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

Zhao, Nan; Yu, Richard; Sun, Hongjian

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
• Public URL: https://durham-repository.worktribe.com/output/1449701