5G Price Competition with Social Equilibrium Optimality for Social Networks
Source abstract
Abstract
Due to the leaps of progress in the 5G telecommunication industry, commodity pricing and consumer choice are frequently subject to change and competition in the search for optimal supply and demand. We here utilize a two-stage extensive game with complete information to mathematically describe user-supplier interactions on a social network. Firstly, an example of how to apply our model in a practical 5G wireless system is shown. Then we build a prototype that offers multiple services to users and provides different outputs for suppliers, where in addition, the user and supplier quantities are independently distributed. Secondly, we then consider a scenario in which we wish to maximize social welfare and determine if there is a perfect answer. We seek the subgame perfect Nash equilibrium and show that it exists, and also show that when both sides reach it, social welfare likewise reaches its maximum. Finally, we provide numerical results that corroborate the efficacy of our approach on a practical example in the 5G background.
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Research context
OpenAlex topics: Opinion Dynamics and Social Influence · Complex Network Analysis Techniques · Game Theory and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities of China
References (38)
Show source-provided references
1. An incentive framework for mobile data offloading market under price competition10.1109/TMC.2017.2688402 ↗
2. Collusion under different pricing schemes10.1111/jems.12392 ↗
3. Wireless service pricing competition under network effect, congestion effect, and bounded rationality10.1109/TVT.2018.2822843 ↗
4. Hierarchical competition as equilibrium program with equilibrium constraints towards security-enhanced wireless networks10.1109/JSAC.2018.2824578 ↗
5. A supplier-firm-buyer framework for computation and content resource assignment in wireless virtual networks10.1109/TWC.2019.2921344 ↗
6. Hybrid pricing for mobile collaborative Internet access10.1109/TNET.2019.2911123 ↗
7. Resource allocation and basestation placement in cellular networks with wireless powered UAVs10.1109/TVT.2018.2883093 ↗
8. An intelligent resource dynamic allocation method for UAV wireless mobile network which supports QoS10.1016/j.comcom.2020.01.032 ↗
9. Jiang, L., Parekh, S., and Walrand, J. (April, January 31). Base station association game in multi-cell wireless networks. Proceedings of the Wireless Communications and Networking Conference, Las Vegas, NV, USA.
10. Adlakha, S., Johari, R., and Goldsmith, A.J. (2007). Competition in wireless systems via Bayesian interference games. arXiv.
11. Etkin, R., Parekh, A., and Tse, D. (2005, January 8–11). Spectrum sharing for unlicensed bands. Proceedings of the First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN, Baltimore, MD, USA.
12. Distributed interference compensation for wireless networks10.1109/JSAC.2006.872889 ↗
13. Kalathil, D., and Jain, R. (2010, January 6–9). Spectrum sharing through contracts. Proceedings of the 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), Singapore.10.1109/DYSPAN.2010.5457899 ↗
14. Utility-based power control for a two-cell CDMA data network10.1109/TWC.2005.858024 ↗
15. Grokop, L., and Tse, D.N. (2008, January 13–18). Spectrum sharing between wireless networks. Proceedings of the 27th Conference on Computer Communications, INFOCOM, Phoenix, AZ, USA.10.1109/INFOCOM.2008.49 ↗
16. Zemlianov, A., and de Veciana, G. (2005, January 13–17). Cooperation and decision-making in a wireless multi-provider setting. Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM, Miami, FL, USA.
17. Sengupta, S., Chatterjee, M., and Ganguly, S. (2007, January 17–20). An economic framework for spectrum allocation and service pricing with competitive wireless service providers. Proceedings of the 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN, Dublin, Ireland.10.1109/DYSPAN.2007.19 ↗
18. Jia, J., and Zhang, Q. (2008, January 26–30). Competitions and dynamics of duopoly wireless service providers in dynamic spectrum market. Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc, Hong Kong, China.10.1145/1374618.1374660 ↗
19. Ileri, O., Samardzija, D., Sizer, T., and Mandayam, N.B. (2005, January 8–11). Demand responsive pricing and competitive spectrum allocation via a spectrum server. Proceedings of the First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN, Baltimore, MD, USA.
20. Acharya, J., and Yates, R.D. (2009, January 13–15). Service provider competition and pricing for dynamic spectrum allocation. Proceedings of the 2009 International Conference on Game Theory for Networks, Istanbul, Turkey.10.1109/GAMENETS.2009.5137401 ↗
21. Inaltekin, H., Wexler, T., and Wicker, S.B. (2007, January 18–21). A duopoly pricing game for wireless IP services. Proceedings of the 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON, San Diego, CA, USA.10.1109/SAHCN.2007.4292872 ↗
22. Dynamics of multiple-seller and multiplebuyer spectrum trading in cognitive radio networks: A game-theoretic modeling approach10.1109/TMC.2008.157 ↗
23. Xi, Y., and Yeh, E.M. (2008, January 13–18). Pricing, competition, and routing for selfish and strategic nodes in multi-hop relay networks. Proceedings of the IEEE INFOCOM 2008-The 27th Conference on Computer Communications, Phoenix, AZ, USA.10.1109/INFOCOM.2008.205 ↗
24. Gao, L., and Wang, X. (2008, January 26–30). A game approach for multi-channel allocation in multihop wireless networks. Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc, Hong Kong, China.10.1145/1374618.1374659 ↗
25. Chen, M., and Huang, J. (2008, January 19–21). Optimal resource allocation for OFDM uplink communication: A primal-dual approach. Proceedings of the 2008 42nd Annual Conference on Information Sciences and Systems, CISS, Princeton, NJ, USA.
26. A hybrid downlink noma with ofdm and ofdm-im for beyond 5g wireless networks10.1109/LSP.2020.2979059 ↗
27. Fudenberg, D., and Tirole, J. (1991). Game Theory, MIT Press.
28. Solving multi-leader-common-follower games10.1080/10556780903448052 ↗
29. A game-theoretic analysis of the implications of overlay network traffic on ISP peering10.1016/j.comnet.2008.06.014 ↗
30. Yang, G., Xu, X., and Liang, Y.-C. (2020, January 25–28). Intelligent reflecting surface assisted non-orthogonal multiple access. Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea.10.1109/WCNC45663.2020.9120476 ↗
31. Joint scheduling of urllc and embb traffic in 5g wireless networks10.1109/TNET.2020.2968373 ↗
32. Nguyen, H.V., Kim, H.M., Kang, G.-M., Nguyen, K.-H., Bui, V.-P., and Shin, O.-S. (2020). A survey on non-orthogonal multiple access: From the perspective of spectral efficiency and energy efficiency. Energies, 13.10.3390/en13164106 ↗
33. Utility maximization for communication networks with multipath routing10.1109/TAC.2006.875032 ↗
34. Voice, T. (2006). Stability of Congestion Control Algorithms with Multi-Path Routing and Linear Stochastic Modelling of Congestion Control. [Ph.D. Dissertation, University of Cambridge].10.1145/1190095.1190167 ↗
35. Bertsekas, D.P. (1999). Nonlinear Programming, Athena Scientific.
36. Mathur, S., Sankaranarayanan, L., and Mandayam, N.B. (2006, January 22–24). Coalitional games in receiver cooperation for spectrum sharing. Proceedings of the 2006 40th Annual Conference on Information Sciences and Systems, CISS, Princeton, NJ, USA.10.1109/CISS.2006.286603 ↗
37. Mas-Colell, A., Whinston, M.D., and Green, J.R. (1995). Microconomic Theory, Oxford University Press.
38. Utility-optimal random access without message passing10.1109/TWC.2009.071446 ↗