According to the analysis above-mentioned, by selecting the optimal spectrum and then using the graph algorithm to allocate idle spectrum, we can make full use of spectrum resources, enhance the spectrum utilization and improve the overall efficiency.
Combining the distributed greedy algorithm and the method of AHP, the spectrum decision diagram is shown in Fig.2.
The improved spectrum allocation algorithm can be described as follows:
After initializing the system and updating the node information, according to the measured values of bandwidth, delay, jitter and packet loss rate of each spectrum, the hierarchical structure is established by using AHP; Set up the corresponding comparison matrix, and obtain the spectrum efficiency; Finally, the selected optimal spectrum is allocated using the coloring algorithm.
3 Numerical Results
In the simulation process, we suppose that there are six primary users in given region of 1000 m×1000 m, and the initial number of idle channels is 10. In this simulation environment, the total network benefits and user fairness of the two algorithms and their improved algorithms are analyzed and compared. Using
40 60 80 100 120
Number of secondary users 1.5
2 2.5 3 3.5 4
Network efficiency
WDGA AHP-DGA DGA
Fig. 3.Network utility curves of the three algorithms in greed mode
40 60 80 100 120 Number of secondary users
1 1.5 2 2.5 3 3.5 4
Fitness
WDGA AHP-DGA DGA
Fig. 4.Changing curves of the three algorithms in greedy mode
U(R) to measure the network efficiency, with the variance to measure the fair- ness between users. In the simulation, randomly generate the network topology diagram. Furthermore, we randomly set the values in available spectrum matrix L, interference matrixC and utility matrixB within [0,1].
The parameters including bandwidth, delay, jitter and packet loss rate meet the data transmission standard of wireless networks proposed by ITU-T [7]. From Figs.3and4, our proposed method using AHP and distributed greed algorithm (AHP-DGA) is compared with the results obtained by original distributed greedy algorithm (DGA) and weighted distributed greedy algorithm (WDGA). It can be concluded that AHP-DGA can receive high network efficiency and decent network fairness.
References
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143–147 (2015)
2. Li, F., Tan, X., Wang, L.: A new game algorithm for power control in cognitive radio networks. IEEE Trans. Veh. Technol.60(9), 4384–4392 (2011)
3. Liu, X., Jia, M., Tan, X.: Threshold optimization of cooperative spectrum sensing in cognitive radio network. Radio Sci.48(1), 23–32 (2013)
4. Wang, W., Liu, X.: List-coloring based channel allocation for open-spectrum wireless networks. In: IEEE VTC-Fall, pp. 690–694 (2005)
5. Liu, Y., Jiang, M., Tan, X., et al.: Maximal independent set based channel allo- cation algorithm in cognitive radios. In: IEEE Youth Conference on Information, Computing and Telecommunication, pp. 78–81 (2009)
6. Bao, Y., Wang, S., Yan, B., et al.: Research on maximal weighted independent set- based graph coloring spectrum allocation algorithm in cognitive radio networks. In:
Proceedings of the International Conference on Communications, Signal Processing and Systems, pp. 263–271 (2016)
7. ITU-T (2016).https://en.wikipedia.org/wiki/ITU-T
8. Li, M.: Research of cognitive radio spectrum allocation algorithm based on graph theory. Southwest Jiaotong University, pp. 45–47 (2012)
Spectrum Pricing in Condition of Normally Distributed User Preference
Li Wang1, Lele Cheng1, Feng Li1(B), Xin Liu2, and Di Shen1
1 Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China {liwang2002,fenglzj}@zjut.edu.cn, 478708892@qq.com, 814120631@qq.com
2 Dalian University of Technology, Dalian 116024, Liaoning, China liuxinstar1984@dlut.edu.cn
Abstract. During secondary user’s dynamic access to authorized spec- trum, a key issue is how to ascertain an appropriate spectrum price so as to maximize primary system’s benefit and satisfy secondary user’s diverse spectrum demands. In this paper, a scheme of pricing-based dynamic spectrum access is proposed. According to the diverse qualities of idle spectrum, the proposal applies Hotelling game model to describe the spectrum pricing problem. Firstly, establish a model of spectrum leasing, among which the idle spectrum with different qualities forms a spectrum pool. Then, divide the idle spectrum into equivalent width of leased channels, which will be uniformly sold in order. Secondary users can choose proper channels to purchase in the spectrum pool according to their spectrum usage preferences which are subject to normal distri- bution and affected by the spectrum quality and market estimation. This paper analyzes the effect of spectrum pricing according to the primary system’s different tendencies to spectrum usage and economic income.
Keywords: Spectrum pricingãCognitive radioãUser preference Spectrum quality
1 Introduction
With the rapid development of wireless communication technology and the estab- lishment of next-generation 5g communication standard, high-quality idle spec- trum is more scarce which has become one of the bottlenecks restricting the development of wireless communication technology [1]. Cognitive radio which is based on dynamic spectrum access has attracted more and more attention of academe and engineering recent years [2]. Various kinds of emerging network technology have begun to adopt dynamic spectrum detection and dynamic spec- trum access to improve the efficiency of spectrum utilization. In the process of dynamic spectrum access, primary users owning licensed spectrum can lease the idle channels to secondary user to gain incomes. For primary users, how to identify an optimal channel pricing to maximize its own profit has become a significant issue. In this paper, we directly price the idle spectrum of autho- rized users according to the secondary user’s diverse preferences. The spectrum
c ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018 X. Gu et al. (Eds.): MLICOM 2017, Part II, LNICST 227, pp. 15–22, 2018.
https://doi.org/10.1007/978-3-319-73447-7_3
pricing scheme has a prior estimate to the spectrum market. Compared with the spectrum auction, it doesn’t need many overheads and improves the convenience of the spectrum access.
Spectrum trading provides an efficient way for secondary users to dynamically access licensed bands while the financial gains can encourage primary users to lease unused spectrum temporarily. Generally, the participants can perform the deal by auction-based method or pricing-based method. The spectrum auction mechanism can be divided into many kinds according to different application circumstances, such as trust-based auction which relaxes the credit limit appro- priately in return for a higher economic efficiency to balance the honesty and the efficiency [3,4]. On the other hand, to lower the overhead and time cost for spectrum pricing, pricing-based spectrum trading has also been widespread concerned either [5,6].
In this paper, we investigate how to price the spectrum when heterogeneous spectrum and stochastic secondary user’s preference are under consideration.
A concept of spectrum pool is introduced to facilitate the following spectrum deal. A secondary spectrum customer will pick a high-quality channel for usage when its capital is ample or wide band is required to support essential service.
We adopt Hotelling model which is proper to describe the product pricing issue in heterogeneous market. By analyzing the secondary user’s preference param- eter, an iterative algorithm for spectrum pricing is obtained by fixing the Nash equilibrium. Numerical results are further provided to evaluate how the pricing parameters affect the primary system’s profits.
2 System Model
Suppose the idle spectrum leased by the primary system consists a spectrum sharing pool, where the spectrum can be divided into many uniform channels for selling. Besides, the qualities of these channels are not homogeneous. For high- quality channels, the secondary users suffers lower channel fading or adjacent channel interference. Thus, secondary users choose these channels according to their diverse preferences. The preference parameter is determined by the channel quality and channel price.