Competition, Collusion and Regulation in Dynamic Spectrum Markets

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1 Competition, Collusion and Regulation in Dynamic Spectrum Markets George Iosifidis CERTH and University of Thessaly, Volos, Greece

2 Outline 1 Introduction 2 Double Auction Mechanisms for Spectrum Exchange 3 Competition and Regulation in Wireless Services Markets 4 Incentive Mechanisms for Hierarchical Spectrum Markets 5 Conclusions

3 Growing Data Demand and Spectrum Scarcity Today we are witnessing an unprecendented increase in user demand for ubiquitous and high speed networking. Mobile data traffic is expected to increase 18-times until 2016 (see Ericsson and Cisco reports). The first and most critical step towards satisfying this ever growing demand is to ensure availability of electromagnetic spectrum. Spectrum is the building block of wireless networks. Current Spectrum Policy. Spectrum is allocated in a static and coarse-grained fashion (large geographic areas/long time periods/exclusive use). Artificial Spectrum Scarcity Problem. Spectrum is mostly reserved, it is expensive, and yet underutilized (15%-85%). Spectrum shortage hampers proliferation of wireless networks and services.

4 Dynamic Spectrum Markets A prominent proposed solution for this problem is the liberalization of spectrum markets: Dynamic Spectrum Markets. Spectrum allocation is accomplished in a finer spatio-temporal scale. Spectrum can be resold, leased or exchanged.

5 DS Markets - New Challenges

6 Double Auctions for Spectrum/Rate Exchange Dynamic spectrum (DS) markets will facilitate spectrum and rate exchange among peer network entities. Especially in secondary markets, i.e. among SOs or SUs. Users exchange rate: 3G connection sharing (mobile WiFi hot-spot, Palm Pre smartphone). SOs exchange their idle spectrum in a dynamic fashion. These scenarios have common characteristics: There exist many resource buyers and sellers. Each entity/node has both roles. The interests of the different entities are often conflicting. The transactions are decentralized and (should be) realized almost in real-time fashion. What is a proper market clearing mechanism for these markets? How much spectrum (rate) each node should allocate to others? At what prices the spectrum (rate) should be traded? Our Approach: Devise a dynamic resource allocation and pricing mechanism that clears the market and maximizes social welfare. Explicitly capture the double role of each node and the interdependency between these roles. The market clearing mechanism can be implemented in a decentralized fashion.

7 Competition in Wireless Markets Analysis of operators competition when users (clients) have an alternative - out of the market - option. We model the latter through the neutral operator P 0. Examples: (i) Primary-Secondary operators, (ii) a city with WiFi commercial operators and one municipal WiFi network. (i) How users select operators and how the operators compete for users in the presence of P 0? (ii) Which are the equilibriums (NE) of this market? (iii) Which parameters affect the NE? (iv) Can we intervene and change the NE? We model the users decision making as an evolutionary game, and the competition of the operators as a noncooperative pricing game. The two games are interrelated.

8 Competition in Wireless Markets Operators: each operator has a certain amount of effective spectrum at its disposal which is equally shared among its subscribers. Operators select the subscription prices and compete with each other to attract as many users as possible. Neutral Operator: offers a fixed (non-congestible), free of charge service of low quality U 0. User Strategy: The utility of each user depends on the service it receives and the price it pays. Dissatisfied users are gradually moving to neutral operator. Users have bounded rationality and limited information. They choose operators based on imitation of better strategies (Evolutionary model). We characterize the Nash equilibria (operators prices) wrt U 0 and the spectrum W of each operator. How a regulator can intervene, by chaning either U 0 or W and steer the market to a certain desirable NE. We also consider the case that operators collude and fix their prices (partition function game): Groups of colluding operators compete with each other. Can the regulator deter colluding in advance?

9 Hierarchical Spectrum Allocation In DS markets spectrum channels will be allocated hierarchically. The goal of the state (controller) is the channels to be eventually assigned to operators (primary or secondary) with the highest needs. Channel allocation in each layer is accomplished through auctions (demand is unknown). Controller (CO) allocates the channels to POs. POs use some of them and reallocate the rest to SOs. They want to maximize their revenue. CO and POs have different objectives. Hence there is the (i) conflicting objectives problem and the (i) coordination problem (channels are not assigned properly to the POs). The revenue-maximizing strategy of POs yields inefficient channel allocation. How can the controller induce the POs to improve the efficiency of the channel allocation?

10 Hierarchical Spectrum Allocation We introduce an incentive mechanism that improves channel allocation efficiency. POs are expected to use optimal auctions in order to maximize their (expected) revenue. Optimal auctions are inefficient: (i) SOs do not obtain enough channels, (ii) SOs pay high prices. CO would prefer them to use a different objective (not revenue maximization). Key idea of the mechanism: CO subsidizes channel allocation to the SOs. This requires a feedback loop from the SOs to the Controller. The POs, due to subsidy, run a new type of auction, the β-optimal auction, where β > 0 is the regulation parameter determined by the CO. The proposed incentive scheme improves the efficiency of channel allocation at a small cost in terms of computation and communication complexity.

11 Conclusions and Future Work Internet goes wireless. Mobile data traffic is exploding. There is need for (not-so-expensive) spectrum. Dynamic spectrum markets are expected to improve spectrum utilization and enable the satisfaction of the growing demand for spectrum and associated wireless services. However, market-based mechanisms are not panacea. They should be carefully designed. We identified (some of) the issues that arise in these markets and discussed how they can be addressed. Future work: consider more specific scenarios, tailor the mechanisms to each scenario, take into account more realistic-parameters, e.g. collusion of users, limited information about the market and the operators, etc.

12 Thank you!

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