DocumentCode :
272014
Title :
Low-complexity linear precoding for multi-cell massive MIMO systems
Author :
Kammoun, Abla ; Muller, A. ; Bjornson, Emil ; Debbah, Mérouane
Author_Institution :
King Abdullah Univ. of Sci. & Technol., Thuwal, Saudi Arabia
fYear :
2014
fDate :
1-5 Sept. 2014
Firstpage :
2150
Lastpage :
2154
Abstract :
Massive MIMO (multiple-input multiple-output) has been recognized as an efficient solution to improve the spectral efficiency of future communication systems. However, increasing the number of antennas and users goes hand-in-hand with increasing computational complexity. In particular, the precoding design becomes involved since near-optimal precoding, such as regularized-zero forcing (RZF), requires the inversion of a large matrix. In our previous work [1] we proposed to solve this issue in the single-cell case by approximating the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are selected for optimal system performance. In this paper, we generalize this technique to multi-cell scenarios. While the optimization of the RZF precoding has, thus far, not been feasible in multi-cell systems, we show that the proposed TPE precoding can be optimized to maximize the weighted max-min fairness. Using simulations, we compare the proposed TPE precoding with RZF and show that our scheme can achieve higher throughput using a TPE order of only 3.
Keywords :
MIMO communication; computational complexity; linear codes; matrix inversion; minimax techniques; precoding; RZF; TPE order; communication systems; computational complexity; low-complexity linear precoding; matrix inversion; multicell massive MIMO systems; multiple-input multiple-output systems; near-optimal precoding; optimal system performance; regularized-zero forcing; single-cell case; spectral efficiency improvement; truncated polynomial expansion; weighted max-min fairness; Antennas; Covariance matrices; Interference; MIMO; Optimization; Polynomials; Signal to noise ratio; Massive MIMO; linear precoding; low complexity; multi-cell systems; random matrix theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
Conference_Location :
Lisbon
Type :
conf
Filename :
6952770
Link To Document :
بازگشت