Article Preview
Top1. Introduction
We live in a time when the evolution of technology, follows a frantic pace, the innovations occur always aiming at optimization and maximum exploitation of available resources. From cell phone first generation to the third-generation iPad, we passed from one generation to another by benefiting from higher data rates and services more and more sophisticated. The recent evolution of wireless technologies creates a strong demand in terms of radio spectrum, thus causing a deficit in the spectral resources. Several recent studies have revealed the sub-optimal usage of radio bands by insisting on the fact that some bands become overloaded while others remain largely under-exploited. In addition, the Federal Commission of Communication (FCC) (Paterson & Gangadharan, 2015) has mentioned that the use of the band below 3 GHz varies between 15% and 85%. To compensate for these problems, researchers have proposed a new paradigm which is Cognitive Radio (CR) (Ding, Fang, Huang, Pan, Li, & Glisic, 2017) in order to better exploit the existing spectrum by taking advantage of free and unused channels in a dynamic and opportunistic way. This technology, therefore, allows an increase in the number of users and better distribution of the available resources.
The concept of cognitive radio is, in reality, an interaction between the wireless technology and artificial intelligence. Indeed, the capacity of cognitive radio integrated into a terminal offers a possibility to interact with its environment radio to adapt, detect, and exploit free frequencies. The terminal will have sufficient capacity to enable it to effectively manage the whole radio resources. Nevertheless, the implementation of this technology requires in-depth studies on detection of free resources, spectrum sharing, the spectrum decision, and mobility management of secondary users (Nguyen, Nguyen, Nguyen & Dang, 2013), etc.
These last times, the spectrum sharing is a matter of high priority research and brings a huge interest for access to spectrum flexible and dynamic aimed at an efficient allocation of radio resources. Obviously, the future technology of the system radio requires a very efficient use of the spectrum and to have also a capacity of coexistence and sharing with other systems radios in an efficient manner. The question concerning the coexistence of the radio system has introduced new technical challenges.
The spectrum market has been introduced in order to optimize the spectrum sharing between primary users (PUs) and cognitive users (CUs) as a result of the economic profits of PUs (Zhang, Zhang, Chen & Guo, 2014; Chien, Chang, & Chan, 2015). The market theory and the prices theory give a capital importance in the economic model's facilitation for the spectrum trade (Zhang & Han, 2017). The main idea of spectrum marketing is to satisfy both sellers and buyers. Therefore, the spectrum price must be set according to the objectives of the primary users and cognitive users (Wang, Zhou & Wu, 2014).
The purpose of this paper is to describe the different models of spectrum trading and proposed a better solution. The behavior of the primary operators (POs) concerning the price models is divided into three models, namely: (1) competition between primary POs (2) no competition and no cooperation between the POs, (3) cooperation between POs. The pricing systems corresponding to the different behaviors are called competitive price, market balance price, and the price cooperative, respectively.