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19
Investment and pricing with spectrum uncertainty: a cognitive operator’s perspective
- IEEE Transactions on Mobile Computing
, 2011
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Wi-Fi 2.0: Price and Quality Competitions of Duopoly Cognitive Radio Wireless Service Providers with Time-varying Spectrum Availability
- In INFOCOM 2011
"... Abstract—The whitespaces (WS) in the legacy spectrum pro-vide new opportunities for the future Wi-Fi-like Internet access, often called Wi-Fi 2.0, since service quality can be greatly enhanced thanks to the better propagation characteristics of the WS than the ISM bands. In the Wi-Fi 2.0 networks, e ..."
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Abstract—The whitespaces (WS) in the legacy spectrum pro-vide new opportunities for the future Wi-Fi-like Internet access, often called Wi-Fi 2.0, since service quality can be greatly enhanced thanks to the better propagation characteristics of the WS than the ISM bands. In the Wi-Fi 2.0 networks, each wireless service provider (WSP) temporarily leases a licensed spectrum band from the licensees and opportunistically utilizes it during the absence of the legacy users. The WSPs in Wi-Fi 2.0 thus face unique challenges since spectrum availability of the leased channel is time-varying due to the ON/OFF spectrum usage patterns of the legacy users, which necessitates the eviction control of in-service customers at the return of legacy users. As a result, to maximize its profit, a WSP should consider both channel leasing and eviction costs to optimally determine a spectrum band to lease and a service tariff. In this paper, we consider a duopoly Wi-Fi 2.0 market where two co-located WSPs compete for the spectrum and customers. The competition between the WSPs is analyzed using game theory to derive the Nash Equilibria (NE) of the price (of the service tariffs) and the quality (of the leased channel, in terms of channel utilization) competitions. The NE existence condition and market entry barriers are also derived. Via an extensive numerical analysis, we show the tradeoffs between leasing/eviction cost, customer arrivals, and channel usage patterns by the legacy users. I.
Exploiting spectrum heterogeneity in dynamic spectrum market
- University of Malaya, Malaysia. His
, 2012
"... Abstract—The dynamic spectrum market (DSM) is a key economic vehicle for realizing the opportunistic spectrum access that will mitigate the anticipated spectrum-scarcity problem. DSM allows legacy spectrum owners to lease their channels to unlicensed spectrum consumers (or secondary users) in order ..."
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Abstract—The dynamic spectrum market (DSM) is a key economic vehicle for realizing the opportunistic spectrum access that will mitigate the anticipated spectrum-scarcity problem. DSM allows legacy spectrum owners to lease their channels to unlicensed spectrum consumers (or secondary users) in order to increase their revenue and improve spectrum utilization. In DSM, determining the optimal spectrum leasing price is an important yet challenging problem that requires a comprehensive understanding of market participants ’ interests and interactions. In this paper, we study spectrum pricing competition in a duopoly DSM, where two wireless service providers (WSPs) lease spectrum access rights, and secondary users (SUs) purchase the spectrum use to maximize their utility. We identify two essential, but previously overlooked, properties of DSM: 1) heterogeneous spectrum resources at WSPs and 2) spectrum sharing among SUs. We demonstrate the impact of spectrum heterogeneity via an in-depth measurement study using a software-defined radio (SDR) testbed. We then study the impacts of spectrum heterogeneity on WSPs ’ optimal pricing and SUs ’ WSP selection strategies using a systematic three-step approach. First, we study how spectrum sharing among SUs subscribed to the same WSP affects the SUs ’ achievable utility. Then, we derive the SUs ’ optimal WSP selection strategy that maximizes their payoff, given the heterogeneous spectrum propagation characteristics and prices. We analyze how individual SU preferences affect market evolution and prove the market convergence to a mean-field limit, even though SUs make local decisions. Finally, given the market evolution, we formulate the WSPs ’ pricing strategies in a duopoly DSM as a noncooperative game and identify its Nash equilibrium points. We find that the equilibrium price and its uniqueness depend on the SUs ’ geographical density and spectrum propagation
Competition in Secondary Spectrum Markets: Price War or Market Sharing?
"... Abstract—Recent initiatives allow cellular providers to offer spot service of their licensed spectrum, paving the way to dynamic secondary spectrum markets. This paper characterizes market outcomes when multiple providers are drawn into competition for secondary demand. We study a game-theoretic mod ..."
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Abstract—Recent initiatives allow cellular providers to offer spot service of their licensed spectrum, paving the way to dynamic secondary spectrum markets. This paper characterizes market outcomes when multiple providers are drawn into competition for secondary demand. We study a game-theoretic model in which each provider aims to enhance its revenue by opportunistically serving secondary demand, while also serving dedicated primary demand. The secondary demand is a function of the price being charged. We consider two philosophies for sharing spectrum between primary and secondary demand: In coordinated access, spectrum providers have the option to decline a secondary access request if that helps enhance their revenue. We explicitly characterize a break-even price such that profitability of secondary access provision is guaranteed if secondary access is priced above the breakeven price, regardless of the volume of secondary demand. Consequently, we establish that competition among providers that employ coordinated access leads to a price war. In particular market sharing above the break-even price is not an equilibrium outcome. This conclusion is valid for arbitrary secondary-demand functions. While the demand function does not play a part in determining the winner, it does affect the price of secondary access as exercised by the winning provider. In uncoordinated access, primary and secondary users share spectrum on equal basis, akin to the sharing modality of ISM bands. We demonstrate that market equilibrium in an uncoordinated access setting can be fundamentally different as it opens up the possibility of providers sharing the market at higher prices. I.
Delay Sensitive Communications over Cognitive Radio Networks
- IEEE Network • November/December 201524
, 2012
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1Efficient Objective Functions for Coordinated Learning in Large-Scale Distributed OSA Systems
"... Abstract—In this paper, we derive and evaluate private objective functions for large-scale, distributed opportunistic spectrum access (OSA) systems. By means of any learning algorithms, these derived objective functions enable OSA users to assess, locate, and exploit unused spectrum opportunities ef ..."
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Abstract—In this paper, we derive and evaluate private objective functions for large-scale, distributed opportunistic spectrum access (OSA) systems. By means of any learning algorithms, these derived objective functions enable OSA users to assess, locate, and exploit unused spectrum opportunities effectively by maximizing the users ’ average received rewards. We consider the elastic traffic model, suitable for elastic applications such as file transfer and web browsing, and in which an SU’s received reward increases proportionally to the amount of received service when the amount is higher than a certain threshold. But when this amount is below the threshold, the reward decreases exponentially with the amount of received service. In this model, SUs are assumed to be treated fairly in that the SUs using the same band will roughly receive an equal share of the total amount of service offered by the band. We show that the proposed objective functions are: near-optimal, as they achieve high performances in terms of average received rewards; highly scalable, as they perform well for small- as well as large-scale systems; highly learnable, as they reach up near-optimal values very quickly; and distributive, as they require information sharing only among OSA users belonging to the same band. Index Terms—Objective function design; scalable and dis-tributed opportunistic spectrum access; dynamic bandwidth shar-ing; cognitive radio networks; coordinated learning. I.
ADemand-Invariant Price Relationships and Market Outcomes in Competitive Private Commons
"... We introduce a private commons model that consists of network providers who serve a fixed primary demand and price strate-gically to improve their revenues from an additional secondary demand. For general forms of secondary demand, we establish the existence and uniqueness of two characteristic pric ..."
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We introduce a private commons model that consists of network providers who serve a fixed primary demand and price strate-gically to improve their revenues from an additional secondary demand. For general forms of secondary demand, we establish the existence and uniqueness of two characteristic prices: the break-even price and the market sharing price. We show that the market sharing price is always greater than the break-even price, leading to a price interval in which a provider is both profitable and willing to share the demand. Making use of this result, we give insight into the nature of market outcomes.
1 Cognitive Mobile Virtual Network Operator: Investment and Pricing with Supply Uncertainty
"... Abstract—This paper presents the first analytical study of optimal investment and pricing decisions of a cognitive mobile virtual network operator (C-MVNO) under spectrum supply uncertainty. Compared with a traditional MVNO who only obtains spectrum by long-term leasing contracts, a C-MVNO can acqui ..."
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Abstract—This paper presents the first analytical study of optimal investment and pricing decisions of a cognitive mobile virtual network operator (C-MVNO) under spectrum supply uncertainty. Compared with a traditional MVNO who only obtains spectrum by long-term leasing contracts, a C-MVNO can acquire shortterm spectrum by both sensing the empty “spectrum holes ” of licensed bands and dynamically leasing from the spectrum owner. As a result, a C-MVNO can make flexible investment and pricing decisions to match the current demands of the secondary unlicensed users. Spectrum sensing is typically cheaper than dynamic spectrum leasing, but the obtained useful spectrum amount is random due to primary licensed users ’ stochastic traffic. The C-MVNO needs to determine the optimal amounts of sensing and leasing spectrum, considering the trade-offs between cost and uncertainty. The C-MVNO also needs to determine the optimal retail price to sell the spectrum to the secondary unlicensed users, taking into account wireless heterogeneity of users such as different maximum transmission power levels and channel gains. We model and analyze these decisions and the interactions between the C-MVNO and secondary users as a multi-stage Stackelberg game. We show several interesting properties of the network equilibrium, such as threshold structures of the optimal investment and pricing decisions, independence between the optimal price and users ’ wireless characteristics, and fair and predictable spectrum allocations to the users. Compared with the traditional MVNO, spectrum sensing can significantly improve the C-MVNO’s expected profit and users ’ payoffs. I.
Coordinating Secondary-User Behaviors for Inelastic Traffic Reward Maximization in Large-Scale DSA Networks
"... Abstract—We develop efficient coordination techniques that sup-port inelastic traffic in large-scale distributed dynamic spectrum access (DSA) networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spec-trum opportunities effectively, thereby inc ..."
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Abstract—We develop efficient coordination techniques that sup-port inelastic traffic in large-scale distributed dynamic spectrum access (DSA) networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spec-trum opportunities effectively, thereby increasing their achieved throughput (or “rewards ” to be more general). Basically, learning algorithms allow DSA users to learn by interacting with the environment, and use their acquired knowledge to select the proper actions that maximize their own objectives, thereby “hopefully” maximizing their long-term cumulative received reward. However, when DSA users ’ objectives are not carefully coordinated, learning algorithms can lead to poor overall system performance, resulting in lesser per-user average achieved rewards. In this paper, we derive efficient objective functions that DSA users can aim to maximize, and that by doing so, users ’ collective behavior also leads to good overall system performance, thus maximizing each user’s long-term cumulative received rewards. We show that the proposed techniques are: (i) efficient by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributive by being implementable in a decentralized manner. Index Terms: distributed resource allocation and management, cooperative and coordinated learning, dynamic and opportunistic spectrum access. I.
Incentive Mechanisms for Hierarchical Spectrum Markets
"... Abstract—We study spectrum allocation mechanisms in hier-archical multi-layer markets which are expected to proliferate in the near future according to the evolving spectrum policy reform proposals. We consider the scenario that arises when a governmental agency sells spectrum channels to Primary Op ..."
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Abstract—We study spectrum allocation mechanisms in hier-archical multi-layer markets which are expected to proliferate in the near future according to the evolving spectrum policy reform proposals. We consider the scenario that arises when a governmental agency sells spectrum channels to Primary Opera-tors (POs) who subsequently resell them to Secondary Operators (SOs) through auctions. We show that these hierarchical markets do not result in a socially efficient spectrum allocation which is aimed by the agency, due to lack of coordination among the entities in different layers and the inherently selfish revenue-maximizing strategy of POs. In order to reconcile these opposing objectives, we propose an incentive mechanism which aligns the strategy of the POs with the objective of the agency. This pricing-based scheme constitutes a method for hierarchical market regulation. A basic component of the mechanism is a novel auction scheme which enables POs to allocate their spectrum by balancing their derived revenue and the welfare of the SOs. Our analytical and numerical results indicate that the proposed incentive mechanism leads to significant system performance improvement in terms of social welfare.
