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Adaptive Energy-Efficient Power Allocation in Green Interference Alignment Based Wireless Networks

Zhao, Nan; Yu, Richard; Sun, Hongjian

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Authors

Nan Zhao

Richard Yu



Abstract

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum especially at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation and transmission-mode adaptation for green IAbased wireless networks. The power allocation problem for IA is first analyzed, then we propose a power allocation scheme that optimizes the energy efficiency of IA-based wireless networks. When SNR is low, the transmitted power of some users may become zero. Thus the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the energy efficiency of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the energy efficiency of IAbased wireless networks.

Citation

Zhao, N., Yu, R., & Sun, H. (2015). Adaptive Energy-Efficient Power Allocation in Green Interference Alignment Based Wireless Networks. IEEE Transactions on Vehicular Technology, 64(9), 4268-4281. https://doi.org/10.1109/tvt.2014.2362005

Journal Article Type Article
Acceptance Date Sep 28, 2014
Online Publication Date Oct 8, 2014
Publication Date Sep 15, 2015
Deposit Date Nov 28, 2014
Publicly Available Date Dec 1, 2014
Journal IEEE Transactions on Vehicular Technology
Print ISSN 0018-9545
Electronic ISSN 1939-9359
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 64
Issue 9
Pages 4268-4281
DOI https://doi.org/10.1109/tvt.2014.2362005

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