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.
Many works also focus on the economic issue of dynamic spectrum sharing. In @cite_16 , the pricing-based spectrum access control is investigated under secondary users competitions. In @cite_10 , spectrum pricing with spatial reuse is considered. Contract theory is utilized in the scenarios where the spectrum buyers have hidden information. In @cite_19 , leveraged contract theory to analyze the spectrum trading between primary operator and SUs. In @cite_22 , contract theory is applied to the cooperative communication scenario. In this paper, we also model the service plan design with contract theory. However, due to the co-existence of hybrid pricing schemes, there is uncertainty about the number of SUs choosing the contract items, which is different from existing works.
@cite_19 @cite_16 @cite_10 @cite_22
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Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing sharing. In this paper, we consider the problem of spectrum trading with single primary spectrum owner (or primary user, PO) selling his idle spectrum to multiple SUs. We model the trading process as a monopoly market, in which the PO acts as monopolist who sets the qualities and prices for the spectrum he sells, and the SUs act as consumers who choose the spectrum with appropriate quality and price for purchasing. We design a monopolist-dominated quality-price contract, which is offered by the PO and contains a set of quality-price combinations each intended for a consumer type. A contract is feasible if it is incentive compatible (IC) and individually rational (IR) for each SU to purchase the spectrum with the quality-price intended for his type. We propose the necessary and sufficient conditions for the contract to be feasible. We further derive the optimal contract, which is feasible and maximizes the utility of the PO, for both discrete-consumer-type model and continuous-consumer-type model. Moreover, we analyze the social surplus, i.e., the aggregate utility of both PO and SUs, and we find that, depending on the distribution of consumer types, the social surplus under the optimal contract may be less than or close to the maximum social surplus. In Cognitive Radio Networks (CRN), there are multiple primary and secondary users in a region, and primaries can lease out their unused bandwidth to secondaries in exchange for a fee. This gives rise to price competition among the primaries, wherein each primary tries to attract secondaries by setting a lower price for its bandwidth than the other primaries. Radio spectrum has the distinctive feature that transmissions at neighboring locations on the same channel interfere with each other, whereas the same channel can be used at far-off locations without mutual interference. So in the above price competition scenario in a CRN, each primary must jointly select a set of mutually non-interfering locations within the region (which corresponds to an independent set in the conflict graph representing the region) at which to offer bandwidth and the price at each location. In this paper, we analyze this price competition scenario as a game and seek a Nash Equilibrium (NE). We identify a class of conflict graphs, which we refer to as mean valid graphs, such that the conflict graphs of a large number of topologies that commonly arise in practice are mean valid. We explicitly compute a symmetric NE in mean valid graphs and show that it is unique. Providing proper economic incentives is essential for the success of dynamic spectrum sharing. Cooperative spectrum sharing is one effective way to achieve this goal. In cooperative spectrum sharing, secondary users (SUs) relay traffics for primary users (PUs), in exchange for dedicated transmission time for the SUs' own communication needs. In this paper, we study the cooperative spectrum sharing under incomplete information, where SUs' types (which capture the relay channel gains and the SUs' power costs) are private information and are not known to the PU. Inspired by the contract theory, we model the network as a labor market. The PU is an employer who offers a contract to the SUs. The contract consists of a set of items representing combinations of spectrum access time (i.e., reward) and relay power (i.e., contribution). The SUs are employees, and each of them selects the best contract item to maximize its payoff. We study the optimal contract design for both weakly and strongly incomplete information scenarios. First, we provide necessary and sufficient conditions for feasible contracts in both scenarios. In the weakly incomplete information scenario, we further derive the optimal contract that achieves the same maximum PU's utility as in the complete information benchmark. In the strongly incomplete information scenario, we propose a Decompose-and-Compare algorithm that achieves a close-to-optimal contract. We further show that the PU's expected utility loss due to the suboptimal algorithm and the strongly incomplete information are both relatively small (less than 2 and 1.3 , respectively, in our numerical results with two SU types).