A Low-Complexity Channel Estimation Method Based on Subspace for Large-Scale MIMO Systems

Zhou, Cheng; Li, Zhengquan; Xing, Song; Wu, Qiong; Yang, Liu; Li, Baolong; Zhao, Xiaoqing

doi:10.1007/978-3-030-19156-6_36

Cited by 1 publication

(2 citation statements)

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“…The computational complexity of MMSE, EW-MMSE, and LS is M j τ p + M 2 j , M j τ p + M j , M j τ p respectively [7]and [8]. The computational complexity of Subspace-based semi-blind channel estimation for Massive MIMO system is primarily dependent on SVD, LS algorithm, and the iteration times [2], [9].…”

Section: Analysis Of Computational Complexitymentioning

confidence: 99%

“…In fact, in Massive MIMO systems, we get all the benefits of conventional MIMO with greater scale. The authors in [2] proposed that a subspace-based adaptive semi-blind channel estimation (SBCE )scheme is introduced to reduce the computational complexity and improve the accuracy of large-scale (LS)-MIMO multiuser systems. Their proposed scheme estimates the column space of the channel matrix firstly, which relies on the fast single compensation approximated power iteration (FSCAPI) algorithm to obtain the received vector subspace, and it requires lower computational complexity than singular value decomposition (SVD)or eigenvalue decomposition (EVD).…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Pilot-Based Channel Estimation Techniques for Massive MIMO Uplink Communication System

Adane

2023

Preprint

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Massive multiple input multiple output(MIMO) technology can support every cell which has a base station (BS) with a large number of antennas and simultaneous use of the same resource. Therefore, Massive MIMO systems can give high spectral (SE) and energy efciency (EE). However, in this technology, channel estimation is one of the challenges that degrade the performance that need to be addressed. In this work, we focus on the frst performance analysis of channel estimation techniques for Massive MIMO in terms of computational complexity and reliability. A practicable solution to solve the computational complexity and reliability is to study the pilot-based channel estimators such as minimum mean square error (MMSE), element-wise minimum mean square error (EW-MMSE), and least square (LS) with their SE and EE. Time division duplex (TDD) protocol, spatial channel correlation, and multicell minimum mean square error (M-MMSE) processing are also considered for reciprocity and pilot contamination problems. We evaluate the performance of channel estimation techniques for the Massive MIMO uplink system by using computational complexity, mean square error (MSE), SE, and EE as performance metrics. The simulation results show that pilot-based channel estimation has the lowest computational complexity and the best reliability when compared to blind and semi-blind channel estimation techniques. Moreover, MMSE estimators provided the lowest normalized MSE and the highest achievable SE with the M-MMSE combining scheme

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