arXiv subjects

Lingjie Duan

Publications and source records attributed to Lingjie Duan.

At least 55 records · Page 3Linked to original sources

arXiv2022

Online Pricing Incentive to Sample Fresh Information

Today mobile users such as drivers are invited by content providers (e.g., Tripadvisor) to sample fresh information of diverse paths to control the age of information (AoI). However, selfish drivers prefer to travel through the shortest path instead of the others with extra costs in time and gas. To motivate drivers to route and sample diverse paths, this paper is the first to propose online pricing for a provider to economically reward drivers for diverse routing and control the actual AoI dynamics over time and spatial path domains. This online pricing optimization problem should be solved without knowing drivers' costs and even arrivals, and is intractable due to the curse of dimensionality in both time and space. If there is only one non-shortest path, we leverage the Markov decision process (MDP) techniques to analyze the problem. Accordingly, we design a linear-time algorithm for returning optimal online pricing, where a higher pricing reward is needed for a larger AoI. If there are a number of non-shortest paths, we prove that pricing one path at a time is optimal, yet it is not optimal to choose the path with the largest current AoI. Then we propose a new backward-clustered computation method and develop an approximation algorithm to alternate different paths to price over time. Perhaps surprisingly, our analysis of approximation ratio suggests that our algorithm's performance approaches closer to optimum given more paths.

eess.SY↗

arXiv2022

To Help or Disturb: Introduction of Crowdsourced WiFi to 5G Networks

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 purposely lower his price to encourage users to add-on WiFi to offload, and he benefits from the introduction of crowdsourced WiFi. However, 5G operator with mild congestion tends to charge users more and all the users' payoffs may decrease.

cs.NI↗

arXiv2022

Dynamic Pricing for Client Recruitment in Federated Learning

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 the robustness of our solution against estimation error of clients' data sizes and run numerical experiments to validate our conclusion.

cs.GT↗

arXiv2021

Multi-UAV Cooperative Trajectory for Servicing Dynamic Demands and Charging Battery

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing their demands locally. 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 is unaddressed in the literature. To our best knowledge, this paper is the first to design and analyze cooperative path planning algorithms of a large UAV swarm for optimally servicing many spatial locations, where ground users' demands are released dynamically in the long time horizon. 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, which is only polynomial with respect to both the numbers of spatial locations and user demands. 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, which is proved to obviously outperform the traditional approach of partitioning UAVs to serve different location clusters separately. 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. Despite of the problem difficulty, for the optimal solution, 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.

cs.NI↗

arXiv2021

Optimal UAV Hitching on Ground Vehicles

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.

cs.NI↗

arXiv2021

Online Ride-Hitching in UAV Travelling

The unmanned aerial vehicle (UAV) has emerged as a promising solution to provide delivery and other mobile services to customers rapidly, yet it drains its stored energy quickly when travelling on the way and (even if solar-powered) it takes time for charging power on the way before reaching the destination. To address this issue, existing works focus more on UAV's path planning with designated system vehicles providing charging service. However, in some emergency cases and rural areas where system vehicles are not available, public trucks can provide more feasible and cost-saving services and hence a silver lining. In this paper, we explore how a single UAV can save flying distance by exploiting public trucks, to minimize the travel time of the UAV. We give the first theoretical work studying online algorithms for the problem, which guarantees a worst-case performance. We first consider the offline problem knowing future truck trip information far ahead of time. By delicately transforming the problem into a graph satisfying both time and power constraints, we present a shortest-path algorithm that outputs the optimal solution of the problem. Then, we proceed to the online setting where trucks appear in real-time and only inform the UAV of their trip information some certain time $Δt$ beforehand. As a benchmark, we propose a well-constructed lower bound that an online algorithm could achieve. We propose an online algorithm MyopicHitching that greedily takes truck trips and an improved algorithm $Δt$-Adaptive that further tolerates a waiting time in taking a ride. Our theoretical analysis shows that $Δt$-Adaptive is asymptotically optimal in the sense that its ratio approaches the proposed lower bounds as $Δt$ increases.

cs.DS↗

arXiv2021

Average-Case Analysis of Greedy Matching for D2D Resource Sharing

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 Erdos-Renyi 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.

cs.DC↗

arXiv2021

Dynamic Pricing and Mean Field Analysis for Controlling Age of Information

Today many mobile users in various zones are invited to sense and send back real-time useful information (e.g., traffic observation and sensor data) 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 the 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 over time, 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, by providing the steady-state analysis for an infinite time horizon, we show that the pricing scheme (though in closed-form) can be further simplified to an $\varepsilon$-optimal version without recursive computing over time. Finally, 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.

eess.SY↗

arXiv2021

Double-IRS Aided MIMO Communication under LoS Channels: Capacity Maximization and Scaling

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 passive beamforming 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.

cs.IT↗

arXiv2020

Incentive Mechanism Design for Distributed Coded Machine Learning

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 (Maximum Distance Separable) 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.

cs.GT↗

arXiv2020

Online Maximum $k$-Interval Coverage Problem

We study the online maximum coverage problem on a line, in which, given an online sequence of sub-intervals (which may intersect among each other) of a target large interval and an integer $k$, we aim to select at most $k$ of the sub-intervals such that the total covered length of the target interval is maximized. The decision to accept or reject each sub-interval is made immediately and irrevocably (no preemption) right at the release timestamp of the sub-interval. We comprehensively study different settings of this problem regarding both the length of a released sub-interval and the total number of released sub-intervals. We first present lower bounds on the competitive ratio for the settings concerned in this paper, respectively. For the offline problem where the sequence of all the released sub-intervals is known in advance to the decision-maker, we propose a dynamic-programming-based optimal approach as the benchmark. For the online problem, we first propose a single-threshold-based deterministic algorithm SOA by adding a sub-interval if the added length exceeds a certain threshold, achieving competitive ratios close to the lower bounds, respectively. Then, we extend to a double-thresholds-based algorithm DOA, by using the first threshold for exploration and the second threshold (larger than the first one) for exploitation. With the two thresholds solved by our proposed program, we show that DOA improves SOA in the worst-case performance. Moreover, we prove that a deterministic algorithm that accepts sub-intervals by multi non-increasing thresholds cannot outperform even SOA.

cs.DS↗

arXiv2020

Pricing-Driven Service Caching and Task Offloading in Mobile Edge Computing

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 paper, we propose an MEC service pricing scheme to coordinate with the service caching decisions and control WDs' task offloading behavior. 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. 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 study both the uniform and differentiated pricing schemes. For differentiated pricing, we prove that the same price should be charged to the cached programs of the same workload.

cs.NI↗

arXiv2020

Optimal pricing for peer-to-peer sharing with network externalities

In this paper, we analyse how a peer-to-peer sharing platform should price its service (when imagined as an excludable public good) to maximize profit, when each user's participation adds value to the platform service by creating a positive externality to other participants. To characterize network externalities as a function of the number of participants, we consider different bounded and unbounded user utility models. The bounded utility model fits many infrastructure sharing applications with bounded network value, in which complete coverage has a finite user valuation (e.g., WiFi or hotspot). The unbounded utility model fits the large scale data sharing and explosion in social media, where it is expected that the network value follows Metcalfe's or Zipf's law. For both models, we analyze the optimal pricing schemes to select heterogeneous users in the platform 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 provable PoI bounds for different utility models. We show that the PoI=2 for the bounded utility model, meaning that just half of profit is lost, whereas the PoI>=2 for the unbounded utility model and increases as for a less concave utility function. We also show that the complicated differentiated pricing scheme which is optimal under incomplete user information, can be replaced by a single 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 contributing no externalities, and show that the platform may charge zero price to the original group of users in order to attract the pure user group.

cs.GT↗

arXiv2020

Learning Large Electrical Loads via Flexible Contracts with Commitment

Large electricity customers (e.g., large data centers) can exhibit huge and variable electricity demands, which poses significant challenges for the electricity suppliers to plan for sufficient capacity. Thus, it is desirable to design incentive and coordination mechanisms between the customers and the supplier to lower the capacity cost. This paper proposes a novel scheme based on flexible contracts. Unlike existing demand-side management schemes in the literature, a flexible contract leads to information revelation. That is, a customer committing to a flexible contract reveals valuable information about its future demand to the supplier. Such information revelation allows the customers and the supplier to share the risk of future demand uncertainty. On the other hand, the customer will still retain its autonomy in operation. We address two key challenges for the design of optimal flexible contracts: i) the contract design is a non-convex optimization problem and is intractable for a large number of customer types, and ii) the design should be robust to unexpected or adverse responses of the customers, i.e., a customer facing more than one contract yielding the same benefit may choose the contract less favorable to the supplier. We address these challenges by proposing sub-optimal contracts of low computational complexity that can achieve a provable fraction of the performance gain under the global optimum.

cs.GT↗

arXiv2020

Distributed Double Auctions for Large-Scale Device-to-Device Resource Trading

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.

cs.GT↗

arXiv2020

Cooperative Double-IRS Aided Communication: Beamforming Design and Power Scaling

Intelligent reflecting surface (IRS) is a promising technology to support high performance wireless communication. By adaptively configuring the reflection amplitude and/or phase of each passive reflecting element on it, the IRS can reshape the electromagnetic environment in favour of signal transmission. This letter advances the existing research by proposing and analyzing a double-IRS aided wireless communication system. Under the reasonable assumption that the reflection channel from IRS 1 to IRS 2 is of rank 1 (e.g., line-of-sight channel), we propose a joint passive beamforming design for the two IRSs. Based on this, we show that deploying two cooperative IRSs with in total K elements can yield a power gain of order O(K^4), which greatly outperforms the case of deploying one traditional IRS with a power gain of order O(K^2). Our simulation results validate that the performance of deploying two cooperative IRSs is significantly better than that of deploying one IRS given a sufficient total number of IRS elements. We also extend our line-of-sight channel model to show how different channel models affect the performance of the double-IRS aided wireless communication system.

cs.IT↗

arXiv2020

Towards Reliable UAV Swarm Communication in D2D-Enhanced Cellular Network

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, to combat the strong interference from occupied GBSs. In Phase II, all the UAVs that have decoded the common control 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 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 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 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 system design.

cs.IT↗

arXiv2019

Strategic Learning Approach for Deploying UAV-provided Wireless Services

Unmanned Aerial Vehicle (UAV) have emerged as a promising technique to rapidly provide wireless services to a group of mobile users simultaneously. The paper 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.

cs.GT↗