Abstract
We present the agent-based model of the real-time spectrum trading market. Real-time means that the frequency spectrum is allocated to the operators in real-time and thus, the capacities of the operators are dynamically varying. The agent-based model consists of the two levels. The first level (the wholesale market) deals with the spectrum distribution towards the operators, where the operators compete for the spectrum resources. The second level (the retail market) presents the place where the operators compete with each-other to provide their services to the end-users. In our model, the operators are assumed to be heterogeneous in terms of the quality of service (QoS) perception. The heterogeneity of the operators exists due to the different placement of their base-stations (BTSs) in the investigated region. The BTS in the middle of the region is naturally favored, because of the unique spectral efficiency it provides to the end-users. We numerically analyze the volumes of the frequency spectra purchased by the operators, average revenue and the retail price of the operators under the consideration of three different pricing mechanisms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wallsten, S.: Is there really a spectrum crisis? Disentangling the regulatory, physical, and technological factors affecting spectrum license value. Inf. Econ. Policy 35, 7–29 (2016)
Peha, J.M., Panichpapiboon, S.: Real-time secondary markets for spectrum trading. Telecommun. Policy 28(78), 603–618 (2004). A selection of papers from the 31st Annual Telecommunications Policy Research Conference
Zheng, L., Joe-Wong, C., Tan, C.W., Ha, S., Chiang, M.: Secondary markets for mobile data: feasibility and benefits of traded data plans. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 1580–1588. IEEE (2015)
Pan, M., Li, M., Li, P., Fang, Y.: The network architecture for spectrum trading. In: Pan, M., et al. (eds.) Spectrum Trading in Multi-Hop Cognitive Radio Networks. SpringerBriefs in Electrical and Computer Engineering, pp. 1–9. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25631-3_1
Shi, G., Liu, Y., Mu, X.: Cooperative spectrum sharing in cognitive radio networks: a centralized contracted-based approach. Int. J. Multimed. Ubiquit. Eng. 11(3), 351–360 (2016)
Fortetsanakis, G., Papadopouli, M.: On multi-layer modeling and analysis of wireless access markets. IEEE Trans. Mob. Comput. 14(1), 113–125 (2015)
Conlisk, J.: Why bounded rationality? J. Econ. Lit. 34(2), 669–700 (1996)
Tonmukayakul, A., Weiss, M.B.: A study of secondary spectrum use using agent-based computational economics. NETNOMICS Econ. Res. Electron. Netw. 9(2), 125–151 (2008)
Xing, Y., Chandramouli, R., Cordeiro, C.: Price dynamics in competitive agile spectrum access markets. IEEE J. Sel. Areas Commun. 25(3), 613–621 (2007)
Yoon, H., Hwang, J., Weiss, M.B.: An analytic research on secondary-spectrum trading mechanisms based on technical and market changes. Comput. Netw. 56(1), 3–19 (2012)
Pastirčák, J., Friga, L., Kováč, V., Gazda, J., Gazda, V.: An agent-based economy model of real-time secondary market for the cognitive radio networks. J. Netw. Syst. Manag. 24(2), 427–443 (2015)
Gazda, J., Kováč, V., Tóth, P., Drotár, P., Gazda, V.: Tax optimization in an agent-based model of real-time spectrum secondary market. Telecommun. Syst. 64, 1–16 (2016)
Karnouskos, S., De Holanda, T.N.: Simulation of a smart grid city with software agents. In: Third UKSim European Symposium on Computer Modeling and Simulation, EMS 2009, pp. 424–429. IEEE (2009)
Gomez-Sanz, J.J., Garcia-Rodriguez, S., Cuartero-Soler, N., Hernandez-Callejo, L.: Reviewing microgrids from a multi-agent systems perspective. Energies 7(5), 3355–3382 (2014)
Sairamesh, J., Kephart, J.O.: Price dynamics and quality in information markets. Decis. Support Syst. 28(1), 35–47 (2000)
Grønnevet, G.A., Hansen, B., Reme, B.-A.: Spectrum policy and competition in mobile data. Inf. Econ. Policy 37, 34–41 (2016)
Sharma, S.K., Chatzinotas, S., Ottersten, B.: Interference alignment for spectral coexistence of heterogeneous networks. EURASIP J. Wirel. Commun. Netw. 2013(1), 1–14 (2013)
Kim, B.-G., Zhang, Y., van der Schaar, M., Lee, J.-W.: Dynamic pricing for smart grid with reinforcement learning. In: 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 640–645. IEEE (2014)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)
Georgilakis, P.S., Orfanos, G.A., Hatziargyriou, N.D.: Computer-assisted interactive learning for teaching transmission pricing methodologies. IEEE Trans. Power Syst. 29(4), 1972–1980 (2014)
Yang, H., Meng, Q., Lee, D.-H.: Trial-and-error implementation of marginal-cost pricing on networks in the absence of demand functions. Transp. Res. Part B: Methodol. 38(6), 477–493 (2004)
Tisue, S., Wilensky, U.: NetLogo: a simple environment for modeling complexity. In: International Conference on Complex Systems, Boston, MA, vol. 21, pp. 16–21 (2004)
Frank, R.H., Glass, A.J.: Microeconomics and Behavior. McGraw-Hill, New York (1991)
Acknowledgments
This work was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic under the contract No. 1/0766/14. This work was also supported by the Slovak Research and Development Agency, project number APVV-15-0055 and by European intergovernmental framework COST Action CA15140: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Vološin, M., Gazda, J., Drotár, P., Bugár, G., Gazda, V. (2017). Spatial Real-Time Price Competition in the Dynamic Spectrum Access Markets. In: Criado Pacheco, N., Carrascosa, C., Osman, N., Julián Inglada, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2016 2016. Lecture Notes in Computer Science(), vol 10207. Springer, Cham. https://doi.org/10.1007/978-3-319-59294-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-59294-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59293-0
Online ISBN: 978-3-319-59294-7
eBook Packages: Computer ScienceComputer Science (R0)