Wireless Network Pricing

Huang, Jianwei; Gao, Lin

doi:10.2200/s00505ed1v01y201305cnt013

Cited by 53 publications

(25 citation statements)

References 66 publications

(129 reference statements)

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“…We show this process in the policy development phase in Algorithm 1, which involves the previous three propositions (Tables IV, V, and Equation (12)). More specifically, the algorithm iteratively computes the prices Compute r L⇤ l (n), r L⇤ h (n) and V L⇤ n by (12). 8: Then r ⇤ l (n), r ⇤ h (n) arg max{V H⇤ n , V M ⇤ n , V L⇤ n }.…”

Section: Optimal Dynamic Pricing and Admission Policiesmentioning

confidence: 99%

Joint spectrum pricing and admission control for heterogeneous secondary users

Jiang

Duan

Huang

2014

2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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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.

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Section: Optimal Dynamic Pricing and Admission Policiesmentioning

confidence: 99%

Joint spectrum pricing and admission control for heterogeneous secondary users

Jiang

Duan

Huang

2014

2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

Self Cite

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“…Pricing is a key tool to allocate resources to the users who value them the most, thus controlling congestion in resource competition cases [31], [32]. However, only a small body of works have studied MEC from an economics viewpoint.…”

Section: A Related Work and Main Contributions Of This Papermentioning

confidence: 99%

Learning-Based Pricing for Privacy-Preserving Job Offloading in Mobile Edge Computing

Siew

Quek

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Mobile edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by selfish mobile users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose learning-based pricing mechanisms where no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency.

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“…See Chiang et al [2010], Sen et al [2013], and Huang and Gao [2013] for surveys of pricing in broadband/wireless networks. In particular, the network utility maximization approach [Kelly 1997] has been very influential.…”

Section: Introductionmentioning

confidence: 99%

Understanding Quota Dynamics in Wireless Networks

Andrews

Bruns

Doğru

et al. 2014

ACM Trans. Internet Technol.

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In designing new service plans, network service providers need to understand how consumption of voice or data service will change in response to pricing signals. It is difficult to acquire such information from customer usage data because voice minutes and data bandwidth are typically sold in the form of large quotas. We address this issue by studying how end-users consume their quotas, both in a prepaid setting (where users pay in advance and refill as needed) and a postpaid setting (where users pay each month for a fixed amount of quota). Our presentation has three main parts. In the first we present data on quota usage for prepaid voice/text services and show that users reduce their voice usage when their balances become low. Moreover, when balances are low there is a tendency to shift from voice to SMS. In the second part, we provide descriptive models of both prepaid and postpaid services. The main feature of these models is that there is a background level of potential demand and the rate at which this potential demand is realized depends on the amount of quota balance available. In the third part, we propose utility maximizing models that can account for this type of behavior. In the prepaid case the main feature of the model is a discount function that represents the perceived cost to the user of a quota refill that will occur sometime in the future. In the postpaid case, where the end-user is attempting to get the maximum amount of utility from his monthly quota, we present a dynamic programming formulation in which utility functions are time varying and not known to the user in advance.

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Wireless Network Pricing

Cited by 53 publications

References 66 publications

Joint spectrum pricing and admission control for heterogeneous secondary users

Joint spectrum pricing and admission control for heterogeneous secondary users

Learning-Based Pricing for Privacy-Preserving Job Offloading in Mobile Edge Computing

Understanding Quota Dynamics in Wireless Networks

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