arXiv subjects

Jianwei Huang

Publications and source records attributed to Jianwei Huang.

At least 145 records · Page 8Linked to original sources

arXiv2016

Spectrum Investment under Uncertainty: A Behavioral Economics Perspective

In this paper, we study a virtual wireless operator's spectrum investment problem under spectrum supply uncertainty. To obtain enough spectrum resources to meet its customer demands, the virtual operator can either sense for the temporarily unused spectrum in a licensed band, or lease spectrum from a spectrum owner. Sensing is usually cheaper than leasing, but the amount of available spectrum obtained by sensing is uncertain due to the primary users' activities in the licensed band. Previous studies on spectrum investment problems mainly considered the expected profit maximization problem of a risk-neutral operator based on the expected utility theory (EUT). In reality, however, an operator's decision is influenced by not only the consideration of expected profit maximization, but also the level of its risk preference. To capture this tradeoff between these two considerations, we analyze the operator's optimal decision problem using the prospect theory from behavioral economics, which includes EUT as a special case. The sensing and leasing optimal problem under prospect theory is non-convex and challenging to solve. Nevertheless, by exploiting the unimodal structure of the problem, we are able to compute the unique global optimal solution. We show that comparing to an EUT operator, both the risk-averse and risk-seeking operator achieve a smaller expected profit. On the other hand, a risk-averse operator can guarantee a larger minimum possible profit, while a risk-seeking operator can achieve a larger maximum possible profit. Furthermore, the tradeoff between the expected profit and the minimum possible profit for a risk-averse operator is better when the sensing cost increases, while the tradeoff between the expected profit and the maximum possible profit for a risk-seeking operator is better when the sensing cost decreases.

cs.NI↗

arXiv2016

Cooperative Wi-Fi Deployment: A One-to-Many Bargaining Framework

We study the cooperation of the mobile network operator (MNO) and the venue owners (VOs) on the public Wi-Fi deployment. We consider a one-to-many bargaining framework, where the MNO bargains with VOs sequentially to determine where to deploy Wi-Fi and how much to pay. Taking into account the negative externalities among different steps of bargaining, we analyze the following two cases: for the exogenous bargaining sequence case, we compute the optimal bargaining solution on the cooperation decisions and payments under a predetermined bargaining sequence; for the endogenous bargaining sequence case, the MNO decides the bargaining sequence to maximize its payoff. Through exploring the structural property of the optimal bargaining sequence, we design a low-complexity Optimal VO Bargaining Sequencing (OVBS) algorithm to search the optimal sequence. More specifically, we categorize the VOs into three types based on the impact of the Wi-Fi deployment at their venues, and show that it is optimal for the MNO to bargain with these three types of VOs sequentially. Numerical results show that compared with the random and worst bargaining sequences, the optimal bargaining sequence improves the MNO's payoff by up to 14.8% and 45.3%, respectively.

cs.GT↗

arXiv2016

Combining Spot and Futures Markets: A Hybrid Market Approach to Dynamic Spectrum Access

Dynamic spectrum access is a new paradigm of secondary spectrum utilization and sharing. It allows unlicensed secondary users (SUs) to exploit opportunistically the under-utilized licensed spectrum. Market mechanism is a widely-used promising means to regulate the consuming behaviours of users and, hence, achieves the efficient allocation and consumption of limited resources. In this paper, we propose and study a hybrid secondary spectrum market consisting of both the futures market and the spot market, in which SUs (buyers) purchase under-utilized licensed spectrum from a spectrum regulator, either through predefined contracts via the futures market, or through spot transactions via the spot market. We focus on the optimal spectrum allocation among SUs in an exogenous hybrid market that maximizes the secondary spectrum utilization efficiency. The problem is challenging due to the stochasticity and asymmetry of network information. To solve this problem, we first derive an off-line optimal allocation policy that maximizes the ex-ante expected spectrum utilization efficiency based on the stochastic distribution of network information. We then propose an on-line VickreyCClarkeCGroves (VCG) auction that determines the real-time allocation and pricing of every spectrum based on the realized network information and the pre-derived off-line policy. We further show that with the spatial frequency reuse, the proposed VCG auction is NP-hard; hence, it is not suitable for on-line implementation, especially in a large-scale market. To this end, we propose a heuristics approach based on an on-line VCG-like mechanism with polynomial-time complexity, and further characterize the corresponding performance loss bound analytically. We finally provide extensive numerical results to evaluate the performance of the proposed solutions.

cs.NI↗

arXiv2016

Spectroscopic Evidence of Type II Weyl Semimetal State in WTe2

Quantum topological materials, exemplified by topological insulators, three-dimensional Dirac semimetals and Weyl semimetals, have attracted much attention recently because of their unique electronic structure and physical properties. Very lately it is proposed that the three-dimensional Weyl semimetals can be further classified into two types. In the type I Weyl semimetals, a topologically protected linear crossing of two bands, i.e., a Weyl point, occurs at the Fermi level resulting in a point-like Fermi surface. In the type II Weyl semimetals, the Weyl point emerges from a contact of an electron and a hole pocket at the boundary resulting in a highly tilted Weyl cone. In type II Weyl semimetals, the Lorentz invariance is violated and a fundamentally new kind of Weyl Fermions is produced that leads to new physical properties. WTe2 is interesting because it exhibits anomalously large magnetoresistance. It has ignited a new excitement because it is proposed to be the first candidate of realizing type II Weyl Fermions. Here we report our angle-resolved photoemission (ARPES) evidence on identifying the type II Weyl Fermion state in WTe2. By utilizing our latest generation laser-based ARPES system with superior energy and momentum resolutions, we have revealed a full picture on the electronic structure of WTe2. Clear surface state has been identified and its connection with the bulk electronic states in the momentum and energy space shows a good agreement with the calculated band structures with the type II Weyl states. Our results provide spectroscopic evidence on the observation of type II Weyl states in WTe2. It has laid a foundation for further exploration of novel phenomena and physical properties in the type II Weyl semimetals.

cond-mat.mes-hall↗

arXiv2016

Two-sided Matching Based Cooperative Spectrum Sharing

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 every PU 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 Robust equilibrium 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% in the simulations).

cs.GT↗

arXiv2016

Optimal Pricing and Admission Control for Heterogeneous Secondary Users

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.

cs.NI↗

arXiv2016

Cooperative Planning of Renewable Generations for Interconnected Microgrids

We study the renewable energy generations in Hong Kong based on realistic meteorological data, and find that different renewable sources exhibit diverse time-varying and location-dependent profiles. To efficiently explore and utilize the diverse renewable energy generations, we propose a theoretical framework for the cooperative planning of renewable generations in a system of interconnected microgrids. The cooperative framework considers the self-interested behaviors of microgrids, and incorporates both their long-term investment costs and short-term operational costs over the planning horizon. Specifically, interconnected microgrids jointly decide where and how much to deploy renewable energy generations, and how to split the associated investment cost. We show that the cooperative framework minimizes the overall system cost. We also design a fair cost sharing method based on Nash bargaining to incentivize cooperative planning, such that all microgrids will benefit from cooperative planning. Using realistic data obtained from the Hong Kong observatory, we validate the cooperative planning framework, and demonstrate that all microgrids benefit through the cooperation, and the overall system cost is reduced by 35.9% compared to the noncooperative planning benchmark.

cs.GT↗

arXiv2016

Electronic Evidence for Type II Weyl Semimetal State in MoTe2

Topological quantum materials, including topological insulators and superconductors, Dirac semimetals and Weyl semimetals, have attracted much attention recently for their unique electronic structure, spin texture and physical properties. Very lately, a new type of Weyl semimetals has been proposed where the Weyl Fermions emerge at the boundary between electron and hole pockets in a new phase of matter, which is distinct from the standard type I Weyl semimetals with a point-like Fermi surface. The Weyl cone in this type II semimetals is strongly tilted and the related Fermi surface undergos a Lifshitz transition, giving rise to a new kind of chiral anomaly and other new physics. MoTe2 is proposed to be a candidate of a type II Weyl semimetal; the sensitivity of its topological state to lattice constants and correlation also makes it an ideal platform to explore possible topological phase transitions. By performing laser-based angle-resolved photoemission (ARPES) measurements with unprecedentedly high resolution, we have uncovered electronic evidence of type II semimetal state in MoTe2. We have established a full picture of the bulk electronic states and surface state for MoTe2 that are consistent with the band structure calculations. A single branch of surface state is identified that connects bulk hole pockets and bulk electron pockets. Detailed temperature-dependent ARPES measurements show high intensity spot-like features that is ~40 meV above the Fermi level and is close to the momentum space consistent with the theoretical expectation of the type II Weyl points. Our results constitute electronic evidence on the nature of the Weyl semimetal state that favors the presence of two sets of type II Weyl points in MoTe2.

cond-mat.mtrl-sci↗

arXiv2016

An Integrated Spectrum and Information Market for Green Cognitive Communications

A database-assisted TV white space network can achieve the goal of green cognitive communication by effectively reducing the energy consumption in cognitive communications. The success of such a novel network relies on a proper business model that provides incentives for all parties involved. In this paper, we propose an integrated spectrum and information market for a database-assisted TV white space network, where the geo-location database serves as both the spectrum market platform and the information market platform. We study the interactions among the database, the spectrum licensee, and unlicensed users by modelling the system as a three-stage sequential decision process. In Stage I, the database and the licensee negotiate regarding the commission for the licensee to use the spectrum market platform. In Stage II, the database and the licensee compete for selling information or channels to unlicensed users. In Stage III, unlicensed users determine whether they should buy exclusive usage right of licensed channels from the licensee or information regarding unlicensed channels from the database. Analyzing such a three-stage model is challenging due to the co-existence of both positive and negative network externalities in the information market. Despite of this, we are able to characterize how the network externalities affect the equilibrium behaviors of all parties involved. We analytically show that in this integrated market, the licensee can never get a market share more than half. Our numerical results further show that the proposed integrated market can improve the network profit up to 87%, compared with a pure information market.

cs.GT↗

arXiv2016

A Game-Theoretic Analysis of User Behaviors in Crowdsourced Wireless Community Networks

A crowdsourced wireless community network can effectively alleviate the limited coverage issue of Wi-Fi access points (APs), by encouraging individuals (users) to share their private residential Wi-Fi APs with each other. This paper presents the first study on the users' joint membership selection and network access problem in such a network. Specifically, we formulate the problem as a two-stage dynamic game: Stage I corresponds to a membership selection game, in which each user chooses his membership type; Stage II corresponds to a set of network access games, in each of which each user decides his WiFi connection time on the AP at his current location. We analyze the Subgame Perfect Equilibrium (SPE) systematically, and study whether and how best response dynamics can reach the equilibrium. Through numerical studies, we further explore how the equilibrium changes with the users' mobility patterns and network access evaluations. We show that a user with a more popular home location, a smaller travel time, or a smaller network access evaluation is more likely to choose a specific type of membership called Bill. We further demonstrate how the network operator can optimize its pricing and incentive mechanism based on the game equilibrium analysis in this work.

cs.GT↗

arXiv2016

Parametric Prediction from Parametric Agents

We consider a problem of prediction based on opinions elicited from heterogeneous rational agents with private information. Making an accurate prediction with a minimal cost requires a joint design of the incentive mechanism and the prediction algorithm. Such a problem lies at the nexus of statistical learning theory and game theory, and arises in many domains such as consumer surveys and mobile crowdsourcing. In order to elicit heterogeneous agents' private information and incentivize agents with different capabilities to act in the principal's best interest, we design an optimal joint incentive mechanism and prediction algorithm called COPE (COst and Prediction Elicitation), the analysis of which offers several valuable engineering insights. First, when the costs incurred by the agents are linear in the exerted effort, COPE corresponds to a "crowd contending" mechanism, where the principal only employs the agent with the highest capability. Second, when the costs are quadratic, COPE corresponds to a "crowd-sourcing" mechanism that employs multiple agents with different capabilities at the same time. Numerical simulations show that COPE improves the principal's profit and the network profit significantly (larger than 30% in our simulations), comparing to those mechanisms that assume all agents have equal capabilities.

cs.GT↗

arXiv2016

HySIM: A Hybrid Spectrum and Information Market for TV White Space Networks

We propose a hybrid spectrum and information market for a database-assisted TV white space network, where the geo-location database serves as both a spectrum market platform and an information market platform. We study the inter- actions among the database operator, the spectrum licensee, and unlicensed users systematically, using a three-layer hierarchical model. In Layer I, the database and the licensee negotiate the commission fee that the licensee pays for using the spectrum market platform. In Layer II, the database and the licensee compete for selling information or channels to unlicensed users. In Layer III, unlicensed users determine whether they should buy the exclusive usage right of licensed channels from the licensee, or the information regarding unlicensed channels from the database. Analyzing such a three-layer model is challenging due to the co-existence of both positive and negative network externalities in the information market. We characterize how the network externalities affect the equilibrium behaviours of all parties involved. Our numerical results show that the proposed hybrid market can improve the network profit up to 87%, compared with a pure information market. Meanwhile, the achieved network profit is very close to the coordinated benchmark solution (the gap is less than 4% in our simulation).

cs.GT↗

arXiv2016

Providing Long-Term Participation Incentive in Participatory Sensing

Providing an adequate long-term participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under (current) information asymmetry. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% to 90%) and social performance (e.g., increasing the social welfare by 15% to 80%).

cs.GT↗

arXiv2016

Electronic Evidence of Temperature-Induced Lifshitz Transition and Topological Nature in ZrTe5

The topological materials have attracted much attention recently. While three-dimensional topological insulators are becoming abundant, two-dimensional topological insulators remain rare, particularly in natural materials. ZrTe5 has host a long-standing puzzle on its anomalous transport properties; its underlying origin remains elusive. Lately, ZrTe5 has ignited renewed interest because it is predicted that single-layer ZrTe5 is a two-dimensional topological insulator and there is possibly a topological phase transition in bulk ZrTe5. However, the topological nature of ZrTe5 is under debate as some experiments point to its being a three-dimensional or quasi-two-dimensional Dirac semimetal. Here we report high-resolution laser-based angle-resolved photoemission measurements on ZrTe5. The electronic property of ZrTe5 is dominated by two branches of nearly-linear-dispersion bands at the Brillouin zone center. These two bands are separated by an energy gap that decreases with decreasing temperature but persists down to the lowest temperature we measured (~2 K). The overall electronic structure exhibits a dramatic temperature dependence; it evolves from a p-type semimetal with a hole-like Fermi pocket at high temperature, to a semiconductor around ~135 K where its resistivity exhibits a peak, to an n-type semimetal with an electron-like Fermi pocket at low temperature. These results indicate a clear electronic evidence of the temperature-induced Lifshitz transition in ZrTe5. They provide a natural understanding on the underlying origin of the resistivity anomaly at ~135 K and its associated reversal of the charge carrier type. Our observations also provide key information on deciphering the topological nature of ZrTe5 and possible temperature-induced topological phase transition.

cond-mat.mtrl-sci↗

arXiv2015

Power-Delay Tradeoff with Predictive Scheduling in Integrated Cellular and Wi-Fi Networks

The explosive growth of global mobile traffic has lead to a rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. The problem is particularly challenging due to the two-timescale operations for the network selection (large timescale) and subchannel and power allocation (small timescale). Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm. The ENSRA algorithm yields a power consumption within O(1/V) bound of the optimal value, and guarantees an O(V) traffic delay for any positive control parameter V. Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20~30% under the same power consumption.

cs.NI↗

arXiv2015

Competitive Charging Station Pricing for Plug-in Electric Vehicles

This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two competing charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.

cs.GT↗

arXiv2015

Time and Location Aware Mobile Data Pricing

Mobile users' correlated mobility and data consumption patterns often lead to severe cellular network congestion in peak hours and hot spots. This paper presents an optimal design of time and location aware mobile data pricing, which incentivizes users to smooth traffic and reduce network congestion. We derive the optimal pricing scheme through analyzing a two-stage decision process, where the operator determines the time and location aware prices by minimizing his total cost in Stage I, and each mobile user schedules his mobile traffic by maximizing his payoff (i.e., utility minus payment) in Stage II. We formulate the two-stage decision problem as a bilevel optimization problem, and propose a derivative-free algorithm to solve the problem for any increasing concave user utility functions. We further develop low complexity algorithms for the commonly used logarithmic and linear utility functions. The optimal pricing scheme ensures a win-win situation for the operator and users. Simulations show that the operator can reduce the cost by up to 97.52% in the logarithmic utility case and 98.70% in the linear utility case, and users can increase their payoff by up to 79.69% and 106.10% for the two types of utilities, respectively, comparing with a time and location independent pricing benchmark. Our study suggests that the operator should provide price discounts at less crowded time slots and locations, and the discounts need to be significant when the operator's cost of provisioning excessive traffic is high or users' willingness to delay traffic is low.

cs.NI↗

arXiv2015

Joint Investment and Operation of Microgrid

In this paper, we propose a theoretical framework for the joint optimization of investment and operation of a microgrid, taking the impact of energy storage, renewable energy integration, and demand response into consideration. We first study the renewable energy generations in Hong kong, and identify the potential benefit of mixed deployment of solar and wind energy generations. Then we model the joint investment and operation as a two-period stochastic programming program. In period-1, the microgrid operator makes the optimal investment decisions on the capacities of solar power generation, wind power generation, and energy storage. In period-2, the operator coordinates the power supply and demand in the microgrid to minimize the operating cost. We design a decentralized algorithm for computing the optimal pricing and power consumption in period-2, based on which we solve the optimal investment problem in period-1. We also study the impact of prediction error of renewable energy generation on the portfolio investment using robust optimization framework. Using realistic meteorological data obtained from the Hong Kong observatory, we numerically characterize the optimal portfolio investment decisions, optimal day-ahead pricing and power scheduling, and demonstrate the advantage of using mixed renewable energy and demand response in terms of reducing investment cost.

eess.SY↗