According to the recent rulings of the Federal Communications Commission (FCC), TV white spaces (TVWS) can now be accessed by secondary users (SUs) after a list of vacant TV channels is obtained via a geo-location database. Proper business models are therefore essential for database operators to manage geo-location databases. Database access can be simultaneously priced under two different schemes: the registration scheme and the service plan scheme. In the registration scheme, the database reserves part of the TV bandwidth for registered White Space Devices (WSDs). In the service plan scheme, the WSDs are charged according to their queries. In this paper, we investigate the business model for the TVWS database under a hybrid pricing scheme. We consider the scenario where a database operator employs both the registration scheme and the service plan scheme to serve the SUs. The SUs' choices of different pricing schemes are modeled as a non-cooperative game and we derive distributed algorithms to achieve Nash Equilibrium (NE). Considering the NE of the SUs, the database operator optimally determines pricing parameters for both pricing schemes in terms of bandwidth reservation, registration fee and query plans.
Most existing works on geo-location databases can be classified into two categories. Some works focus on the design of geo-location database to protect primary users. In @cite_6 , discussed the methods to calculate the protection area for TV stations. In @cite_2 , designed a database-driven white space network based on measurement studies and terrain data. Some other works focused on the networking issue with the assumption that the database is already set up. In @cite_1 , presented a white space system utilizing a database. In @cite_12 , considered the channel selection and access point association problem. One recent work @cite_9 also address the business model related to the geo-location database. In @cite_9 , the authors proposed that the geo-location database acts as a spectrum broker reserving the spectrum from spectrum licensees. They considered only one pricing scheme which is similar to the registration scheme discussed in our paper. Compared to our previous work @cite_20 , in this paper, we further extend the scenario to non-strategic SUs and compared the pricing schemes with non-strategic and strategic SUs under complete information scenario. We also extend our theoretical analysis and numerical evaluations.
@cite_9 @cite_1 @cite_6 @cite_2 @cite_20 @cite_12
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According to the recent rulings of the Federal Communications Commission (FCC), TV white spaces (TVWS) can now be accessed by secondary users (SUs) after a list of vacant TV channels is obtained via a geo-location database. Proper business models are essential for database operators to manage the cost of maintaining geo-location databases. Database access can be simultaneously priced under two different schemes: the registration scheme and the service plan scheme. In the registration scheme, the database reserves part of the TV bandwidth for registered White Space Devices (WSD) in a soft-license way. In the service plan scheme, WSDs are charged according to their queries. In this paper, we investigate the business model for the TVWS database under a hybrid pricing scheme. We consider the scenario where a database operator employs both the registration scheme and the service plan scheme to serve the SUs. The SUs' choices of different pricing schemes are modeled as a non-cooperative game and we derive distributed algorithms to achieve the Nash Equilibrium (NE). Considering the NE of the SUs, the database operator optimally determines the pricing parameters for both pricing schemes in terms of bandwidth reservation, registration fee and query plans. Geo-location database driven white space network is a very promising approach for improving secondary spectrum utilization. In this paper, we consider the business modeling for geo-location database driven white space network. In our proposed model, the database acts as a spectrum broker buying (reserving) bandwidth from spectrum licensees in advance, and then resells the reserved bandwidth to unlicensed white space devices (WSDs) in real-time. We study the optimal bandwidth reservation for the database with WSDs' demand uncertainty under both information symmetry and asymmetry. Under information symmetry, the database and the WSD experience the same degree of uncertainty about the market demand. We derive the optimal bandwidth reservations in a centralized integrated manner (as a benchmark). Under information asymmetry, the WSD has more information (i.e., with less uncertainty) about demand (due to the proximity to end-users). We propose a contract-based bandwidth reservation mechanism, which ensures WSDs share their local information with the database credibly. We further characterize the optimal bandwidth reservation contract systematically. Simulations show that under information asymmetry, the optimal bandwidth reservation contract improves both the database's profit and the social welfare significantly (larger than 30 in our simulations) without sacrificing the WSDs' benefits, comparing to those mechanisms without information sharing. The most recent FCC ruling proposes relying on a database of incumbents as the primary means of determining white space availability at any white spaces device (WSD). While the ruling provides broad guidelines for the database, the specifics of its design, features, implementation, and use are yet to be determined. Furthermore, architecting a network where all WSDs rely on the database raises several systems and networking challenges that have remained unexplored. Also, the ruling treats the database only as a storehouse for incumbents. We believe that the mandated use of the database has an additional opportunity: a means to dynamically manage the RF spectrum. Motivated by this opportunity, in this paper we present SenseLess, a database driven white spaces network. As suggested by its very name, in SenseLess, WSDs obviate the need to sense the spectrum by relying entirely on a database service to determine white spaces availability. The service, using a combination of an up-to-date database of incumbents, sophisticated signal propagation modeling, and an efficient content dissemination mechanism ensures efficient, scalable, and safe white space network operation. We build, deploy, and evaluate SenseLess and compare our results to ground truth spectrum measurements. We present the unique system design considerations that arise due to operating over the white spaces. We also evaluate its efficiency and scalability. To the best of our knowledge, this is the first paper that identifies and examines the systems and networking challenges that arise from operating a white space network, which is solely dependent on a channel occupancy database. Cognitive Radio Network (CRN) is regarded as a promising way to address the increasing demand for wireless channel resources. It solves the channel resource shortage problem by allowing a Secondary User (SU) to access the channel of a Primary User (PU) when the channel is not occupied by the PU. The latest FCC's rule in May 2012 enforces database-driven CRNs, in which an SU queries a database to obtain spectrum availability information by submitting a location based query. However, one concern about database-driven CRNs is that the queries sent by SUs will inevitably leak the location information. In this study, we identify a new kind of attack against location privacy of database-drive CRNs. Instead of directly learning the SUs' locations from their queries, our discovered attacks can infer an SU's location through his used channels. We propose Spectrum Utilization based Location Inferring Algorithm that enables the attacker to geo-locate an SU. To thwart location privacy leaking from query process, we propose a novel Private Spectrum Availability Information Retrieval scheme that utilizes a blind factor to hide the location of the SU. To defend against the discovered attack, we propose a novel prediction based Private Channel Utilization protocol that reduces the possibilities of location privacy leaking by choosing the most stable channels. We implement our discovered attack and proposed scheme on the data extracted from Google Earth Coverage Maps released by FCC. Experiment results show that the proposed protocols can significantly improve the location privacy.