Low-Complexity LSQR-Based Linear Precoding for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) using a large number of antennas at the base station (BS) is a promising technique for the next-generation 5G wireless communications. It has been shown that linear precoding schemes can achieve near-optimal performance in massive MIMO systems. However,...

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Bibliographic Details
Published in:2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall) pp. 1 - 5
Main Authors: Tian Xie, Zhaohua Lu, Qian Han, Jinguo Quan, Bichai Wang
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2015
Subjects:
Antennas
Computational complexity
MIMO
Signal to noise ratio
Simulation
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Summary:Massive multiple-input multiple-output (MIMO) using a large number of antennas at the base station (BS) is a promising technique for the next-generation 5G wireless communications. It has been shown that linear precoding schemes can achieve near-optimal performance in massive MIMO systems. However, classical linear precoding schemes such as zero- forcing (ZF) precoding suffer from high complexity due to the fact they require the matrix inversion of a large size. In this paper, we propose a low-complexity precoding scheme based on the least square QR (LSQR) method to realize the near-optimal performance of ZF precoding without matrix inversion. We show that the proposed LSQR-based precoding can reduce the complexity of ZF precoding by about one order of magnitude. Simulation results verify that the proposed LSQR-based precoding can provide a better tradeoff between complexity and performance than the recently proposed Neumann-based precoding.
DOI:10.1109/VTCFall.2015.7391016