Millimeter Wave MIMO channel estimation based on adaptive compressed sensing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multiple-input multiple-output (MIMO) systems are well suited for millimeter-wave (mmWave) wireless communications where large antenna arrays can be integrated in small form factors due to tiny wavelengths, thereby providing high array gains while supporting spatial multiplexing, beamforming, or antenna diversity. It has been shown that mmWave channels exhibit sparsity due to the limited number of dominant propagation paths, thus compressed sensing techniques can be leveraged to conduct channel estimation at mmWave frequencies. This paper presents a novel approach of constructing beamforming dictionary matrices for sparse channel estimation using the continuous basis pursuit (CBP) concept, and proposes two novel low-complexity algorithms to exploit channel sparsity for adaptively estimating multipath channel parameters in mmWave channels. We verify the performance of the proposed CBP-based beamforming dictionary and the two algorithms using a simulator built upon a three-dimensional mmWave statistical spatial channel model, NYUSIM, that is based on real-world propagation measurements. Simulation results show that the CBP-based dictionary offers substantially higher estimation accuracy and greater spectral efficiency than the grid-based counterpart introduced by previous researchers, and the algorithms proposed here render better performance but require less computational effort compared with existing algorithms.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-53
Number of pages7
ISBN (Electronic)9781509015252
DOIs
StatePublished - Jun 29 2017
Event2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017 - Paris, France
Duration: May 21 2017May 25 2017

Other

Other2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017
CountryFrance
CityParis
Period5/21/175/25/17

Fingerprint

Compressed sensing
Channel estimation
Millimeter waves
Glossaries
Beamforming
Multipath propagation
Antenna arrays
Multiplexing
Simulators
Antennas
Wavelength
Communication

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture

Cite this

Sun, S., & Rappaport, T. (2017). Millimeter Wave MIMO channel estimation based on adaptive compressed sensing. In 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017 (pp. 47-53). [7962632] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCW.2017.7962632

Millimeter Wave MIMO channel estimation based on adaptive compressed sensing. / Sun, Shu; Rappaport, Theodore.

2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 47-53 7962632.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sun, S & Rappaport, T 2017, Millimeter Wave MIMO channel estimation based on adaptive compressed sensing. in 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017., 7962632, Institute of Electrical and Electronics Engineers Inc., pp. 47-53, 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017, Paris, France, 5/21/17. https://doi.org/10.1109/ICCW.2017.7962632
Sun S, Rappaport T. Millimeter Wave MIMO channel estimation based on adaptive compressed sensing. In 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 47-53. 7962632 https://doi.org/10.1109/ICCW.2017.7962632
Sun, Shu ; Rappaport, Theodore. / Millimeter Wave MIMO channel estimation based on adaptive compressed sensing. 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 47-53
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