Game-Theory-Based on Downlink Spectrum Allocation in Two-Tier Networks
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Game-Theory-Based on Downlink Spectrum Allocation in Two-Tier Networks

Authors: Yu Zhang, Ye Tian, Fang Ye Yixuan Kang

Abstract:

The capacity of conventional cellular networks has reached its upper bound and it can be well handled by introducing femtocells with low-cost and easy-to-deploy. Spectrum interference issue becomes more critical in peace with the value-added multimedia services growing up increasingly in two-tier cellular networks. Spectrum allocation is one of effective methods in interference mitigation technology. This paper proposes a game-theory-based on OFDMA downlink spectrum allocation aiming at reducing co-channel interference in two-tier femtocell networks. The framework is formulated as a non-cooperative game, wherein the femto base stations are players and frequency channels available are strategies. The scheme takes full account of competitive behavior and fairness among stations. In addition, the utility function reflects the interference from the standpoint of channels essentially. This work focuses on co-channel interference and puts forward a negative logarithm interference function on distance weight ratio aiming at suppressing co-channel interference in the same layer network. This scenario is more suitable for actual network deployment and the system possesses high robustness. According to the proposed mechanism, interference exists only when players employ the same channel for data communication. This paper focuses on implementing spectrum allocation in a distributed fashion. Numerical results show that signal to interference and noise ratio can be obviously improved through the spectrum allocation scheme and the users quality of service in downlink can be satisfied. Besides, the average spectrum efficiency in cellular network can be significantly promoted as simulations results shown.

Keywords: Femtocell networks, game theory, interference mitigation, spectrum allocation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3299435

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References:


[1] R. F. Kayser, D. A. Yang, and D. A. Yaag, “National telecommunications and information administration,” Department of Commerce, 2011.
[2] L. Wang, C. Li, Y. Zhang, and G. Gui, “Game-theoretic social-aware resource allocation for device-to-device communications underlaying cellular network,” Wireless Communications and Mobile Computing, vol. 2018, no. 10, pp. 1–12, 2018.
[3] A. Y. Al-Zahrani and F. R. Yu, “An energy-efficient resource allocation and interference management scheme in green heterogeneous networks using game theory,” IEEE Transactions on Vehicular Technology, vol. 65, no. 7, pp. 5384–5396, 2016.
[4] C. Niu, Y. Li, R. Q. Hu, and F. Ye, “Fast and efficient radio resource allocation in dynamic ultra-dense heterogeneous networks,” IEEE Access, vol. 5, no. 99, pp. 1911–1924, 2017.
[5] M. Peng, C. Wang, J. Li, H. Xiang, and V. Lau, “Recent advances in underlay heterogeneous networks: Interference control, resource allocation, and self-organization,” IEEE Communications Surveys and Tutorials, vol. 17, no. 2, pp. 700–729, 2015.
[6] X. Xie, M. Peng, Y. Li, W. Wang, and H. V. Poor, “Channel estimation for two-way relay networks in the presence of synchronization errors,” IEEE Transactions on Signal Processing, vol. 62, no. 23, pp. 6235–6248, 2014.
[7] W. Li and Y. Li, “A new algorithm for spectrum detection in cognitive radio system,” Applied science and technology, vol. 38, no. 1, pp. 49–53, 2011.
[8] Q. Sun, Y. Tian, and M. Diao, “Cooperative localization algorithm based on hybrid topology architecture for multiple mobile robot system,” IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1–1.
[9] J. Neel, V. Mprg, Tech, J. H. Reed, Mprg, V. Tech, and R. P. Gilles, “The role of game theory in the analysis of software radio networks,” Revista Medica De Chile, vol. 118, no. 3, pp. 313–319, 2002.
[10] J. O. Neel, J. H. Reed, and R. P. Gilles, “Convergence of cognitive radio networks,” Wcnc March, vol. 4, pp. 2250–2255 Vol.4, 2004.
[11] N. Nie and C. Comaniciu, “Adaptive channel allocation spectrum etiquette for cognitive radio networks,” Mobile Networks and Applications, vol. 11, no. 6, pp. 779–797, 2006.
[12] Y. JIa and Yue, “Dynamic overlapped spectrum allocation based on potential game in cognitive radio networks,” High Technology Letters, vol. 20, no. 4, 2014.