating proper workers' participation in coded machine learning, despite the multi-dimensional incomplete information about heterogeneous workers' computation performances and costs. A key contribution of this work is to summarize workers' multi-dimensional heterogeneity as a one-dimensional metric, which guides the platform's efficient selection of workers under incomplete information with a linear computation complexity. Although the exact overall runtime is intractable, we characterize the platform's (asymptotically) optimal load assignment to heterogeneous workers in coded machine learning. When the platform has incomplete information about workers' costs, it is optimal to assign loads only based on workers' computation performances; when the platform further lacks workers' computation performance information, it is optimal to design the loads to be cost-dependent and performance-dependent.

Due to its agility and mobility, the unmanned aerial vehicle (UAV) is a promising technology to provide high-quality mobile services (e.g., fast Internet access, edge computing, and local caching) to ground users. The Internet service providers (ISPs) directly or commission the third-party UAV firms to provide UAV-provided services (UPS) to improve and make up for the shortage of their current mobile services for additional profit. Yet the UAV has limited energy storage and needs to fly to serve users locally, requiring an optimal energy allocation for balancing both hovering time and service capacity. For profit-maximizing purpose, when hovering in a hotspot, how the UAV should dynamically price its capacity-limited UPS according to randomly arriving users with private service valuations is another question. This paper first introduce a threshold-based assignment policy to show how the UAV decides to serve the users or not under complete information that a user’s service valuation can be observed when he arrives. Following this benchmark, we analyze the UAV’s optimal pricing under incomplete information about the users’ random arrival and private service valuations. It is proved that the UAV should ask for a higher price if the leftover hovering time is longer or its service capacity is smaller, and its expected profit approaches to that under complete user information if the hovering time is sufficiently large. Then, based on the optimal pricing, the energy allocation to hovering time and service capacity in a hotspot is optimized. We show that as the hotspot’s user occurrence rate increases, a shorter hovering time or a larger service capacity should be allocated. Finally, when a UAV faces multiple hotspot candidates with different user occurrence rates and flying distances, we prove that it is optimal to deploy the UAV to serve a single hotspot, by taking the optimal pricing and energy allocation of each hotspot into consideration. With multiple UAVs, however, this result can be reversed with UAVs’ forking deployment to different hotspots, especially when hotspots are more symmetric or the UAV number is large. Perhaps surprisingly, more UAVs may be deployed to the second-best hotspot rather than the first-best one.

Unmanned Aerial Vehicle (UAV) have emerged as a promising technique to rapidly provide wireless services to a group of mobile users simultaneously. The article aims to address a challenging issue that each user is selfish and may misreport his location or preference for changing the optimal UAV location to be close to himself. Using algorithmic game theory, we study how to determine the final location of a UAV in the 3D space, by ensuring all selfish users' truthfulness in reporting their locations for learning purpose. To minimize the social service cost in this UAV placement game, we design strategyproof mechanisms with the approximation ratios, when comparing to the social optimum. We also study the obnoxious UAV placement game to maximally keep their social utility, where each incumbent user may misreport his location to keep the UAV away from him. Moreover, we present the dual-preference UAV placement game by considering the coexistence of the two groups of users above, where users can misreport both their locations and preference types (favorable or obnoxious) towards the UAV. Finally, we extend the three games above to include multiple UAVs and design strategyproof mechanisms with provable approximation ratios.

In this paper, we analyse how a peer-to-peer sharing platform should price its service to maximize profit, when user participation increases the value of the service to others by causing positive externalities. Modelling the service as an excludable public good, we propose a bounded utility model to capture many infrastructure sharing applications with bounded network value, in which complete coverage generates finite user valuation (e.g., WiFi or hotspot). Unbounded utility models are used to capture the large-scale user interactions in social media, where the network value follows Metcalfe's or Zipf's law. For these utility models, we analyze the optimal pricing schemes in the case of heterogeneous users under complete and incomplete information of users' service valuations. We propose the concept of `price of information' (PoI) to characterize the profit loss due to lack of information, and present asymptotic PoI bounds for different utility models. We also show that the difficult-to-implement differentiated pricing scheme, which is optimal under incomplete user information, can be replaced by a simple uniform price scheme that is asymptotic optimal. Finally, we extend our pricing schemes to a two-sided market by including a new group of `pure' service users who do not contribute to the public good, and show that the platform may charge zero price to the original group of users in order to attract this pure user group.

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV's mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV's sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV's transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks.

In the existing cellular networks, it remains a challenging problem to communicate with and control an unmanned aerial vehicle (UAV) swarm with both high reliability and low latency. Due to the UAV swarm's high working altitude and strong ground-to-air channels, it is generally exposed to multiple ground base stations (GBSs), while the GBSs that are serving ground users (occupied GBSs) can generate strong interference to the UAV swarm. To tackle this issue, we propose a novel two-phase transmission protocol by exploiting cellular plus device-to-device (D2D) communication for the UAV swarm. In Phase I, one swarm head is chosen for ground-to-air channel estimation, and all the GBSs that are not serving ground users (available GBSs) transmit a common control message to the UAV swarm simultaneously, using the same cellular frequency band. Both the swarm head and other swarm members can utilize the high power gain from multiple available GBSs' transmission, to combat the strong interference from occupied GBSs, while some UAVs may fail to decode the message due to uncorrelated ground-to-air channels. In Phase II, all the UAVs that have decoded the message in Phase I further relay it to the other UAVs in the swarm via D2D communication, by exploiting the less interfered D2D frequency band and the proximity among UAVs. In this paper, we aim to characterize the reliability performance of the above two-phase transmission protocol, i.e., the expected percentage of UAVs in the swarm that can decode the common control message, which is a non-trivial problem due to the complex system setup and the intricate coupling between the two transmission phases. Nevertheless, we manage to obtain an approximated expression of the reliability performance of interest, under reasonable assumptions and with the aid of the Pearson distributions. Numerical results validate the accuracy of our analytical results and show the effectiveness of our proposed protocol over other benchmark protocols. We also study the effect of key system parameters on the reliability performance, to reveal useful insights on the practical design of cellular-connected UAV swarm communication.

Provided with mobile edge computing (MEC) services, wireless devices (WDs) no longer have to experience long latency in running their desired programs locally, but can pay to offload computation tasks to the edge server. Given its limited storage space, it is important for the edge server at the base station (BS) to determine which service programs to cache by meeting and guiding WDs' offloading decisions. In this article, we propose an MEC service pricing scheme to coordinate with the service caching decisions and control WDs' task offloading behavior in a cellular network. We propose a two-stage dynamic game of incomplete information to model and analyze the two-stage interaction between the BS and multiple associated WDs. Specifically, in Stage I, the BS determines the MEC service caching and announces the service program prices to the WDs, with the objective to maximize its expected profit under both storage and computation resource constraints. In Stage II, given the prices of different service programs, each WD selfishly decides its offloading decision to minimize individual service delay and cost, without knowing the other WDs' desired program types or local execution delays. Despite the lack of WD's information and the coupling of all the WDs' offloading decisions, we derive the optimal threshold-based offloading policy that can be easily adopted by the WDs in Stage II at the Bayesian equilibrium. In particular, a WD is more likely to offload when there are fewer WDs competing for the edge server's computation resource, or when it perceives a good channel condition or low MEC service price. Then, by predicting the WDs' offloading equilibrium, we jointly optimize the BS' pricing and service caching in Stage I via a low-complexity algorithm. In particular, we first study the differentiated pricing scheme and prove that the same price should be charged to the cached programs of the same workload. Motivated by this analysis, we further propose a low-complexity uniform pricing heuristics.

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing, fast Internet connection, and local caching) to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing demands locally. It is necessary for different UAVs to cooperate with each other for servicing many users, and how to determine their cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question. This paper is the first to design and analyze cooperative path-planning algorithms of a UAV swarm for optimally servicing many spatial locations with dynamic user arrivals and waiting deadlines in the time horizon. For each UAV, it needs to decide whether to wait at the current location or chase a newly released demand in another location, under upper coordination with the other UAVs in the swarm. For each UAV's routing problem even without coordinating with the rest UAVs, it follows dynamic programming structure and is difficult to solve directly given many user demands. We manage to simplify and propose an optimal algorithm of fast computation time (only polynomial with respect to both the numbers of user locations and user demands) for returning the UAV's optimal path-planning. When a large number |K| of UAVs are coordinating, the dynamic programming simplification becomes intractable. Alternatively, we present an iterative cooperation algorithm with approximation ratio 1 - (1 - 1/|K| )|K|in the worst case, which is proved to obviously outperform the traditional idea of partitioning UAVs to serve different user/location clusters separately. Finally, we conduct simulation experiments to show that our algorithm's average performance is close to the optimum.

Mobile users in future wireless networks face limited wireless resources such as data plan, computation capacity and energy storage. Given that some of these users may not be utilizing fully their wireless resources, device-to-device (D2D) resource sharing is a promising approach to exploit users' diversity in resource use and for pooling their resources locally. In this paper, we propose a novel two-sided D2D trading market model that enables a large number of locally connected users to trade resources. Traditional resource allocation solutions are mostly centralized without considering users' local D2D connectivity constraints, becoming unscalable for large-scale trading. In addition, there may be market failure since selfish users will not truthfully report their actual valuations and quantities for buying or selling resources. To address these two key challenges, we first investigate the distributed resource allocation problem with D2D assignment constraints. Based on the greedy idea of maximum weighted matching, we propose a fast algorithm to achieve near-optimal average allocative efficiency. Then, we combine it with a new pricing mechanism that adjusts the final trading prices for buying and selling resources in a way that buyers and sellers are incentivized to truthfully report their valuations and available resource quantities. Unlike traditional double auctions with a central controller, this pricing mechanism is fully distributed in the sense that the final trading prices between each matched pair of users only depend on their own declarations and hence can be calculated locally. Finally, we analyze the repeated execution of the proposed D2D trading mechanism in multiple rounds and determine the best trading frequency.

With the explosion of the Internet-of-Things (IoT) technology, numerous IoT terminal devices generate tremendous traffic. Device-to-device (D2D)-enabled caching can greatly relieve the pressure of massive resource-limited terminal devices in the IoT network. This article proposes a novel distributed framework, termed crowdcaching, which motivates selective file caching and cooperative file sharing among terminal devices via short-range (e.g., D2D) communications. After modeling file preference distributions and local connectivities, we optimize the caching strategy for any given coalition of cooperative users to minimize their total delay cost. In particular, if users have homogeneous file preferences and local connectivities, we can mathematically define a popularity index, according to which files are chosen to be cached in user devices. In a more general setting where users have heterogeneous file preferences and local connectivities, we propose a greedy algorithm of low complexity to determine the optimal caching strategy. Based on the cooperative caching strategy for any given coalition, we further investigate users' incentive to form crowdcaching coalitions through the coalitional game theory and propose a distributed algorithm to yield a stable coalition formulation. The simulation results show that crowdcaching can effectively reduce the average delay cost of users by as much as 45.64%.

Online content platforms are concerned about the freshness of their content updates to their end customers, and increasingly more platforms now invite and pay the crowd to sample real-time information (e.g., traffic observations and sensor data) to help reduce their ages of information (AoI). How much crowdsourced data to sample and buy over time is a critical question for a platform's AoI management, requiring a good balance between its AoI and the incurred sampling cost. This question becomes more interesting by considering the stage after sampling, where multiple platforms coexist in sharing the content delivery network of limited bandwidth, and one platform's update may jam or preempt the others' under negative network externalities. When these selfish platforms know each other's sampling cost, we formulate their competition as a non-cooperative game and show they want to over-sample to reduce their own AoIs, causing the price of anarchy (PoA) to be infinity. To remedy this huge efficiency loss, we propose a trigger mechanism of non-monetary punishment in a repeated game to enforce the platforms' cooperation to approach the social optimum. We also study the more challenging scenario of incomplete information that some new platform hides its private sampling cost information from the other incumbent platforms in the Bayesian game. Perhaps surprisingly, we show that even the platform with more information may get hurt. We successfully redesign the trigger-and-punishment mechanism to negate the platform's information advantage and ensure no cheating. Our extensive simulations show that the mechanisms can remedy the huge efficiency loss due to platform competition, and the performance improves as we have more incumbent platforms with known cost information.

Cooperative jamming is a widely used approach for improving the security of wireless networks. In this approach, a friendly jammer sends jamming signals to disrupt the reception of the eavesdropper, which inevitably interferes with the legitimate receiver. In this paper, we propose a novel approach, namely, information jamming, to exceed the secrecy rate achieved by the traditional cooperative jamming approach in a multiple-input single-output (MISO) wiretap channel. We propose that multiple multi-antenna information jammers (iJammers) transmit the source signals of the legitimate transmitter rather than independent noise signals for simultaneously enhancing the signal strength at the legitimate receiver and canceling the received signal at the eavesdropper. Specifically, we aim at maximizing the achievable secrecy rate by jointly optimizing the beamforming vectors at Alice and iJammers, subject to the individual transmit power constraints. We first propose a semi-definite relaxation based approach to solve the original non-convex problem optimally. For ease of implementation, we then provide a suboptimal distributed information beamforming scheme, whose optimal solution is obtained in closed-form. Finally, we extend our study to the imperfect channel state information case. Simulation results show that our proposed information jamming approach significantly outperforms the traditional cooperative jamming approach in terms of achievable secrecy rate.

Age of information is a new network performance metric that captures the freshness of information at end-users. This paper studies the age of information from a scheduling perspective. To that end, we consider a wireless broadcast network where a base-station (BS) is updating many users on random information arrivals under a transmission capacity constraint. For the offline case when the arrival statistics are known to the BS, we develop a structural MDP scheduling algorithm and an index scheduling algorithm, leveraging Markov decision process (MDP) techniques and the Whittle's methodology for restless bandits. By exploring optimal structural results, we not only reduce the computational complexity of the MDP-based algorithm, but also simplify deriving a closed form of the Whittle index. Moreover, for the online case, we develop an MDP-based online scheduling algorithm and an index-based online scheduling algorithm. Both the structural MDP scheduling algorithm and the MDP-based online scheduling algorithm asymptotically minimize the average age, while the index scheduling algorithm minimizes the average age when the information arrival rates for all users are the same. Finally, the algorithms are validated via extensive numerical studies.

In today's growing data market, wireless service providers (WSPs) compete severely to attract users by announcing innovative data plans. Two of the most popular innovative data plans are rollover and shared data plans, where the former plan allows a user to roll his unused data quota to next month and the latter plan allows users in a family to share unused data. As a pioneer to provide such data plans, a WSP faces immediate revenue loss from existing users who pay less overage charges due to less data over-usage, but his market share increases gradually by attracting new users and those under the other WSPs. In some countries, WSPs have asymmetric timing for providing such innovative data plans, while some other markets' WSPs have symmetric timing or no planning. This raises the question of why and when the competitive WSPs should offer the new data plans. This paper provides game theoretic modelling and analysis of the WSPs' timing of offering innovative data plans, by considering new user arrival and dynamic user churn between WSPs. Our equilibrium analysis shows that the WSP with small market share prefers to announce the innovative data plan first to attract more users, while the WSP with large market share prefers to announce later to avoid the immediate revenue loss. In a market with many new users, WSPs with similar market shares will offer the data plans simultaneously, but these WSPs facing few new users may not offer any new plan. Perhaps surprisingly, WSPs' profits can decrease with new user number and they may not benefit from the option of innovative data plans. Finally, unlike rollover data plan, we show that the timing of shared data plan further depends on the composition of users.

This paper studies a new and challenging wireless surveillance problem where a legitimate monitor attempts to eavesdrop two suspicious communication links simultaneously. To facilitate concurrent eavesdropping, our multi-antenna legitimate monitor employs a proactive eavesdropping via jamming approach, by selectively jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the achievable eavesdropping rate region for the minimum-mean-squared-error (MMSE) receiver case, by optimizing the legitimate monitor's jamming transmit covariance matrix subject to its power budget. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping rate region is essentially the intersection of the achievable rate region for the two suspicious links and that for the two eavesdropping links. The former region can be purposely altered by the monitor's jamming transmit covariance matrix, whereas the latter region is fixed when the MMSE receiver is employed. Therefore, we first analytically characterize the achievable rate region for the two suspicious links via optimizing the jamming transmit covariance matrix and then obtain the achievable eavesdropping rate region for the MMSE receiver case. In addition, we also consider the MMSE with successive interference cancellation (MMSE-SIC) receiver case and characterize the corresponding achievable eavesdropping rate region by jointly optimizing the time-sharing factor between different decoding orders. Furthermore, extensions to the imperfect channel state information case and the more than two suspicious links scenario are also examined. Finally, numerical results are provided to corroborate our analysis and evaluate the eavesdropping performance.

This paper studies a new and challenging wireless surveillance problem where a legitimate monitor (e.g., the National Security Agency in the USA) attempts to eavesdrop more than one suspicious communication links simultaneously to maximally protect public security. To facilitate concurrent eavesdropping, our multi-antenna monitor employs a proactive eavesdropping via jamming approach, by purposely jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the monitor's achievable eavesdropping signal-to-interference-plus-noise ratio (SINR) region of two suspicious links, by optimizing the legitimate monitor's transmit covariance matrix for jamming the two suspicious receivers. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping SINR region is essentially the intersection of the achievable region for the two suspicious links and that for the two eavesdropping links, and the former region can be purposely altered by the monitor's jamming transmit covariance matrix subject to its power budget. As both suspicious links' SINRs are affected by monitor's jamming covariance matrix, we first analyze the achievable region bounds for both suspicious links and then characterize the achievable eavesdropping SINR region. Finally, numerical results are provided to corroborate our analysis.

Mobile social network applications constitute an important platform for traffic information sharing, helping users collect and share sensor information about the driving conditions they experience on the traveled path in real time. In this paper we analyse the simple but fundamental model of a platform choosing between two paths: one with known deterministic travel cost and the other that alternates over time between a low and a high random cost states, where the low and the high cost states are only partially observable and perform respectively better and worse on average than the fixed cost path. The more users are routed over the stochastic path, the better the platform can infer its actual state and use it efficiently.At the Nash equilibrium, if asked to take the riskier path, in many cases selfish users (that are allowed to have access to the information collected by the platform) will myopically disregard the optimal path suggestions of the platform, leading to a suboptimal system without enough exploration on the stochastic path. We prove the interesting result that if the past collected information is hidden from users, the system becomes incentive compatible and even `sophisticated' users (in the sense that they have full capability to reverse-engineer the platform's recommendation and derive the path state distribution conditional on the recommendation) prefer to follow the platform's recommendations. In a more practical setting where the platform implements a model-free Q-learning algorithm to minimise the social travel cost, our analysis suggests that increasing the accuracy of the learning algorithm increases the range of system parameters for which sophisticated users follow the recommendations of the platform, becoming in the limit fully incentive compatible. Finally, we extend the two-path model to include more stochastic paths, and show that incentive compatibility holds under our information restriction mechanism.

Due to its agility and mobility, the unmanned aerial vehicle (UAV) is a promising technology to provide high-quality mobile services (e.g., fast Internet access, edge computing, and local caching) to ground users. Major Internet Service Providers (ISPs) want to enable UAV-provided services (UPS) to improve and enrich the current mobile services for additional profit. This profit-maximization problem is not easy as the UAV has limited energy storage and needs to fly closely to serve users, requiring an optimal energy allocation for balancing both hovering time and service capacity. When hovering in a hotspot, how the UAV should dynamically price its capacity-limited UPS according to randomly arriving users with private service valuations is another question. We prove that the UAV should ask for a higher price if the leftover hovering time is longer or its service capacity is smaller, and its expected profit approaches to that under complete user information if the hovering time is sufficiently large. As the hotspot's user occurrence rate increases, a shorter hovering time or a larger service capacity should be allocated. Finally, when the UAV faces multiple hotspot candidates with different user occurrence rates and flying distances, we prove that it is optimal to deploy the UAV to serve a single hotspot. With multiple UAVs, however, this result can be reversed with UAVs' forking deployment to different hotspots.

Fueled by the rapid development of communication networks and sensors in portable devices, today many mobile users are invited by content providers to sense and send back real-time useful information (e.g., traffic observations and sensor data) to keep the freshness of the providers' content updates. However, due to the sampling cost in sensing and transmission, an individual may not have the incentive to contribute the realtime information to help a content provider reduce the age of information (AoI). Accordingly, we propose dynamic pricing for the provider to offer age-dependent monetary returns and encourage users to sample information at different rates over time. This dynamic pricing design problem needs to balance the monetary payments to users and the AoI evolution over time, and is challenging to solve especially under the incomplete information about users' arrivals and their private sampling costs. For analysis tractability, we linearize the nonlinear AoI evolution in the constrained dynamic programming problem, by approximating the dynamic AoI reduction as a time-average term and solving the approximate dynamic pricing in closed-form. Then, we estimate this approximate term based on Brouwer's fixed-point theorem. Finally, we provide the steady-state analysis of the optimized approximate dynamic pricing scheme for an infinite time horizon, and show that the pricing scheme can be further simplified to an ε-optimal version without recursive computing over time.

Online content platforms are concerned about the freshness of their content updates to their end customers, and increasingly more platforms now invite and pay the crowd to share real-time information (e.g., news and sensor data) to help reduce their ages of information (AoI). How much crowdsourced data to sample and buy over time is a critical question for a platform's AoI management, requiring a good balance between its AoI and the incurred sampling cost. This question becomes more interesting by considering the stage after sampling, where two platforms coexist in sharing the content delivery network of limited bandwidth, and one platform's update may jam or preempt the other's under negative network externalities. When the two selfish platforms know each other's sampling costs, we formulate their interaction as a non-cooperative game and show both want to over-sample to reduce their own AoI, causing the price of anarchy (PoA) to be infinity. To remedy this huge efficiency loss, we propose a non-monetary trigger mechanism of punishment in a repeated game to enforce the platforms' cooperation to achieve the social optimum. We also study the more challenging incomplete information scenario that platform 1 knows more information about sampling cost than platform 2 by hiding its sampling cost information in the Bayesian game. Perhaps surprisingly, we show that even platform 1 may get hurt by knowing more information. We successfully redesign the trigger-and-punishment mechanism to negate platform 1's information advantage and ensure no cheating. As compared to the social optimum, extensive simulations show that the mechanisms can remedy the huge efficiency loss due to platform competition in different information scenarios.

Unlike passive eavesdropping, proactive eavesdropping is recently proposed to use jamming to moderate a suspicious link's communication rate for facilitating simultaneous eavesdropping. This paper advances the proactive eavesdropping research by considering a practical half-duplex mode for the legitimate monitor (e.g., a government agency) and dealing with the challenging case that the suspicious link opportunistically communicates over parallel fading channels. To increase eavesdropping success probability, we propose cognitive jamming for the monitor to change the suspicious link's long-term belief on the parallel channels' distributions and thereby induce it to transmit more likely over a smaller subset of unjammed channels with a lower transmission rate. As the half-duplex monitor cannot eavesdrop the channel that it is simultaneously jamming to, our jamming design should also control the probability of such “own goal” that occurs when the suspicious link chooses one of the jammed (uneavesdroppable) channels to transmit. We formulate the optimal jamming design problem as a mixed integer nonlinear programming (MINLP) and show that it is non-convex. Nevertheless, we prove that the monitor should optimally use the maximum jamming power if it decides to jam, for maximally reducing the suspicious link's communication rate and driving the suspicious link out of the jammed channels. Then we manage to simplify the MINLP to integer programming and reveal a fundamental trade-off in deciding the number of jammed channels: jamming more channels helps reduce the suspicious link's communication rate for overhearing more clearly but increases own goal probability and thus decreases eavesdropping success probability. Finally, we extend our study to the two-way suspicious communication scenario and show that there is another interesting trade-off in deciding the common jammed channels for balancing bidirectional eavesdropping performances. Numerical results show that our optimized jamming-assisted eavesdropping schemes greatly increase eavesdropping success probability as compared with the conventional passive eavesdropping.

Unmanned Aerial Vehicle (UAV) networks have emerged as a promising technique to rapidly provide wireless coverage to a geographical area, where a flying UAV can be fast deployed to serve as cell site. Existing work on UAV-enabled wireless networks overlook the fast UAV deployment for wireless coverage, and such deployment problems have only been studied recently in sensor networks. Unlike sensors, UAVs should be deployed to the air and they are generally different in flying speed, operating altitude and wireless coverage radius. By considering such UAV heterogeneity to cover the whole target area, this paper studies two fast UAV deployment problems: one is to minimize the maximum deployment delay among all UAVs (min-max) for fairness consideration, and the other is to minimize the total deployment delay (min-sum) for efficiency consideration. We prove both min-max and min-sum problems are NP-complete in general. When dispatching UAVs from the same location, we present an optimal algorithm of low computational complexity O(n2) for the min-max problem. When UAVs are dispatched from different locations, we propose to preserve their location order during deployment and successfully design a fully polynomial time approximation scheme (FPTAS) of computation complexity O(n2log 1/ϵ) to arbitrarily approach the global optimum with relative error ϵ. The min-sum problem is more challenging. When UAVs are dispatched from the same initial location, we present an approximation algorithm of linear time. As for the general case, we further reformulate it as a dynamic program and propose a pseudo polynomial-time algorithm to solve it optimally.

Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV's agility and mobility, a key question is how to adapt UAV deployment to the best cater to instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users' instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users' exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV's displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. The extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large.

Unmanned aerial vehicle (UAV) can be used as an aerial base station to provide rapid wireless connectivity to ground users. Given UAV's agility and mobility, a key problem is how to adapt UAV deployment to best cater to the instantaneous wireless traffic in a territory. In this paper, we propose a traffic-aware adaptive UAV deployment scheme in a UAV-aided communication network, where the UAV initiated at the cell center adapts its displacement direction and distance to the spatial randomness of the Poisson distributed mobile users within its target cell. In each realization, the UAV chooses its displacement direction based on a simple majority rule, i.e., to fly to the sector that has the greatest number of users. To balance the service for the users in different sectors, we further optimize the UAV's displacement distance in the chosen sector to maximize the average throughput. We show that the optimal displacement distance under the proposed scheme decreases with the user density. Extensive simulations illustrate that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, where the performance gain is especially significant for small user density.

Cooperative jamming approach has been commonly used in the field of physical layer security to achieve a good secrecy rate of wireless networks. In this approach, the Gaussian noise signal sent by a friendly jammer can disrupt the reception of the eavesdropper, however, it may also interfere with the legitimate receiver. In this paper, we propose a novel approach, namely, information jamming, to go beyond the secrecy rate achieved by the traditional cooperative jamming approach in a multiple-input single-output (MISO) wiretap channel. We propose that a multi-antenna friendly jammer transmits the source message rather than Gaussian noise signal for simultaneously enhancing the signal strength at the legitimate receiver and canceling the received signal at the eavesdropper. In particular, we aim to maximize the achievable secrecy rate by optimizing the information jammer's beamforming vector. Though the optimization problem is non-convex, we successfully solve it optimally via a two-stage based numerical approach. To gain more useful analytical results, we also propose two suboptimal jamming schemes with low computation, by targeting at improving the legitimate reception and avoiding the eavesdropping, respectively. Finally, numerical results show that the proposed information jamming approach significantly helps go beyond the secrecy rate of the existing cooperative jamming approach, especially when the transmit power of the jammer is large enough.

The following topics are dealt with: telecommunication scheduling; optimisation; cellular radio; wireless channels; probability; radio networks; mobile radio; cache storage; telecommunication traffic; and stochastic processes.

Mobile crowdsensing (MCS) is a novel and appealing sensing paradigm that leverages the diverse embedded sensors of massive mobile devices to collect different kinds of data. One of the key challenges in MCS is to efficiently schedule mobile device users to perform different sensing tasks. Prior effort to this problem mainly focused on the interaction between the task-layer and the user-layer, without considering the similar data requirements of tasks and the heterogeneous sensing capabilities of users. In this work, we introduce a new data-layer between tasks and users, and propose a three-layer data-centric MCS framework, which enables different tasks to reveal their common data requirements and hence reuse the common data items. We focus on studying the joint task selection and user scheduling problem under this new framework, aiming at maximizing the social welfare. Specifically, we first analyze theoretical performance gain due to data reuse in the ideal scenario with complete information. We then consider the practical scenario with private information of both tasks and users, and propose a two-sided randomized auction mechanism, which is computationally efficient, individually rational, incentive compatible (truthful) in expectation, and close-to-optimal. We further show that the proposed randomized auction may not be budget balanced, and hence introduce a reserve price into the auction to achieve the desired budget balance at the cost of certain welfare loss. Simulation results show that with data reuse, the social welfare achieved in the proposed randomized auction can be increased from 270 up to 4,500 percent, comparing with those without data reuse.

Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of the rich embedded sensors of mobile user devices. However, the traditional server-client MCS architecture often suffers from the high operational cost on the centralized server (e.g., for storing and processing massive data), hence the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce the server's cost by leveraging the user devices' computation and storage resources. In this work, we propose a novel P2P-based MCS architecture, where the sensing data is saved and processed in user devices locally and shared among users in a P2P manner. To provide necessary incentives for users in such a system, we propose a quality-aware data sharing market, where the users who sense data can sell data to others who request data but not want to sense the data by themselves. We analyze the user behavior dynamics from the game-theoretic perspective, and characterize the existence and uniqueness of the game equilibrium. We further propose best response iterative algorithms to reach the equilibrium with provable convergence. Our simulations show that the P2P data sharing can greatly improve the social welfare, especially in the model with a high transmission cost and a low trading price.

A smartphone user's personal hotspot (pH) allows him to share a cellular connection to another (e.g., a traveler) in the vicinity, but such sharing consumes the limited data quota in his two-part tariff plan and may lead to an overage charge. This paper studies how to motivate such pH-enabled data-plan sharing between local users and travelers in the ever-growing roaming markets, and proposes pricing incentive for a data-plan buyer to reward surrounding pH sellers (if any). The pricing scheme practically takes into account the information uncertainty at the traveler side, including the random mobility and the sharing cost distribution of selfish local users who potentially share their pHs. Though the pricing optimization problem is non-convex, we show that there always exists a unique optimal price to a tradeoff between the successful sharing opportunity and the sharing price. We further generalize the optimal pricing to the case of heterogeneous selling pHs who have diverse data usage behaviors in the sharing cost distributions, and we show such diversity may or may not benefit the traveler. Lacking selfish pHs' information, the traveler's expected cost is higher than that under the complete information, but the gap diminishes as the pHs' spatial density increases. Finally, we analyze the challenging scenario that multiple travelers overlap for demanding data-plan sharing, by resorting to a near-optimal pricing scheme. We show that a traveler suffers as the travelers' spatial density increases.

In this paper, we study a wireless powered communication network (WPCN) in a generalN-user interference channel (IFC), where a hybrid access-point (H-AP) in each cell supports its corresponding user. In this multi-cell environment, the H-AP first sends the energy signal to charge users in the downlink (DL) phase, while in the subsequent uplink (UL) phase, each user transmits its information signal to the corresponding H-AP utilizing the previously harvested energy. For the WPCN in this IFC scenario, cross-link interference occurs due to asynchronous time allocation of the DL and the UL amongNcells which significantly affects the overall performance. To handle the interference issue efficiently, we jointly optimize the DL and UL time allocation of each cell as well as the transmit power allocation at the H-APs and the users so that the weighted sum-rate of UL information transmission is maximized. To tackle non-convexity of the weighted sum-rate maximization problem, we propose an iterative algorithm where the time allocation and the transmit power are updated based on the weighted minimum mean square error criteria and the gradient projection method, respectively. Furthermore, we consider two simple protocols where the DL time allocation of each cell is synchronized and present resource allocation method, respectively. In simulation results, we verify that the proposed algorithm for the asynchronous protocol outperforms conventional schemes.

With recent developments in wireless communication technologies, malicious users can use them to commit crimes or launch terror attacks, thus imposing new threats on public security. To quickly respond to these attacks, authorized parities need to intervene in the malicious communication links over the air. This paper investigates the emerging wireless communication intervention problem at the physical layer. Unlike prior studies using jamming to disrupt or disable the targeted wireless communications, we propose a new physical-layer spoofing approach to change their communicated information. Consider an abstract three-node model over additive white Gaussian noise channels, in which a legitimate spoofer aims to spoof a malicious communication link from a malicious transmitter to a malicious receiver, such that the received message at the receiver is changed from the transmitter's originally sent message to the one desired by the spoofer. We propose a new symbol-level spoofing scheme, where the spoofer designs the spoofing signal by exploiting the symbol-level relationship between each original constellation point of the transmitter and the desirable one of the spoofer. In particular, the spoofer aims to minimize the average spoofing-symbol-error-rate (SSER), which is defined as the average probability that the symbols decoded by the malicious receiver fail to be changed or spoofed, by designing its spoofing signals over symbols subject to the average transmit power constraint. By considering two cases when the malicious transmitter employs the widely-used binary phase-shift keying and quadrature phase-shift keying modulations, we obtain the respective optimal solutions to the two average SSER minimization problems. Numerical results show that the symbol-level spoofing scheme with optimized transmission achieves a much lower average SSER, as compared with other benchmark schemes.

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the others waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called Scheduled WPT (SWPT), aiming to minimize data packet loss from a network of wireless powered sensor nodes by jointly considering the sensor nodes' energy consumption and data queue state information. The scheduling problem is formulated by a MDP model, assuming that the complete states of each sensor node are well known by the base station. This presents the best effort performance of the scheduling that can be collected in a wireless powered sensor network. The simulation results show that, in terms of network throughput and packet loss rate, the proposed scheduling model significantly improves the network performance.

A smartphone user's personal hotspot (pH) allows one to share cellular connection to another device nearby, but such sharing consumes the limited data quota in his or her two-part tariff plan and may lead to overage charge. This paper studies how to motivate such secondary data sharing via pHs for roaming markets, and proposes pricing incentive for a secondary data buyer (typically, a traveler) to opportunistically demand and reward pHs (if any) in the vicinity to reach a win-win situation. The pricing scheme practically takes into account the information uncertainty at the traveler side, including the random mobility and the sharing cost distribution of selfish local users who share pHs. Though the pricing optimization is non-convex problem, we show that there always exists a unique optimal price to tradeoff between the sharing opportunity and the sharing price, and can further extend the optimal pricing to the case of heterogeneous selling users/pHs who have diverse data usage behaviors. Lacking selfish pHs' information and cooperation, the traveler's expected cost is higher than that under the complete information, but the gap diminishes as the selfish pHs' spatial density increases. The traveler may or may not benefit from the diversity of pHs' data usage behaviors. Perhaps surprisingly, when the pHs' data usages are very diverse, the traveler's expected cost does not change with such diversity.

This paper studies the wireless surveillance of a hybrid automatic repeat request (HARQ) based suspicious communication link over Rayleigh fading channels. We propose a proactive eavesdropping approach, where a half-duplex monitor can opportunistically jam the suspicious link to exploit its potential retransmissions for overhearing more efficiently. In particular, we consider that the suspicious link uses at most two HARQ rounds for transmitting the same data packet, and we focus on two cases without and with HARQ combining at the monitor receiver. In both cases, we aim to maximize the successful eavesdropping probability at the monitor, by adaptively allocating the jamming power in the first HARQ round according to fading channel conditions, subject to an average jamming power constraint. For both cases, we show that the optimal jamming power allocation follows a threshold-based policy, and the monitor jams with constant power when the eavesdropping channel gain is less than the threshold. Numerical results show that the proposed proactive eavesdropping scheme achieves higher successful eavesdropping probability than the conventional passive eavesdropping, and HARQ combining can help further improve the eavesdropping performance.

Unmanned Aerial Vehicle (UAV) networks have emerged as a promising technique to rapidly provide wireless coverage to a geographical area out of the reach or capacity of existing core networks, where a flying UAV can be fast deployed to serve as a base station. Existing work on UAV overlook the emergency deployment problem and only the recent research on sensor networks study the deployment problems the assumed in one-dimensional (1D) ground. However, UAVs should be deployed to the air (beyond one-dimension) by considering their different flying speeds during deployment and deployment altitudes, this paper studies this novel emergency UAV deployment to minimize the UAV deployment delay till covering the whole target area. When a number n of diverse UAVs are dispatched from the same location (e.g., the closest UAV station) to the target area, we present an optimal deployment algorithm by balancing UAVs' diverse flying speeds and coverage radii, and this algorithm has low computation complexity O(n2). When UAVs are generally dispatched from different locations, we first prove that the emergency UAV deployment problem is NP-complete. By preserving UAVs' location order, we then successfully design a fully polynomial time approximation scheme (FPTAS) of computation complexity O(n2log 1/ε) to arbitrarily approach the global optimum.

Age of information is a newly proposed metric that captures delay from an application layer perspective. The age measures the amount of time that elapsed from the moment the mostly recently received update was generated until the present time. In this paper, we study an age minimization problem over a wireless broadcast network with many users, where only one user can be served at a time. We formulate a Markov decision process (MDP) to find dynamic transmission scheduling schemes, with the purpose of minimizing the long-run average age. While showing that an optimal scheduling algorithm for the MDP is a simple stationary switch-type, we propose a sequence of finite-state approximations for our infinite-state MDP and prove its convergence. We then propose both optimal off-line and online scheduling algorithms for the finite-approximate MDPs, depending on knowledge of time-varying arrivals.

Recent developments of smart devices and mobile applications have significantly increased the level at which human users interact with mobile systems. As a result, human activities, usage behavior, and perceived experience of users weigh increasingly on the performance of mobile networks, which has created new challenges for system operation in various aspects, such as increasing uncertainty, selfishness in operations, and complicated performance evaluation. On the other hand, the strong engagement of a large population of human users makes it possible to take advantage of the unique features of human behavior and to leverage the computing powers owned by users. Due to these emerging features of mobile networks, their design and evaluation require a hybrid view of human factor and information technology, and a paradigm shift is required for designing a new human-in-the-loop architecture by actively learning, adapting, and steering user behavior, so as to exploit the human factor in future ubiquitous mobile systems, and to greatly enhance system efficiency and provide superior quality-of-experience to users. The goal of this survey is to summarize recent results that focus on understanding and exploiting the human factor in mobile networks. In the tutorial, we summarize and discuss novelties of these formulations, adopted methodologies, and interesting results. We also point out some future research directions.

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

Today, mobile users are intensively interconnected thanks to the emerging mobile social networks, where they share location-based information with each other when traveling on different routes and visit different areas of the city. In our model, the information collected is aggregated over all users' trips and made publicly available as a public good. Due to information overlap, the total useful content amount increases with the diversity in path choices made by the users, and it is crucial to motivate selfish users to choose different paths, despite the potentially higher costs associated with their trips. In this paper, we combine the benefits from social information sharing with the fundamental routing problem where a unit mass of non-atomic selfish users decides their trips in a non-cooperative game by choosing between a high-cost and a low-cost path. To remedy the inefficient low-content equilibrium where all users choose to explore a single path (the low-cost path), we propose and analyze two new incentive mechanisms that can be used by the social network application, one based on side payments and the other on restricting access to content for users that choose the low cost path. Under asymmetric information about user types (their valuations for content quality), both mechanisms efficiently penalize the participants that use the low-cost path and reward the participants that take the high-cost path. They lead to greater path diversity and hence to more total available content at the social cost of reduced user participation or restricted content to part of the users. We show that user heterogeneity can have opposite effects on social efficiency depending on the mechanism used. We also obtain interesting price of anarchy results that show some fundamental tradeoffs between achieving path diversity and maintaining greater user participation, motivating a combined mechanism to further increase the social welfare. Our model extends classical dynamic routing in the case of externalities caused from traffic on different paths of the network.

Local caching is an effective scheme for leveraging the memory of the mobile terminal (MT) and short range communications to save the bandwidth usage and reduce the download delay in the cellular communication system. In particular, the MTs first cache in their local memories in off-peak hours and then exchange the requested files with each other in the vicinity during peak hours. However, prior works largely overlook MTs' heterogeneity in file preferences and their selfish behaviors. In this paper, we practically categorize the MTs into different interest groups according to the MTs' preferences. Each group of MTs aims to increase the probability of successful file discovery from the neighboring MTs (from the same or different groups). Hence, we define the groups' utilities as the probability of successfully discovering the file in the neighboring MTs, which should be maximized by deciding the caching strategies of different groups. By modeling MTs' mobilities as homogeneous Poisson point processes, we analytically characterize MTs' utilities in the closed form. We first consider the fully cooperative case where a centralizer helps all groups to make caching decisions. We formulate the problem as a weighted-sum utility maximization problem, through which the maximum utility tradeoffs of different groups are characterized. Next, we study two benchmark cases under selfish caching, namely, partial and no cooperation, with and without inter-group file sharing, respectively. The optimal caching distributions for these two cases are derived. Finally, numerical examples are presented to compare the utilities under different cases and show the effectiveness of the fully cooperative local caching compared with the two benchmark cases.

Dynamic spectrum access (DSA) can effectively improve the spectrum efficiency and alleviate the spectrum scarcity, by allowing unlicensed secondary users (SUs) to access the licensed spectrum of primary users (PUs) opportunistically. Cooperative spectrum sharing is a new promising paradigm to provide necessary incentives for both PUs and SUs in dynamic spectrum access. The key idea is that SUs relay the traffic of PUs in exchange for the access time on the PUs' licensed spectrum. In this paper, we formulate the cooperative spectrum sharing between multiple PUs and multiple SUs as a two-sided market, and study the market equilibrium under both complete and incomplete information. First, we characterize the sufficient and necessary conditions for the market equilibrium. We analytically show that there may exist multiple market equilibria, among which there is always a unique Pareto-optimal equilibrium for PUs (called PU-Optimal-EQ), in which everyPU achieves a utility no worse than in any other equilibrium. Then, we show that under complete information, the unique Pareto-optimal equilibrium PU-Optimal-EQ can always be achieved despite the competition among PUs; whereas, under incomplete information, the PU-Optimal-EQ may not be achieved due to the mis-representations of SUs (in reporting their private information). Regarding this, we further study the worse-case equilibrium for PUs, and characterize a Robustequilibrium for PUs (called PU-Robust-EQ), which provides every PU a guaranteed utility under all possible mis-representation behaviors of SUs. Numerical results show that in a typical network where the number of PUs and SUs are different, the performance gap between PU-Optimal-EQ and PU-Robust-EQ is quite small (e.g., less than 10 percent in the simulations).

Indoor localization based on smartphone can enhance user's experiences in indoor environments. Although some innovative solutions have been proposed in the past two decades, how to accurately and efficiently localize users in indoor environments is still a challenging problem. Traditional indoor positioning systems based on Wi-Fi fingerprints or dead reckoning suffer from the variation of Wi-Fi signals and the drift of dead reckoning problems, respectively. Crowdsourcing and ambient sensing stimulate new ways to improve existing localization systems' accuracy. Using human social factors to calibrate the accuracy of localization is practical and awarding. In this paper, we propose WAIPO, a collaborative indoor localization system with the fusion of Wi-Fi and magnetic fingerprints, image-matching, and people co-occurrence. Specifically, we could obtain the most likely top-n locations based on Wi-Fi fingerprints. We utilize the statistics of users' historical locations known by image-matching, for which we propose a photo-room matching algorithm, to reduce estimating areas. In order to further improve the accuracy of localization, we propose a co-occurrence and non-co-occurrence detection algorithm to detect users' spatial-temporal co-occurrence and determine users' locations with magnetic calibration. We have fully implemented WAIPO on the Android platform and perform testbed experiments. The experimental results demonstrate that WAIPO achieves an accuracy of 87.3% on average, which outperforms the state-of-the-art indoor localization systems.

We consider a group of neighboring smartphone users who are roughly at the same time interested in the same network content, while the users can share content enabled by broadcast device-to-device communications. As the users are selfish in practice, an incentive mechanism is needed to motivate the physically neighboring users to form a social group. We propose a novel concept of equal-reciprocal incentive over broadcast networks, which fairly ensures that each pair of users in the social group share the same amount of content with each other. As the equal-reciprocal incentive may restrict the amount of the shared content, we analyze the optimal equal-reciprocal scheme that maximizes the shared content. While ensuring fairness among users, we show that this optimized scheme also maximizes each user's utility. Finally, we look at dynamic content arrivals and extend our incentive scheme successfully by proposing optimal online scheduling algorithms that achieve the equal-reciprocal incentive.

To enhance the national security, there is a growing need for authorized parties to legitimately monitor suspicious communication links for preventing intended crimes and terror attacks. In this paper, we propose a new wireless information surveillance paradigm by investigating a scenario, where a legitimate monitor aims to intercept a suspicious wireless link over fading channels. The legitimate monitor can successfully eavesdrop (decode) the information of the suspicious link at each fading state only when its achievable data rate is no smaller than that at the suspicious receiver. We propose a new approach, namely, proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver in a full-duplex mode so as to change the suspicious communication (e.g., to a smaller data rate) for overhearing more efficiently. By assuming perfect self-interference cancelation (SIC) and global channel state information (CSI) at the legitimate monitor, we characterize the fundamental information-theoretic limits of proactive eavesdropping. We consider both delay-sensitive and delay-tolerant applications for the suspicious communication, under which the legitimate monitor maximizes the eavesdropping non-outage probability (for event-based monitoring) and the relative eavesdropping rate (for content analysis), respectively, by optimizing the jamming power allocation over different fading states subject to an average power constraint. Numerical results show that the proposed proactive eavesdropping via cognitive jamming approach greatly outperforms other benchmark schemes. Furthermore, by extending to a more practical scenario with residual SI and local CSI, we design an efficient online cognitive jamming scheme inspired by the optimal cognitive jamming with perfect SIC and global CSI.

Mobile crowdsensing emerges as a promising sensing paradigm through leveraging the diverse embedded sensors in massive mobile devices. A key objective in mobile crowdsensing is to efficiently schedule mobile device users to perform multiple sensing tasks. Prior work mainly focused on the interactions between the task layer and the user layer, without considering the similarity of tasks' data requirements and the heterogeneity of users'sensing capabilities. In this work, we propose a three-layer data-centric crowdsensing model by introducing a new data layer between tasks and users, which allows us to effectively leverage both the task similarity and the user heterogeneity. We formulate a joint task selection and user scheduling problem on top of the data layer, aiming at maximizing the social welfare. This problem is difficult to solve due to the combinatorial nature as well as the two-sided private information of tasks and users. To address both issues, we propose a two- sided randomized auction mechanism, which is computationally efficient, individually rational, and incentive compatible in expectation. Simulations show that (i) the proposed randomized auction can achieve 90% of the maximum social welfare (benchmark), and (ii) the social welfare gain due to data reuse increases with the task similarity and reaches up to 1300% in our simulations.

This paper studies a full-duplex filter-and-forward (FD-FF) relay system in frequency-selective channels. Conventionally, the loop-back signal at the FD relay is treated as harmful self- interference and needs to be significantly suppressed via both analog- and digital-domain cancellation. However, the performance of the conventional self-interference cancellation approach is fundamentally limited due to the quantization error induced by the analog-to-digital converter (ADC) with limited dynamic range. In this paper, we consider an analog filter-and-forward design to help avoid the quantization error, and surprisingly show that the maximum achievable rate of such an FD-FF relay system is in fact regardless of the loop- back channel at the FD relay. We characterize the maximum achievable rate of this channel by jointly optimizing the transmit power allocation over frequency at the source and the frequency response of the filter at the relay, subject to their individual power constraints. Although this problem is non- convex, we obtain its optimal solution by applying the Lagrange duality method. By simulations it is shown that the proposed joint source and relay optimization achieves rate gains over other heuristic designs, and is also advantageous over the conventional approach by cancelling the relay loop- back signal as self-interference, especially when the residual self-interference after cancellation is still significant.

Local caching with device-to-device (D2D) communications has been recently introduced as an effective scheme for reducing the average download time of the mobile terminals (MTs). The MTs first cache the files in their local memories and then exchange the files with each other within the vicinity via D2D communications. Prior works have largely overlooked MTs' heterogeneity in file preferences and assume unselfish caching behaviors of the MTs. In this work, we practically divide the MTs into different groups according to their individual preferences over the files and propose optimal file caching strategies for self-interested MTs to reduce the average file download time. Assuming the knowledge of the social file preference for an intelligent group, we develop the optimal caching strategy for this group by formulating and solving a convex optimization problem. Closed-form solution for the problem is obtained, which is shown to follow a water-filling structure over the files. Finally, numerical examples are presented to show that the selfish caching of a group can be detrimental to both itself and the other intelligent groups.

There is a growing need for government agencies to monitor suspicious communication links to prevent crimes and terror attacks. In this paper, we study a legitimate surveillance scenario where a legitimate monitor aims to intercept the suspicious communication between a transmitter and a receiver over fading channels. The legitimate monitor can eavesdrop (decode) the information of the suspicious link only when its achievable data rate is no smaller than that at the suspicious receiver. In practice, the legitimate eavesdropping is challenging, especially when the legitimate monitor is far from the suspicious transmitter. To overcome this issue, we propose a new approach, namely proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver and changes the suspicious communication (e.g., to a smaller data rate) in order to overhear easily. In particular, we consider delay-sensitive and delay-insensitive applications for the suspicious transmission, under which the legitimate monitor maximizes the eavesdropping non-outage probability and the relative eavesdropping rate, respectively, by optimizing its jamming power allocation over different fading states subject to an average power constraint. We present efficient algorithms for optimally solving the formulated problems. Numerical results show that thanks to the cognitive jamming, the proposed proactive eavesdropping scheme greatly outperforms the conventional passive eavesdropping without jamming.

To cope with consumers' ever-increasing demand of high-quality contents in the Internet, content providers (e.g., YouTube) mainly pay to global pure-play CDN companies (e.g., Akamai) instead of ISPs for content delivery. This motivates ISPs to offer their own regional CDN services as Telco-CDNs that compete with the pure-play CDNs for CPs in the CDN market. Unlike the pure-play CDNs with global consumer coverage geographically, Telco-CDNs have the strength of better QoS due to integration of traffic engineering with content delivery. In this paper, we study their competition for CPs by using dynamic game theory, where Telco-CDNs can choose to federate with each other and to which extent. We first analyze the traditional case when Telco-CDNs do not federate and independently operate to locally compete with a typical pure-play CDN. We next study the case when Telco-CDNs form a federation by (i) physically pooling their resources and/or further (ii) economically sharing the total revenue. Depending on the degree of their cooperation (with only (i), called partial federation, or with both (i) and (ii), called full federation), we study how strongly and successfully Telco-CDNs are able to penetrate into the CDN market. Perhaps surprisingly, we show that Telco-CDNs' federation may not help themselves due to the threat of perfect competition with the pure-play CDN.

Traditional macrocell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macrocells. Given the massive deployment of small-cells in a macrocell, the aggregate power consumption of small-cells (though being low individually) can be larger than that of the macrocell. Compared to the macrocell base station (MBS) whose power consumption increases significantly with its traffic load, the power consumption of a small-cell base station (SBS) is relatively flat and independent of its load. To reduce the total power consumption of the heterogeneous networks (HetNets), we dynamically change the operating states (on and off) of the SBSs, while keeping the MBS on to avoid any service failure outside active small-cells. First, we consider that the wireless users are uniformly distributed in the network, and propose an optimal location-based operation scheme by gradually turning off the SBSs closer to the MBS. We then extend the operation problem to a more general case where users are nonuniformly distributed in the network. Although this problem is NP-hard, we propose a joint location and user density based operation scheme to achieve near-optimum (with less than 1% performance loss in our simulations) in polynomial time.

In this paper, to minimize the on-grid energy cost in a large-scale green cellular network, we jointly design the optimal base station (BS) ON/OFF operation policy and the on-grid energy purchase policy from a network-level perspective. We consider that the BSs are aggregated as a microgrid with hybrid energy supplies and an associated central energy storage, which can store the harvested renewable energy and the purchased on-grid energy over time. Due to the fluctuations of the on-grid energy prices, the harvested renewable energy, and the network traffic loads over time, as well as the BS coordination to hand over the traffic offloaded from the inactive BSs to the active BSs, it is generally NP-hard to find a network-level optimal adaptation policy that can minimize the on-grid energy cost over a long-term and yet assures the downlink transmission quality at the same time. Aiming at the network-level dynamic system design, we jointly apply stochastic geometry (Geo) for large-scale green cellular network analysis and dynamic programming (DP) for adaptive BS ON/OFF operation design and on-grid energy purchase design, and thus propose a new Geo-DP design approach. By this approach, we obtain the optimal BS ON/OFF policy, which shows that the optimal BSs' active operation probability in each horizon is just sufficient to assure the required downlink transmission quality with time-varying load in the large-scale cellular network. However, due to the curse of dimensionality of the DP, it is of high complexity to obtain the optimal on-grid energy purchase policy. We thus propose a suboptimal on-grid energy purchase policy with low complexity, where the low-price on-grid energy is over purchased in the current horizon only when the current storage level and the future renewable energy level are both low. Simulation results show that the suboptimal on-grid energy purchase can achieve near-optimal performance. We also compare the proposed policy with the existing schemes to show that our proposed policy can more efficiently save the on-grid energy cost over time.

Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs' locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the expressions of the distribution metrics of the aggregate received power at a typical SN. To design the charging radius for the optimal AD-WPT operation, we exploit the tradeoff between the power intensity of the energy beams and the number of SNs to be charged. Depending on different SN task requirements, the optimal AD-WPT can maximize the average received power or the active probability of the SNs, respectively. It is shown that both the maximum average received power and the maximum sensor active probability increase with the increased deployment density and transmit power of the PBs, and decrease with the increased density of the SNs and the energy beamwidth. Finally, we show that the optimal AD-WPT can significantly improve the energy transfer efficiency compared with the traditional omnidirectional WPT.

In the emerging hybrid electricity market, mobile network operators (MNOs) of cellular networks can make day-ahead energy purchase commitments at low prices and real-time flexible energy purchase at high prices. To minimize electricity bills, it is essential for MNOs to jointly optimize the day-ahead and real-time energy purchase based on their time-varying wireless traffic load. In this paper, we consider two different MNOs coexisting in the same area, and exploit their collaboration in both energy purchase and wireless load sharing for energy cost saving. Specifically, we propose a new approach named energy group buying with load sharing, in which the two MNOs are aggregated as a single group to make the day-ahead and real-time energy purchase, and their base stations (BSs) share the wireless traffic to maximally turn lightly-loaded BSs into sleep mode. When the two MNOs belong to the same entity and aim to minimize their total energy cost, we use the two-stage stochastic programming to obtain the optimal day-ahead and real-time energy group buying jointly with wireless load sharing. When the two MNOs belong to different entities and are self-interested in minimizing their individual energy costs, we propose a novel repeated Nash bargaining scheme for them to negotiate and share their energy costs under energy group buying and load sharing. Our proposed repeated Nash bargaining scheme is shown to achieve Pareto-optimal and fair energy cost reductions for both MNOs.

In this paper, multiple relays capable of harvesting energy from radio-frequency (RF) signals are employed to collaboratively forward data from a source transmitter to its destined receiver. Due to the relays' inability to harvest energy and transmit data simultaneously, the source needs to optimally schedule the relays' energy harvesting (EH) and data transmission. Considering different channel conditions and energy constraints, the relays need to optimally design a beamforming vector that specifies each relay a power amplifier coefficient to forward the source signal and suppress the noise. By joint EH scheduling and beamforming, we maximize the overall throughput formulated in a nonconvex problem. We first propose a centralized scheme that achieves the optimal throughput by exploiting the monotonicity in the problem structure. We further propose a distributed suboptimal scheme in a game theoretic approach, which requires the source and the relays to iteratively update EH scheduling and beamforming vector, respectively. We show that the suboptimal scheme has a threshold-based structure for the relays' power control depending on the source-relay channel conditions. Numerical results show near-optimal performance of the distributed scheme compared with the centralized optimal scheme.

Cooperative communications has long been proposed as an effective method for reducing the energy consumption of the mobile terminals (MTs) in wireless cellular networks. However, it is hard to implement due to the lack of incentives for the MTs to cooperate. In this paper, we propose a pricing mechanism to incentivize the uplink cooperative communications for the energy saving of MTs. We first consider the ideal case of MTs' full cooperation under complete information. For this scenario as the benchmark case, where the private information of the helping MTs such as the channel and battery conditions is completely known by the source MT, the problem is formulated as a relay selection problem. Then, for the practical case of partial cooperation with incomplete information, the MTs need to cooperate under the uncertainties of the helping MTs' channel and battery conditions. For this scenario, we propose a partial cooperation scheme with pricing where a source MT in low-battery level or bad channel condition is allowed to select and pay another MT in proximity to help forward its data to the base station (BS). We formulate the source MT's pricing and load sharing problem as an optimization problem. Efficient algorithms based on dichotomous search and alternative optimization are proposed to solve the problem for the cases of splittable and nonsplittable data at the source MT, respectively. Finally, extensive numerical results are provided to show that our proposed cooperative communications scheme with pricing can significantly decrease both the communications and battery outages for the MTs, and can also increase the average battery level during the MTs' operation.

This paper studies how to maximize a spectrum database operator's expected revenue in sharing spectrum to secondary users, through joint pricing and admission control of spectrum resources. A unique feature of our model is the consideration of the stochastic and heterogeneous nature of secondary users' demands. We formulate the problem as a stochastic dynamic programming problem, and present the optimal solutions under both static and dynamic pricing schemes. In the case of static pricing, the prices do not change with time, although the admission control policy can still be time-dependent. In this case, we show that a stationary (time-independent) admission policy is in fact optimal under a wide range of system parameters. In the case of dynamic pricing, we allow both prices and admission control policies to be time-dependent. We show that the optimal dynamic pricing can improve the operator's revenue by more than 30% over the optimal static pricing, when secondary users' demands for spectrum opportunities are highly elastic.

Mobile data services are becoming the main driver of a wireless service provider's (WSPs) revenue growth, and two-part tariff data plans (each including a lump-sum fee and a per-unit charge) are usually provided to wireless users. Some users can easily use up their monthly data quota and may pay for costly data over-usage. Motivated by users' diverse usage behavior (more or less than the subscribed data quotas), this paper proposes a new type of user-initiated network for cellular users to trade data plans by leveraging personal hotspots (PHs) with users' smartphones. A user with data surplus can set up a PH and share the cellular data connection to another user with data deficit in the vicinity. Due to users' randomness in data usage, incentive to trade, and user mobility to enter or leave the PH connection range, the analysis on the secondary trading market is challenging. To overcome these issues, we propose a PH-market for users with diverse data usage behaviors and random user mobility to directly trade data as sellers and buyers, by designing a market-clearing price. It is shown that the PH-market greatly saves all users' expected costs when the existence condition of the PH-market is met. Finally, as this PH-market will challenge the WSP's revenue collection (especially the surcharge from users' data over-usage), we analyze the WSP's response to the PH-market and propose two effective countermeasure strategies by either reducing the selling users' data quota in their data plans (the PH-market's supply) or increasing the buying users' data quota (the PH-market's demand). When we have more than one WSP and they are competitive, we show that one WSP can take advantage of the PH-market by indirectly selling more data to the other WSP's users.

Mobile crowdsensing is a new sensing paradigm relying on computation and storage capabilities of mobile devices. However, traditional server-client mobile crowdsensing models suffer from a high operational cost on the server, and hence a poor scalability. Peer-to- peer (P2P) mobile crowdsensing models can effectively reduce the server's operational cost, by leveraging the mobile devices' under-utilized computation and storage resources. In a P2P mobile crowdsensing model, the sensing data is saved and processed in mobile users' devices in a distributed fashion, and is shared among mobile users directly in a P2P manner. In this work, we focus on the incentive issue in such a P2P mobile crowdsensing model. Specifically, we propose a data market and a generic pricing scheme for the data sharing among data sensors and requesters. We analyze the user interactions in such a data market from a game theoretic perspective, and prove the existence and uniqueness of the market equilibrium. We further propose a generalized best response dynamics to reach the market equilibrium. Our theoretic analysis and numerical results indicate that the equilibrium social welfare decreases with the data transfer cost and data prices, while the ratio of the equilibrium social welfare to the maximum social welfare benchmark increases with the data transfer cost and data prices.

Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs' locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the closed-form expressions of the distribution metrics of the aggregate received power at a typical SN. We analyze the optimal charging radius that maximizes the average received power. It is shown that both the optimal charging radius and maximized average received power decrease with the increased density of the SNs and the energy beamwidth. Finally, we show that the optimal AD-WPT can significantly improve the energy transfer efficiency compared to the traditional omnidirectional WPT.

WiFi offloading helps serve ever-increasing data traffic in cellular networks and mitigate the network congestion. Yet it can only apply to cellular users within WiFi coverage. Recently, delayed WiFi offloading is proposed to exploit users' mobility to purposely travel to WiFi coverage for data offloading. Its successful implementation depends on users' willingness to delay ongoing cellular data services until entering WiFi coverage. This paper proposes an incentive mechanism to allow a network operator to optimally reward his users to participate in delayed WiFi offloading, so as to reduce the network congestion. We formulate the design problem as a two-stage Stackelberg game: in Stage I, the operator announces a uniform reward to users to delay their existing cellular services; and in Stage II, each user decides to join the delayed offloading or not, depending on the reward, the network congestion, and his estimation of waiting cost for WiFi connection. The operator and users may or may not know all users' mobility and waiting cost information; thus, we propose optimal reward mechanisms under various information availability scenarios. Interestingly, we show that the optimal reward does not always increase with the cellular traffic load, as the increased network congestion can also help motivate users to switch to WiFi networks.

Major cellular operators are planning to upgrade to high-speed 4G networks, but due to budget constraints, they have to dynamically plan and deploy the 4G networks through multiple stages of time. By considering one-time deployment cost, daily operational cost and 3G network congestion, this paper studies how an operator financially manages the cash flow and plans the 4G deployment in a finite time horizon to maximize his final-stage profit. The operator provides both the traditional 3G service and the new 4G service, and we show that users will start to use the 4G service only when it reaches a sizable coverage. At each time stage, the operator first decides an additional 4G deployment size, by predicting users' responses in choosing between the 3G and 4G services. We formulate this problem as a dynamic programming problem, and propose an optimal threshold-based 4G deployment policy. We show that the operator will not deploy to a full 4G coverage in an area with low user density or high deployment/operational cost. Perhaps surprisingly, during the 4G deployment process, we show that the 4G subscriber number first increases and then decreases, as the 4G service helps mitigate 3G network congestion and increases its QoS.

We consider a cognitive radio network, where primary users (PUs) share their spectrum with energy harvesting (EH) enabled secondary users (SUs), conditioned on a limited SUs' interference at PU receivers. Due to the lack of information exchange between SUs and PUs, the SU-PU interference channels are subject to uncertainty in channel estimation. Besides channel uncertainty, SUs' EH profile is also subject to spatial and temporal variations, which enforce an energy causality constraint on SUs' transmit power control and affect SUs' interference at PU receivers. Considering both the channel and EH uncertainties, we propose a robust design for SUs' power control to maximize SUs' throughput performance. Our robust design targets at the worst-case interference constraint to provide a robust protection for PUs, while guarantees a transmission probability to reflect SUs' minimum QoS requirements. To make the non-convex throughput maximization problem tractable, we develop a convex approximation for each robust constraint and successfully design a successive approximation approach that converges to the global optimum of the throughput objective. Simulations show that SUs will change transmission strategies according to PUs' sensitivity to interference, and we also exploit the impact of SUs' EH profile (e.g., mean, variance, and correlation) on SUs' power control.

The recent development of social services tightens wireless users' social relationships and encourages them to generate more data traffic under network effect. This boosts the demand for wireless services yet may challenge the limited wireless capacity. To fully exploit this opportunity, we study mobile users' data usage behaviors by jointly considering the network effect based on their social relationships in the social domain and the congestion effect in the physical wireless domain. Accordingly, we develop a Stackelberg game for problem formulation: In Stage I, a wireless provider first decides the data pricing to all users to maximize its revenue, and then in Stage II users observe the price and decide data usage subject to mutual interactions under both network and congestion effects. We analyze the two-stage game using backward induction. For Stage II, we first show the existence and uniqueness of a user demand equilibrium (UDE). Then we propose a distributed update algorithm for users to reach the UDE. Furthermore, we investigate the impacts of different parameters on the UDE. For Stage I, we develop an optimal pricing algorithm to maximize the wireless provider's revenue. We evaluate the performance of our proposed algorithms by numerical studies using real data, and thereby draw useful engineering insights for the operation of wireless providers.

Wireless charging is a promising way to power wireless nodes' transmissions. This paper considers new dual-function access points (APs), which are able to support the energy/information transmission to/from wireless nodes. We focus on a large-scale wireless powered communication network (WPCN), and use stochastic geometry to analyze the wireless nodes' performance tradeoff between energy harvesting and information transmission. We study two cases with battery-free and battery-deployed wireless nodes. For both cases, we consider a harvest-then-transmit protocol by partitioning each time frame into a downlink (DL) phase for energy transfer, and an uplink (UL) phase for information transfer. By jointly optimizing frame partition between the two phases and the wireless nodes' transmit power, we maximize the wireless nodes' spatial throughput subject to a successful information transmission probability constraint. For the battery-free case, we show that the wireless nodes prefer to choose small transmit power to obtain large transmission opportunity. For the battery-deployed case, we first study an ideal infinite-capacity battery scenario for wireless nodes, and show that the optimal charging design is not unique, due to the sufficient energy stored in the battery. We then extend to the practical finite-capacity battery scenario. Although the exact performance is difficult to be obtained analytically, it is shown to be upper and lower bounded by those in the infinite-capacity battery scenario and the battery-free case, respectively. Finally, we provide numerical results to corroborate our study.

To match wireless users' soaring traffic demand, spectrum regulators are considering allocating additional spectrum to the wireless market. There are two major directions for the spectrum allocation: licensed (e.g., 4G cellular service) and unlicensed services (e.g., Super Wi-Fi service). The 4G service provides a ubiquitous coverage, has a higher spectrum efficiency, and often charges users a high service price. The Super Wi-Fi service has a limited coverage, a lower spectrum efficiency, but often charges users a low service price. The spectrum regulator now simply allocates the spectrum to maximize its income, but such an income-centric allocation does not ensure the best spectrum utilization by the users. This motivates us to design a new spectrum allocation scheme which jointly considers the spectrum regulator's income and the users' aggregate utility by investigating three market tiers: the spectrum regulator, 4G and Super Wi-Fi operator coalitions, and all the wireless users. We formulate it as a three-stage game and derive the unique subgame perfect equilibrium. Compared with the traditional income-centric allocation, we prove that the proposed scheme significantly improves users' aggregate utility with a limited spectrum regulator's income loss.

This paper analyzes two pricing schemes commonly used in Wi-Fi markets: the flat-rate and the usage-based pricing. The flat-rate pricing encourages the maximum usage, while the usage-based pricing can flexibly attract more users especially those with low valuations in mobile Internet access. First, we use theoretical analysis to compare the two schemes and show that for a single provider in a market, as long as the Wi-Fi capacity is abundant, the flat-rate pricing leads to more revenue. Second, we study how a global provider (e.g., Skype) collaborates with this monopolist in each local market to provide a global Wi-Fi service. We formulate the interactions between the global and local providers as a dynamic game. In Stage I, the global provider bargains with the local provider in each market to determine the global Wi-Fi service price and revenue sharing agreement. In Stage II, local users and travelers choose local or global Wi-Fi services. We analytically show that the global provider prefers to use the usage-based pricing to avoid a severe competition with the local provider. At the equilibrium, the global provider always shares the majority of his revenue with the local provider to incentivize the cooperation. Finally, we analytically study how the interaction changes if the local market has more than one local provider. In this case, the global provider can integrate the coverages of multiple local providers and provide a better service. Compared to the local monopoly case, local market competition enables the global provider to share less revenue with each of the local providers. However, we numerically show that the global provider's revenue could decrease, as he shares his revenue with more providers and can only charge a lower price.

As the successor to the 3G standard, the 4G cellular standard provides much higher data rates to address cellular users' ever-increasing demands for high-speed multimedia communications. This paper analyzes the cellular operators' timing of network upgrades, by considering user subscription dynamics induced by switching from 3G to 4G technologies. Being the first to upgrade 3G to 4G service, an operator increases its market share but takes more risk or upgrade cost as 4G technology matures overtime. This paper first studies a 4G monopoly market with one dominant operator and some small operators, where the monopolist decides its upgrade time by trading off increased market share and upgrade cost. The paper also considers a 4G competitive market and develops a game theoretic model for studying operators' interactions. The analysis shows that operators select different upgrade times to avoid severe competition. One operator takes the lead to upgrade, using the benefit of a larger market share to compensate for the larger cost of an early upgrade. This result matches well with many industry observations of asymmetric4G upgrades. The paper further shows that the availability of 4G upgrade may decrease both operators' profits due to increased competition. Perhaps surprisingly, the profits can increase with the upgrade cost.

Sensing falsification is a key security threat in cooperative spectrum sensing in cognitive radio networks. Intelligent malicious users (IMUs) adjust their malicious behaviors according to their objectives and the network's defense schemes. Without long-term collection of information on users' reputation, the existing schemes fail to thwart such malicious behaviors. In this paper, we construct a joint spectrum sensing and access framework to thwart the malicious behaviors of both rational and irrational IMUs. Lack of reputation information makes the malicious behavior resistance degrade performance since the honest users may be misjudged as IMUs. Based on the moral hazard principal-agent model, we design an incentive compatible mechanism to provide a moderate punishment to IMUs. Our findings show that neither spectrum sensing nor spectrum access alone can prevent malicious behaviors without any information on users' reputation. According to the different properties of malicious behavior resistance by spectrum sensing and spectrum access, we employ joint spectrum sensing and access to optimally prevent the IMUs sensing falsification. The proposed malicious behavior resistance mechanism is shown to achieve almost the same performance as the ideal case with truthful sensing.

In cognitive radio networks, it is challenging for secondary users (SUs) to estimate and control their interference at the receivers of primary users (PUs), due to incomplete or erroneous channel information between SUs and PUs. Thus, SUs need to estimate the worst-case aggregate interference at PU receivers to ensure guaranteed protection for PUs from excessive interference. As it is rare that all SU-PU channels experience the worst-case conditions simultaneously, we propose a practical model (namely, the worst-case selective robust model) for SUs to estimate their aggregate interference power. This model employs an adjustable parameter to control the number of SU-PU channels that are in the worst-case conditions. For an individual SU-PU channel, the estimation of worst-case channel gain is subject to a distribution uncertainty. Given this robust model, we study SUs' power control problem in a non-cooperative game where each SU selfishly maximizes its own throughput performance subject to coupled interference constraints at PU receivers. We study the existence and uniqueness of Nash equilibrium and propose an iterative algorithm for SUs to achieve the equilibrium in a distributed manner. Numerical results show that our algorithm provides guaranteed protection for PUs and fair throughput performance for SUs, provided with uncertain SU-PU channel information.

In cognitive radio networks, it is challenging for secondary users (SUs) to keep track of their interference at the receivers of primary users (PUs), due to the error in channel estimation and irregular information exchange between SUs and PUs. In this paper, we practically consider that SUs have only partial knowledge about the channel gains from SUs to PUs, based on which SUs estimate the worst-case channel gains and decide transmit power to robustly protect PUs. As it is rare that all SU-PU channels experience the worst-case conditions simultaneously, we proposed the worst-case selective robust model for SUs to estimate the aggregate interference power at PU receivers by predicting that only a part of SU-PU channels are in the worst-case conditions. We study SUs' robust power control problem in a non-cooperative game, where each SU maximizes its own throughput subject to interference constraints at PU receivers. We propose an iterative algorithm for SUs to achieve unique Nash equilibrium in a distributed manner. Extensive numerical results show that our algorithm provides guaranteed protection for PUs provided with uncertain SU-PU channel information.

Powered by renewable energy sources, cellular communication systems usually have different traffic loads and resource availabilities over time. It is helpful for two neighbouring systems to cooperate in resource sharing when one is excessive in one resource (e.g., spectrum), while the other is sufficient in another resource (e.g., energy). In this paper, we propose a joint energy and spectrum sharing scheme between different cellular systems to save their operational costs. When the two systems are fully cooperative (e.g., belonging to the same entity), we formulate their cooperation problem to minimize the weighted sum cost as a convex optimization problem and obtain its closed-form optimal solution. We also study another partially cooperative scenario where the two systems have their own interests. We show that the two systems seek for partial cooperation when they find complementarity between the spectrum and energy resources. Under the partial cooperation conditions, we propose a distributed algorithm for the two systems to gradually and simultaneously reduce their costs from a non-cooperation benchmark to the Pareto optimum. This distributed algorithm also takes fairness into consideration, by reducing each system's cost proportionally. Finally, numerical results are presented to demonstrate the improvement made by our proposed schemes.

Sensing falsification is a key security problem in cooperative spectrum sensing for cognitive radio networks. Most previous approaches assume that malicious users only cheat in their sensing reports following a predefined rule. However, some malicious users usually act intelligently to strategically adjust their malicious behavior according to their objectives and the network's defense schemes. The existing schemes cannot resist the malicious behaviors of intelligent malicious users (IMUs) without long-term collection of information on their reputation. In this paper, we construct a moral hazard principal-agent framework and design an incentive compatible mechanism to thwart the malicious behaviors of rational and irrational IMUs. We find that neither spectrum sensing nor spectrum access alone can prevent the malicious behavior without any information on users' reputation. According to the analysis of malicious behavior resistance methods, we propose a joint spectrum sensing and access mechanism to optimally prevent the IMUs from sensing falsification. Our evaluation results show that the proposed mechanism achieves almost the same performance as the ideal case with perfect sensing.

This paper solves the problem of long-term revenue maximization of a spectrum database operator, through joint pricing of spectrum resources and admission control of secondary users. A unique feature that we consider is the stochastic and heterogeneous nature of secondary users' demands. We formulate the problem as a stochastic dynamic programming problem, and consider the optimal solutions under both static and dynamic prices. In the case of static pricing, we constrain the prices to be time-independent while allowing the admission control policies to be time dependent. We show that in most cases a stationary (time independent) admission policy is in fact optimal in this case. We further look at the general case of dynamic pricing, where both the prices and admission control policies can be time dependent. We show that the flexibility of dynamic pricing can significantly improve the operator's revenue (by more than 30%) when secondary users have high demand elasticities.

Powered by renewable energy sources, cellular communication systems usually have different wireless traffic loads and available resources over time. To match their traffics, it is beneficial for two neighboring systems to cooperate in resource sharing when one is excessive in one resource (e.g., spectrum), while the other is sufficient in another (e.g., energy). In this paper, we propose a joint energy and spectrum cooperation scheme between different cellular systems to reduce their operational costs. When the two systems are fully cooperative in nature (e.g., belonging to the same entity), we formulate the cooperation problem as a convex optimization problem to minimize their weighted sum cost and obtain the optimal solution in closed form. We also study another partially cooperative scenario where the two systems have their own interests. We show that the two systems seek for partial cooperation as long as they find inter-system complementarity between the energy and spectrum resources. Under the partial cooperation conditions, we propose a distributed algorithm for the two systems to gradually and simultaneously reduce their costs from the non-cooperative benchmark to the Pareto optimum. This distributed algorithm also has proportional fair cost reduction by reducing each system's cost proportionally over iterations. Finally, we provide numerical results to validate the convergence of the distributed algorithm to the Pareto optimality and compare the centralized and distributed cost reduction approaches for fully and partially cooperative scenarios.

Providing economic incentives to all parties involved is essential for the success of dynamic spectrum access. Cooperative spectrum sharing is one effective way to achieve this, where secondary users (SUs) relay traffics for primary users (PUs) in exchange for dedicated spectrum access time for SUs' own communications. In this paper, we study the cooperative spectrum sharing under incomplete information, where SUs' wireless characteristics are private information and not known by a PU. We model the PU-SU interaction as a labor market using contract theory. In contract theory, the employer generally does not completely know employees' private information before the employment and needs to offers employees a contract under incomplete information. In our problem, the PU and SUs are, respectively, the employer and employees, and the contract consists of a set of items representing combinations of spectrum accessing time (i.e., reward) and relaying power (i.e., contribution). We study the optimal contract design for both weakly and strongly incomplete information scenarios. In the weakly incomplete information scenario, we show that the PU will optimally hire the most efficient SUs and the PU achieves the same maximum utility as in the complete information benchmark. In the strongly incomplete information scenario, however, the PU may conservatively hire less efficient SUs as well. We further propose a decompose-and-compare (DC) approximate algorithm that achieves a close-to-optimal contract. We further show that the PU's average utility loss due to the suboptimal DC algorithm and the strongly incomplete information are relatively small (less than 2 and 1.3 percent, respectively, in our numerical results with two SU types).

This paper analyzes and compares different incentive mechanisms for a master to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. To collect massive sensitive data from users, we propose a reward-based collaboration mechanism, where the master announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users wanting to collaborate. We show that if the master knows the users' collaboration costs, then he can choose to involve only users with the lowest costs. However, without knowing users' private information, then he needs to offer a larger total reward to attract enough collaborators. Users will benefit from knowing their costs before the data acquisition. Perhaps surprisingly, the master may benefit as the variance of users' cost distribution increases. To utilize smartphones' computation resources to solve complex computing problems, we study how the master can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the master involves a user type as long as the master's preference characteristic outweighs that type's unit cost. All collaborators achieve a zero payoff in this case. If the master does not know users' private cost information, however, he will conservatively target at a smaller group of users with small costs, and has to give most benefits to the collaborators.

This paper studies spatial capacity in a stochastic wireless ad hoc network. We propose a novel signal-to-interference-ratio (SIR) threshold based scheduling scheme with multi-stage probing and data transmission, where each transmitter iteratively decides to further probe or stay idle, depending on whether the estimated SIR in the proceeding probing is no smaller than a predefined threshold. Though the locations of the initial transmitters can be modeled as a homogeneous Poisson Point Process (PPP), the SIR based scheduling makes the PPP model no longer applicable in the subsequent probing and data transmission phases. We first focus on single-stage probing and find that when the SIR threshold is set sufficiently small to assure an acceptable network interference level, the proposed scheme can greatly outperform the reference scheme without any transmission scheduling in terms of spatial capacity. We clearly characterize the spatial capacity with exact/approximate closed-form expressions, by proposing a new approximate approach to deal with the correlated SIR distributions over non-PPPs. Then, we successfully extend to multi-stage probing, by properly designing the multiple SIR thresholds to assure gradual improvement of the spatial capacity. Furthermore, we analyze the impact of multi-stage probing overhead and present a probing-capacity tradeoff in scheduling design. Finally, extensive numerical results are presented to demonstrate the scheduling performance.

Small cell access points (SAPs) can offload macrocell traffic, improve indoor coverage and cell-edge user performance, and boost network capacity. In this paper, we investigate the problem faced by the mobile network operator (MNO) on how to properly incentivize the existing private SAPs to serve extra roaming macrocell users. We propose a refunding framework for small cell networks with limited-capacity backhaul, where small cell holders (SHs) receive refunding from the MNO and then admit macrocell users. Specifically, we formulate a two-stage refunding-admission game with MNO being the leader and SHs being the followers. Our results can be summarized as follows: 1) we formulate a revenue maximization problem by allowing the MNO to set individualized refunding and interference temperature constraints to SAPs. We propose a lookup table approach to solve it; 2) for small cells with guaranteed QoS provisioning, we consider access-based refunding and propose a near-optimal joint user admission and power allocation algorithm to solve the utility maximization problem at each SAP; and 3) for small cells with best-effort QoS provisioning, we consider usage-based refunding and propose a majorization method-based power allocation algorithm. Extensive numerical results show that our proposed framework and algorithms yield significant improvements on the MNO's net revenue and SHs' utilities compared with a non-refunding case. Our research highlights the possibility of enhancing the MNO's net revenue without changing the current network structure and the importance of incentivizing SHs by taking the limited-capacity backhaul into account.

This paper studies how to manage a cognitive radio network (CRN) by trading off the cost of spectrum acquisition and the quality of service (QoS) in serving secondary users. Due to bursty nature of primary users' traffic and random realization of spectrum holes, it is difficult for a CRN operator to guarantee the QoS to its users by using the cheap but unreliable spectrum sensing only. Once the sensed available spectrum turns out to be not enough to satisfy users' QoS requirement, the CRN operator needs to lease additional spectrum from licensed network operators, which will incur high leasing cost and threatens the long-run viability of the CRN network. In this paper, we propose a real-time decision-making algorithm for the CRN operator to acquire spectrum based on a Lyapunov optimization framework. We successfully minimize the total sensing and leasing cost, while guaranteeing each user's average queuing delay below a certain designed threshold.

In this paper, we investigate the limited-capacity backhaul's impact on small cell holders' (SHs') utilities and the mobile network operator's (MNO's) net revenue under a refunding framework. SHs are reluctant to share accesses with guest users due to selfish nature. To advocate better resource utilization, the MNO refunds SHs to motivate hybrid access as incentives. We model the interactions between the MNO and SHs as a Stackelberg game: in Stage I, the MNO refunds SHs and we propose a lookup table approach to decide individualized refunding and interference temperature constraints to different SHs; in Stage II, SHs admit guest users and we propose a near-optimal two-phase guest user admission algorithm where guest users are gradually admitted in terms of the minimum increment of sum-log power. Simulation results show that under the limited-capacity backhaul, a higher refunding can increase SHs' utilities while decrease the MNO's net revenue. Hence, the MNO implicitly controls the number of admitted guest users through individualized refunding to maximize its net revenue.

To accommodate users' ever-increasing traffic in wireless broadband services, the Federal Communications Commission (FCC) in the U.S. is considering allocating additional spectrum to the wireless market. There are two major directions: licensed (e.g. 3G) and unlicensed services (e.g. Wi-Fi). On the one hand, 3G service can realize a high spectrum efficiency and provide ubiquitous connection. On the other hand, the Wi-Fi service (often with limited coverage) can provide users with high-speed local connections, but is subject to uncontrollable interferences. Regarding spectrum allocation, prior studies only focused on revenue maximization. However, one of FCC's missions is to better improve all wireless users' utilities. This motivates us to design a spectrum allocation scheme that jointly considers social welfare and revenue. In this paper, we formulate the interactions among the FCC, typical 3G and Wi-Fi operators, and the endusers as a three-stage dynamic game and derive the equilibrium of the entire game. Compared to the benchmark case where the FCC only maximizes its revenue, the consideration of social welfare will encourage the FCC to allocate more spectrum to the service which lacks spectrum to better serve its users. Such consideration for the social welfare, to our delight, brings limited revenue loss for the FCC.

This paper analyzes two pricing schemes commonly used in WiFi markets: flat-rate pricing and usage-based pricing. The flat-free pricing encourages users to achieve the maximum WiFi usage and targets at users with high valuations in mobile Internet access, whereas the usage-based pricing is flexible to attract more users - even those with low valuations. First, we show that for a local provider, the flat-rate pricing provides more revenue than the usage-based pricing, which is consistent with the common practice in today's local markets. Second, we study how Skype may work with many local WiFi providers to provide a global WiFi service. We formulate the interactions between Skype, local providers, and users as a two-stage dynamic game. In Stage I, Skype bargains with each local provider to determine the global Skype WiFi service price and revenue sharing agreement; in Stage II, local users and travelers decide whether and how to use local or Skype WiFi service. Our analysis discovers two key insights behind Skype's current choice of usage-based pricing for its global WiFi service: to avoid severe competition with local providers and attract travelers to the service. We further show that at the equilibrium, Skype needs to share the majority of his revenue with a local provider to compensate the local provider's revenue loss due to competition. When there are more travelers or fewer local users, the competition between Skype and a local provider becomes less severe, and Skype can give away less revenue and reduce its usage-based price to attract more users.

As the successor to the 3G standard, 4G provides much higher data rates to address cellular users' ever-increasing demands for high-speed multimedia communications. This paper analyzes the cellular operators' timing of network upgrades and models that users can switch operators and services. Being the first to upgrade 3G to 4G service, an operator increases his market share but takes more risk or upgrade cost because 4G technology matures over time. This paper first studies a 4G monopoly market with one dominant operator and some small operators, where the monopolist decides his upgrade time by trading off increased market share and upgrade cost. The paper also considers a 4G competition market and develops a game theoretic model for studying operators' interactions. The analysis shows that operators select different upgrade times to avoid severe competition. One operator takes the lead to upgrade, using the benefit of a larger market share to compensate for the larger cost of an early upgrade. This result matches well with many industry observations of asymmetric 4G upgrades. The paper further shows that the availability of 4G upgrade may decrease both operators' profits due to increased competition. Perhaps surprisingly, the profits can increase with the upgrade cost.

Femtocells can effectively resolve the poor connectivity issue of indoor cellular users. This paper investigates the economic incentive for a cellular operator to add femtocell service on top of its existing macrocell service. We model the interactions between a cellular operator and users as a Stackelberg game: The operator first determines spectrum allocations and pricings of femtocell and macrocell services, and then heterogeneous users choose between the two services and the amount of resource to request. In the ideal case where the femtocell service has the same full spatial coverage as the macrocell service, we show that the operator will choose to provide femtocell service only, as this leads to a better user quality of service and a higher operator profit. However, if we impose the constraint that no users' payoffs decrease after introducing the femtocell service, then the operator will always continue providing the macrocell service (with or without the femtocell service). Furthermore, we study the impact of operational cost, limited coverage, and spatial reuse on femtocell service provision. As the operational cost increases, fewer users are served by femtocell service and the operator's profit decreases. When the femtocell service has limited spatial coverage, the operator always provides the macrocell service beside the femtocell service. However, when the coverage is high or the total resource is low, the operator will set the prices such that all users who can access femtocell will choose to use the femtocell service only. Finally, spatial reuse of spectrum will increase the efficiency of femtocell services and gives the operator more incentives to allocate spectrum to femtocells.

This paper analyzes and compares different incentive mechanisms for a client to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. Data acquisition from a large number of users is essential to build a rich database and support emerging location-based services. We propose a reward-based collaboration mechanism, where the client announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users willing to collaborate. We show that if the client knows the users' collaboration costs, then he can choose to involve only users with the lowest costs by offering a small total reward. However, if the client does not know users' private cost information, then he needs to offer a larger total reward to attract enough collaborators. Users will benefit from knowing their costs before the data acquisition. Distributed computing aims to solve computational intensive problems in a distributed and inexpensive fashion. We study how the client can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the client will involve a user type as long as the client's preference for that type outweighs the corresponding cost. All collaborators achieve a zero payoff in this case. But if the client does not know users' private cost information, he will conservatively target at a smaller group of efficient users with small costs. He has to give most benefits to the collaborators, and a collaborator's payoff increases in his computing efficiency.

Collaborative spectrum sensing is vulnerable to data falsification attacks, where malicious secondary users (attackers) submit manipulated sensing reports to mislead the fusion center's decision on spectrum occupancy. This paper considers a challenging attack scenario, where multiple attackers cooperatively maximize their aggregate spectrum utilization. Without attack-prevention mechanisms, we show that honest secondary users (SUs) are unable to opportunistically transmit over the licensed spectrum and may even get penalized for collisions caused by attackers. To prevent such attacks, we propose two attack-prevention mechanisms with direct and indirect punishments. Our key idea is to identify collisions with the primary user (PU) that should not happen if all SUs follow the fusion center's decision. Unlike prior work, the proposed simple mechanisms do not require the fusion center to identify and exclude attackers. The direct punishment can effectively prevent all attackers from behaving maliciously. The indirect punishment is easier to implement and can prevent attacks when the attackers care enough about their long-term reward.

This paper presents a comprehensive analytical study of two competitive secondary operators' investment (i.e., spectrum leasing) and pricing strategies, taking into account operators' heterogeneity in leasing costs and users' heterogeneity in transmission power and channel conditions. We model the interactions between operators and users as a three-stage dynamic game, where operators simultaneously make spectrum leasing decisions in Stage I, and pricing decisions in Stage II, and then users make purchase decisions in Stage III. Using backward induction, we are able to completely characterize the dynamic game's equilibria. We show that both operators' investment and pricing equilibrium decisions process interesting threshold properties. For example, when the two operators' leasing costs are close, both operators will lease positive spectrum. Otherwise, one operator will choose not to lease and the other operator becomes the monopolist. For pricing, a positive pure strategy equilibrium exists only when the total spectrum investment of both operators is less than a threshold. Moreover, two operators always choose the same equilibrium price despite their heterogeneity in leasing costs. Each user fairly achieves the same service quality in terms of signal-to-noise ratio (SNR) at the equilibrium, and the obtained predictable payoff is linear in its transmission power and channel gain. We also compare the duopoly equilibrium with the coordinated case where two operators cooperate to maximize their total profit. We show that the maximum loss of total profit due to operators' competition is no larger than 25 percent. The users, however, always benefit from operators' competition in terms of their payoffs. We show that most of these insights are robust in the general SNR regime.

This paper studies the optimal investment and pricing decisions of a cognitive mobile virtual network operator (C-MVNO) under spectrum supply uncertainty. Compared with a traditional MVNO who often leases spectrum via long-term contracts, a C-MVNO can acquire spectrum dynamically in short-term by both sensing the empty “spectrum holes” of licensed bands and dynamically leasing from the spectrum owner. As a result, a C-MVNO can make flexible investment and pricing decisions to match demands of the secondary unlicensed users. Compared to dynamic spectrum leasing, spectrum sensing is typically cheaper, but the obtained useful spectrum amount is random due to primary licensed users' stochastic traffic. The C-MVNO needs to determine the optimal amounts of spectrum sensing and leasing by evaluating the trade-off between cost and uncertainty. The C-MVNO also needs to determine the optimal price to sell the spectrum to the secondary unlicensed users, taking into account wireless heterogeneity of users such as different maximum transmission power levels and channel gains. We model and analyze the interactions between the C-MVNO and secondary unlicensed users as a Stackelberg game. We show several interesting properties of the network equilibrium, including threshold structures of the optimal investment and pricing decisions, the independence of the optimal price on users' wireless characteristics, and guaranteed fair and predictable QoS among users. We prove that these properties hold for general SNR regime and general continuous distributions of sensing uncertainty. We show that spectrum sensing can significantly improve the C-MVNO's expected profit and users' payoffs.

This paper presents the first analytical study of optimal investment and pricing decisions of a cognitive mobile virtual network operator (C-MVNO) under spectrum supply uncertainty. Compared with a traditional MVNO who only obtains spectrum by long-term leasing contracts, a C-MVNO can acquire short-term spectrum by both sensing the empty "spectrum holes" of licensed bands and dynamically leasing from the spectrum owner. As a result, a C-MVNO can make flexible investment and pricing decisions to match the current demands of the secondary unlicensed users. Spectrum sensing is typically cheaper than dynamic spectrum leasing, but the obtained useful spectrum amount is random due to primary licensed users' stochastic traffic. The CMVNO needs to determine the optimal amounts of sensing and leasing spectrum, considering the trade-offs between cost and uncertainty. The C-MVNO also needs to determine the optimal retail price to sell the spectrum to the secondary unlicensed users, taking into account wireless heterogeneity of users such as different maximum transmission power levels and channel gains. We model and analyze these decisions and the interactions between the C-MVNO and secondary users as a multi-stage Stackelberg game. We show several interesting properties of the network equilibrium, such as threshold structures of the optimal investment and pricing decisions, independence between the optimal price and users' wireless characteristics, and fair and predictable spectrum allocations to the users. Compared with the traditional MVNO, spectrum sensing can significantly improve the C-MVNO's expected profit and users' payoffs.