Blind Estimation of Sparse Broadband Massive MIMO Channels With Ideal and One-bit ADCs | IEEE Journals & Magazine | IEEE Xplore

Blind Estimation of Sparse Broadband Massive MIMO Channels With Ideal and One-bit ADCs


Abstract:

We study the maximum likelihood problem for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure, the temporal...Show More

Abstract:

We study the maximum likelihood problem for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure, the temporal shifts across antennas in the broadband regime, and ultimately one-bit quantization at the receiver. The sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. The main advantage of this approach is the fact that the overhead due to pilot sequences can be dramatically reduced especially when operating at low SNR per antenna. In addition, as sparsity is the only assumption made about the channel, the proposed method is robust with respect to the statistical properties of the channel and data and allows the channel estimation and the separation of interfering users from adjacent base stations to be performed in rapidly time-varying scenarios. For the case of one-bit receivers, a blind channel estimation is proposed that relies on the expectation maximization algorithm. Additionally, performance limits are derived based on the Clairvoyant Cramer-Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy in the narrowband as well as the wideband regime with reasonable computational complexity and limited model assumptions.
Published in: IEEE Transactions on Signal Processing ( Volume: 66, Issue: 11, 01 June 2018)
Page(s): 2972 - 2983
Date of Publication: 30 March 2018

ISSN Information:

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Author image of Amine Mezghani
University of Texas at Austin, Austin, USA
University of California, Irvine, USA
Amine Mezghani (S’08–M’16) received the Dipl.Ing. degree in electrical engineering from the Technische Universität München, Munich, Germany, in 2006, the Diplome d’Ingénieur degree from the École Centrale Paris, Paris, France, in 2006, and the Ph.D. degree in electrical engineering from the Technische Universität München, in 2015. In Summer 2017, he joined the University of Texas at Austin as a Postdoctoral Fellow. Pr...Show More
Amine Mezghani (S’08–M’16) received the Dipl.Ing. degree in electrical engineering from the Technische Universität München, Munich, Germany, in 2006, the Diplome d’Ingénieur degree from the École Centrale Paris, Paris, France, in 2006, and the Ph.D. degree in electrical engineering from the Technische Universität München, in 2015. In Summer 2017, he joined the University of Texas at Austin as a Postdoctoral Fellow. Pr...View more
Author image of A. Lee Swindlehurst
Center for Pervasive Communications and Computing, University of California, Irvine, USA
Institute for Advanced Study, Technische Universität München, Munich, Germany
A. Lee Swindlehurst (F’04) received the B.S. and M.S. degrees in electrical engineering from Brigham Young University (BYU), Provo, UT, USA, in 1985 and 1986, respectively, and the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA, USA, in 1991. He was with the Department of Electrical and Computer Engineering, BYU, from 1990 to 2007, where he was a Department Chair from 2003 to 2006. Durin...Show More
A. Lee Swindlehurst (F’04) received the B.S. and M.S. degrees in electrical engineering from Brigham Young University (BYU), Provo, UT, USA, in 1985 and 1986, respectively, and the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA, USA, in 1991. He was with the Department of Electrical and Computer Engineering, BYU, from 1990 to 2007, where he was a Department Chair from 2003 to 2006. Durin...View more

I. Introduction

Channel estimation is recognized as one of the key issues in developing the fifth generation of wireless communication systems [2]. In particular, estimating massive MIMO millimeter wave (mmWave) channels is challenging due to the larger dimensions, larger bandwidths, hardware imperfections and faster temporal variations. In addition, such systems are expected to operate at low SNR values per antenna due to several factors like increased path-loss, hardware restrictions of the power amplifiers, larger noise bandwidths and smaller antenna sizes, which, together with the issues of pilot-contamination and carrier frequency offset, renders common pilot based estimation methods inefficient and even impossible.

Author image of Amine Mezghani
University of Texas at Austin, Austin, USA
University of California, Irvine, USA
Amine Mezghani (S’08–M’16) received the Dipl.Ing. degree in electrical engineering from the Technische Universität München, Munich, Germany, in 2006, the Diplome d’Ingénieur degree from the École Centrale Paris, Paris, France, in 2006, and the Ph.D. degree in electrical engineering from the Technische Universität München, in 2015. In Summer 2017, he joined the University of Texas at Austin as a Postdoctoral Fellow. Prior to this, he was a Postdoctoral Scholar with the Department of Electrical Engineering and Computer Science, University of California, Irvine, CA, USA. His current research interests include millimeter-wave massive MIMO, hardware constrained communication theory, and signal processing under low-resolution analog-to-digital and digital-to-analog converters. He was the recipient of the Rohde & Schwarz Outstanding Dissertation Award in 2016.
Amine Mezghani (S’08–M’16) received the Dipl.Ing. degree in electrical engineering from the Technische Universität München, Munich, Germany, in 2006, the Diplome d’Ingénieur degree from the École Centrale Paris, Paris, France, in 2006, and the Ph.D. degree in electrical engineering from the Technische Universität München, in 2015. In Summer 2017, he joined the University of Texas at Austin as a Postdoctoral Fellow. Prior to this, he was a Postdoctoral Scholar with the Department of Electrical Engineering and Computer Science, University of California, Irvine, CA, USA. His current research interests include millimeter-wave massive MIMO, hardware constrained communication theory, and signal processing under low-resolution analog-to-digital and digital-to-analog converters. He was the recipient of the Rohde & Schwarz Outstanding Dissertation Award in 2016.View more
Author image of A. Lee Swindlehurst
Center for Pervasive Communications and Computing, University of California, Irvine, USA
Institute for Advanced Study, Technische Universität München, Munich, Germany
A. Lee Swindlehurst (F’04) received the B.S. and M.S. degrees in electrical engineering from Brigham Young University (BYU), Provo, UT, USA, in 1985 and 1986, respectively, and the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA, USA, in 1991. He was with the Department of Electrical and Computer Engineering, BYU, from 1990 to 2007, where he was a Department Chair from 2003 to 2006. During 1996–1997, he held a joint appointment as a Visiting Scholar with Uppsala University and the Royal Institute of Technology in Sweden. From 2006 to 2007, he was on leave, working as the Vice President of Research for ArrayComm LLC, San Jose, CA, USA. Since 2007, he has been a Professor with the Electrical Engineering and Computer Science Department, University of California, Irvine, CA, USA, where he was the Associate Dean for Research and Graduate Studies with the Samueli School of Engineering from 2013 to 2016. During 2014–2017, he also was a Hans Fischer Senior Fellow with the Institute for Advanced Studies, Technical University of Munich, Munich, Germany. His research focuses on array signal processing for radar, wireless communications, and biomedical applications, and he has more than 300 publications in these areas. He was the inaugural Editor-in-Chief of the IEEE Journal of Selected Topics in Signal Processing. He was recipient of the 2000 IEEE W. R. G. Baker Prize Paper Award, the 2006 IEEE Communications Society Stephen O. Rice Prize in the Field of Communication Theory, the 2006 and 2010 IEEE Signal Processing Society Best Paper Awards, and the 2017 IEEE Signal Processing Society Donald G. Fink Overview Paper Award.
A. Lee Swindlehurst (F’04) received the B.S. and M.S. degrees in electrical engineering from Brigham Young University (BYU), Provo, UT, USA, in 1985 and 1986, respectively, and the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA, USA, in 1991. He was with the Department of Electrical and Computer Engineering, BYU, from 1990 to 2007, where he was a Department Chair from 2003 to 2006. During 1996–1997, he held a joint appointment as a Visiting Scholar with Uppsala University and the Royal Institute of Technology in Sweden. From 2006 to 2007, he was on leave, working as the Vice President of Research for ArrayComm LLC, San Jose, CA, USA. Since 2007, he has been a Professor with the Electrical Engineering and Computer Science Department, University of California, Irvine, CA, USA, where he was the Associate Dean for Research and Graduate Studies with the Samueli School of Engineering from 2013 to 2016. During 2014–2017, he also was a Hans Fischer Senior Fellow with the Institute for Advanced Studies, Technical University of Munich, Munich, Germany. His research focuses on array signal processing for radar, wireless communications, and biomedical applications, and he has more than 300 publications in these areas. He was the inaugural Editor-in-Chief of the IEEE Journal of Selected Topics in Signal Processing. He was recipient of the 2000 IEEE W. R. G. Baker Prize Paper Award, the 2006 IEEE Communications Society Stephen O. Rice Prize in the Field of Communication Theory, the 2006 and 2010 IEEE Signal Processing Society Best Paper Awards, and the 2017 IEEE Signal Processing Society Donald G. Fink Overview Paper Award.View more
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