Admission-based pricing in a competitive wireless bandwidth-on-demand market

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Main developments in next-generation wireless networks

Mobile wireless technologies beyond the currently implemented third generation (3G) are being investigated from multiple perspectives (Berezdivin et al., 2002). The wireless communication industry, including content players and application providers, are interested in understanding the potential behind next-generation networks and are trying to anticipate the potentially disruptive character of technology on current business models. Researchers from the technical departments at universities and other research institutions investigate new concepts for increasing the efficiency of the wireless air interface as well as develop innovative ways of managing wireless resource allocation to enable high-speed, high-quality wireless data transmission. In other fields such as computer science and information management new application and usage scenarios are developed and tested. Yet another research stream investigates the consequences for society and the changes in how humans and commercial entities will communicate over highly available wireless multimedia networks.

Economic theory for network pricing

Economics has become an important and indispensable background for network pricing for various reasons. Network economics, a sub field of general economic theory, has evolved to analyse specific problems related to networks, and, in particular, the effects stemming from network externalities. Positive network externalities relate to the effect that consumers’ willingness-to-pay increases by an increasing number of all consumers subscribing to the same service. Negative externalities, in contrast, arise from the shared consumption of limited network resources. Besides the effect of externalities, most networks share other properties such as very high installation costs (for example, to install a communication network or an electricity grid) but very low production costs (Shapiro and Varian., 1999). Such special properties have a strong influence on the economic analysis and the pricing and market structure.

The Role of Game Theory for Network Pricing

Game theory is a sub-field of economics that studies conflict and cooperation between interdependent agents, which are typical for an environment in which resources are allocated between multiple, independently-acting entities. Game theory provides the tools for structuring and analysing the problem of strategic choice (Turocy and von Stengel, 2001). A game, in this context, is defined as a formal model of an interactive situation, which involves several players. In a cooperative game, groups of players may enforce cooperative behaviour depending on the relative amount of power or knowledge held by the agents. In contrast, noncooperative game theory analyses the strategic choices of players, which make choices only in their own interest. A central assumption in noncooperative game theory is the rational behaviour of the players, which lets them always choose an action that maximises the most preferred outcome. The goal of the analysis is to predict how a game will be played by such rational players.

Categorisation of Research in this Thesis

The work in this thesis concentrates on the development and performance testing of suitable control models for resource allocation in a multi-provider environment. In all models we use pricing as the central element for communicating the network state between the participating entities, namely providers and network users. The matrix shown in Figure 1.3 differentiates the chosen approach from the existing work. Most control models using dynamic pricing focus on the lower row under the assumption that only one provider supplies network resources to network users. We concentrate on models in which network resources are supplied by multiple, independentlyacting providers. We distinguish two cases: (a) the case where multiple customers concurrently compete for network resources from multiple providers and (b), where multiple providers are faced with one customer at a time requesting network resources. While the first case is typical for a situation in which customers with elastic demand can adapt their consumption according to the level of congestion in the network, the latter case applies to an access control scheme in which users arrive in an asynchronous fashion and providers compete for the customer over price.

Delimitations of Scope and Key Assumptions

The above research questions put a strong focus on the economic side of competition rather than on implementing sophisticated resource allocation mechanisms on the technological side. While it has been essential for this research to understand the underlying technological principles, especially in the sense of what will be feasible and what will be infeasible in next-generation wireless networks we put our focus on understanding how technology can be complemented by economic models to control user demand and to reach certain design objectives. In the following we describe the key assumptions taken on the modelling side of user behaviour as well as on the side of modelling of wireless network capacity. When modelling resource allocation in communication networks by means of economic methodologies, many different objectives can be followed.

Contents :

• 1 Introduction
  • 1.1 Introduction
  • 1.1.1 Main developments in next-generation wireless networks
  • 1.1.2 Economic theory for network pricing
  • 1.1.3 The Role of Game Theory for Network Pricing
  • 1.2 Research Motivation
  • 1.3 Research Challenges
  • 1.4 Categorisation of Research in this Thesis
  • 1.5 Thesis Synopsis
• 2 Research Objectives and Methodology
  • 2.1 Introduction
  • 2.2 Research Objectives
  • 2.3 Delimitations of Scope and Key Assumptions
  • 2.4 Contributions of this Research
  • 2.5 Methodology
    • 2.5.1 Description of the applied research methodologies
    • 2.5.2 Definition of the term simulation
    • 2.5.3 Theoretical foundation of simulation as research methodology
• 3 Knowledge Domains and Literature Review
  • 3.1 Introduction
  • 3.2 Aspects of Network Pricing
    • 3.2.1 Terminology
    • 3.2.2 Delimitation of network transport services
    • 3.2.3 Objectives of network pricing
    • 3.2.4 Pricing practices in mobile and wireless communication networks
    • 3.2.5 Feasibility of network pricing
  • 3.3 A Classification Framework for Pricing of Network Transport Services
    • 3.3.1 Classification categories for network pricing
    • 3.3.2 Alternative classification approaches
    • 3.3.3 The wireless pricing time-scale classification framework
  • 3.4 Literature Review
    • 3.4.1 The physical channel time-scale
    • 3.4.2 The packet time-scale
    • 3.4.3 The flow time-scale
    • 3.4.4 The admission time-scale
    • 3.4.5 The subscription time-scale
    • 3.4.6 Literature on pricing for wireless multiple access
  • 3.5 Selection criteria for in-depth analysis
  • 3.6 Chapter Summary
  • 3.7 Chapter Appendix: Technical Background
    • 3.7.1 Multiplexing and data transmission over the wireless channel
    • 3.7.2 General Quality-of-Service architectures
    • 3.7.3 Particularities for providing Quality-of-Service over the air interface
• 4 The PSP auction in a Competitive Wireless Environment
  • 4.1 Introduction
    • 4.1.1 Auctions as market institutions for resource allocation
    • 4.1.2 The PSP auction mechanism in a wireless IP-based environment
    • 4.1.3 Chapter outline
  • 4.2 The PSP auction in a Single-Seller Setting
    • 4.2.1 The PSP auction
    • 4.2.2 Properties of the PSP auction
    • 4.2.3 A simple PSP example
  • 4.3 Truthful Bidding in a Multi-Auction Market
    • 4.3.1 The application of the multi-auction concept to a scenario of competitive wireless access networks
    • 4.3.2 Truthful behaviour with multiple auctions
    • 4.3.3 The BalancedBid bidding strategy
  • 4.4 Alternative Bidding Strategies in a Multi-Auction Market
    • 4.4.1 Short description and basic properties of the alternative bidding strategies
    • 4.4.2 The BidAll bidding strategy
    • 4.4.3 The UtilityBased bidding strategy
    • 4.4.4 The OneActive bidding strategy
    • 4.4.5 The ComplementaryUtility bidding strategy
• 5 Admission-based pricing in a competitive wireless bandwidth-on-demand market
  • 5.1 Introduction
  • 5.2 Revenue Maximisation in a Monopolistic Market for Wireless Resources
    • 5.2.1 Main model assumptions and system parameters
    • 5.2.2 The optimal control model for revenue maximisation
    • 5.2.3 Revenue maximisation under resource constraints for the timestationary case
  • 5.3 A Game-Theoretic Discussion to the Two-Provider Case
    • 5.3.1 The situation as a game of complete information
    • 5.3.2 The situation as a game of incomplete information
    • 5.3.3 Bayesian Nash equilibrium in linear pricing strategies
    • 5.3.4 Bayesian Nash equilibrium in hyperbolic equilibrium pricing strategies
    • 5.3.5 Bayesian Nash equilibrium in symmetric pricing strategies
  • 5.4 A Heuristic Approximation Framework for the Two-Provider Case
    • 5.4.1 User demand and utility
    • 5.4.2 Modelling of the estimators
    • 5.4.3 The approximation procedure
    • 5.4.4 Modelling of the technical admission control function
    • 5.4.5 Statistical analysis of simulation output
  • 5.5 Experimental Results
    • 5.5.1 Parameterisation of the simulation platform
    • 5.5.2 Simulation results for the one-provider, one-cell scenario
• 6 The Simulation Architecture
• 7 Conclusions and Future Research Directions

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Dynamic pricing of wireless network resources in a competitive provider setting