Heterogeneous Service-Oriented Spectrum Trading

Heterogeneous Service-Oriented Spectrum Trading

Gang Hu (National University of Defense Technology, China), Lixia Liu (National University of Defense Technology, China) and Yuxing Peng (National University of Defense Technology, China)
DOI: 10.4018/978-1-4666-6571-2.ch032
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Multiple characters of spectrum resource bring many challenges to spectrum trading. The demanders may not find the full-matching spectrum resource. Meanwhile, the optimal matching strategy cannot be determined if the demanders have different matching ratios. This chapter proposes an algorithm called HSO-ST (Heterogeneous Service-Oriented Spectrum Trading) with the target of maximum matching number under the priority restriction. This algorithm can satisfy as many secondary users as possible. Compared with other spectrum trading strategies, HSO-ST can greatly improve the spectrum demand-matching ratio.
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Dynamic spectrum access has attracted wide and intensive attention as it is a promising method to solve the spectrum under-utilization problem and will satisfy the rapid-developing spectrum demand in the near future(Qing Zhao & Brian M. Sadler, 2007).

From the technical perspective, cognitive radio can sense the spectrum environment(J.Mitola & Q.Gerald,1999), then find the unused spectrum and access it opportunistically without interfering the primary user (PU, the licensed user). It contains many complex operations, such as spectrum sensing, power control, spectrum allocation, spectrum handoff and so on. Although the secondary user (SU, the unlicensed user) can share the spectrum resource for free, its communication quality can’t be guaranteed because it has to quit from the spectrum immediately once PU needs the spectrum. PU will not take part in the spectrum sharing process actively because nothing can motivate it to cooperate with SU. So it just cares its benefits.

From the economic perspective, PU would like to lease or sell the unused spectrum to SU via spectrum trading so that it can gain some profit to counterbalance the cost of getting the spectrum license from the spectrum provider. SU only has to pay something for the spectrum access chance and don’t need to worry about the interference to PU and conflict with other SU. Spectrum trading, which can improve the spectrum efficiency obviously, is an advisable method especially for the users who have high demands for the spectrum quality and do not consider the payment problem.

Some economic models, such as auction and game theory, have been investigated in (K.M.V. Rodriguez & R.Tafazolli,2005, D.Niyato & E.Hossain,2007). Price has been paid much attention to as well as strategy. Qian et al. proposed an agent-based spectrum trading model and tried to maximize the profit of the agent as well as to enhance the satisfaction of secondary users(L Qian, et al., 2011). P.M.Joseph proposed a multiple-dimension auctioning mechanism through a broker and discussed two trading negotiation protocols, merchant and auction(P.M.Joseph, et al., 2011). The broker’s objective is to maximize its own revenue. Duan proposed a cognitive mobile virtual network operator (C-MVNO) which serves as the interface between the spectrum owner and the secondary end-users(B.S. Lingjie Duan, Jianwei Huang, 2011). In the practical application, the primary user and the secondary user both do not want the agent, broker or operator to participate the spectrum trading if they can do it by themselves. The basic reason is that no one would like to share the profit with others if he can’t get obvious advantage.

Tan modeled a non-cooperative pricing game using the profit as the payoff, he also studied the short-term price war and long-term price war(K.Yi Tan & Shamil Sengupta, 2010). Price is the crucial problem of spectrum trading, but it is not enough to concentrate on how to formulate an optimal pricing strategy, in order to maximize the revenue from the perspective of spectrum provider without taking the customer behavior into consideration. In the practical business scenario, the demander also affects the spectrum market as well as the supplier.

Key Terms in this Chapter

Cognitive Radio: The concept of cognitive radio is coined by Joseph Mitola III. It is a kind of radio than be able to complete some function intelligently. It can be regarded as smart radio, which is an evolution from software defined radio.

Secondary User: The user that want to access the spectrum opportunistically without interfering with primary user.

Bipartite Graph: It means a kind of graph that consists of two parts of node, edges of the graph only exist between different part of node.

Matching Ratio: A ratio that the number of attributes that provided spectrum can satisfy the demander’s requirement for the spectrum resource.

Heterogeneous Service: It refers to different network services such as email, video, p2p, etc.

Primary User: The subscriber that can access the spectrum with legal protection.

Spectrum Trade: An exchange behavior that allows one to get the spectrum resource under the condition of some sacrifice.

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