Authors: Yun Rui; Lei Deng; Qian Wang; Jing Li; Guoming Qian; Mingqi Li; Yingguan Wang
Addresses: Shanghai Advanced Research Institute, Chinese Academic of Science, and National Mobile Communications Research Laboratory, Southeast University, 201210, China ' Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China ' School of Computer Science, Wuhan University, 430072, China ' State Key Laboratory of Integrated Service Networks, Xidian University, 710075, China ' College of Electronic Science and Engineering in NUPT, 210003, China ' Shanghai Advanced Research Institute, Chinese Academic of Science, 201210, China ' Key Lab of Wireless Sensor Network and Communications, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences 201210, China
Abstract: In this paper, we will investigate the energy efficient power loading problem in multiple-input multiple-output singular value decomposition (MIMO-SVD) architecture. Most existing power loading schemes are developed on the assumption that a scheduler possesses perfect channel state information (CSI). However, in practice, perfect CSI is not always available. In this paper, we propose an energy-efficient power loading scheme for MIMO system under imperfect CSI, taking into account the effects of channel estimation error (CEE). Specifically, we propose two algorithms to maximise the system energy efficiency. One is a global method which can achieve the global optimal solution with high computational complexity; the other is a suboptimal method which can obtain the approximate optimal solution with lower computational complexity. Simulation results validate the effectiveness and robustness of our proposed power loading scheme.
Keywords: energy efficiency; power loading; MIMO; multiple-input multiple-output; imperfect CSI; channel state information; singular value decomposition; SVD; channel estimation error; simulation.
International Journal of Sensor Networks, 2015 Vol.18 No.3/4, pp.140 - 147
Received: 27 Sep 2012
Accepted: 18 Sep 2013
Published online: 05 Jul 2015 *