New Method of Energy Efficient Subcarrier Allocation Based on Evolutionary Game Theory
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Since there is a competition between subcarriers because FBMC (Filter Bank Multicarrier) modulation technology does not need subcarriers to be orthogonal to each other, we consider the evolutionary game method to optimize subcarrier allocation. Because the adjacent subcarriers do not need to be orthogonal to each other in FBMC, there is conflict and competition, thus the evolutionary game theory is used to optimize the subcarrier allocation problem. We innovatively introduced the channel state matrix to show the quality of subcarriers. Considering the height of secondary user and base station’s antenna, the total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier, a nonlinear fractional programming problem is established where maximum energy efficiency is the objective function, total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier are constraint conditions. The utility function for each secondary user is established when the evolutionary game operator is designed. When the utility function becomes optimal, the evolutionary game reaches Nash equilibrium, and the strategy combination is considered to be the energy efficient resource allocation scheme. Through experimental simulation, EESA-EG proposed in this paper gives the most reasonable subcarrier allocation scheme, allocates more subcarriers for the subcarriers with better channel state and the energy efficiency in EESA-EG is optimal.
KeywordsCognitive radio network FBMC Resource allocation Evolution game Energy efficient optimization
This research work is supported by National Natural Science Foundation of China (Grant No. 61571328), Tianjin Key Natural Science Foundation (No.13JCZDJC34600), CSC Foundation (No. 201308120010), Major projects of science and technology in Tianjin (No.15ZXDSGX 00050), Training plan of Tianjin University Innovation Team (No.TD12-5016, No.TD13-5025), Major projects of science and technology for their services in Tianjin (No.16ZXFWGX00010, No.17YFZC GX00360), the Key Subject Foundation of Tianjin(15JCYBJC 46500), Training plan of Tianjin 131 Innovation Talent Team (No.TD2015-23).
- 5.Li GY, Xu Z, Xiong C (2011) Energy-efficient wireless communications: tutorial, survey, and open issues. IEEE Trans Wirel Commun 18(6):29–35Google Scholar
- 6.Liu S, Zhang T (2017) Novel unequal clustering routing protocol considering energy balancing based on Network Partition & Distance for Mobile education. J Netw Comput Appl 88(15):1–9Google Scholar
- 7.D. G. Zhang, H. Ge, T. Zhang (2018) New Multi-hop Clustering Algorithm for Vehicular Ad Hoc Networks. IEEE Transactions on Intelligent Transportation Systems 7. doi: https://doi.org/10.1109/TITS.2018.2853165
- 15.Singh K, Ku ML, Lin JC (2014) Power control for achieving energy efficient multiuser two-way balancing relay networks. Proc IEEE ICASSP:2749–2753Google Scholar
- 19.Denis J, Pischella M, Le Ruyet D (2016) Optimal energy-efficient power allocation for asynchronous cognitive radio networks using FBMC/OFDM. IEEE wireless conference and networking conference (WCNC 2016) track 1: PHY and fundamentalsGoogle Scholar
- 23.Yaacoub E (2009) Z. Dawy. A game theoretical formulation for proportional fairness in LTE uplink scheduling. IEEE Wireless Commun Network Conf 2009:1–5Google Scholar
- 24.Vatsikas S, Armour S, Vos MD (2011) A Fast and Fair Algorithm for Distributed Subcarrier Allocation Using Coalitions and the Nash Bargaining Solution. 2011 IEEE Vehicular Technology Conference (VTC Fall) 1–5Google Scholar
- 26.Huang SL, Tan JJ, Xu J (2015) Nash Bargaining Game Based Subcarrier Allocation for Physical Layer Security in Orthogonal Frequency Division Multiplexing System. 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 1094–1100Google Scholar
- 27.Song QY, Zhuang JH, Zhang LC (2011) Evolution Game Based Spectrum Allocation in Cognitive Radio Networks. 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing 1–4Google Scholar
- 28.Medjahdi Y, Terre M, Le Ruyet D et al. (2009) Inter-cell interference analysis for OFDM/FBMC systems. IEEE 10th Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 598–602Google Scholar