Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation

Hamed Masoumi; Nitin Jonathan Myers; Geert Leus; Sander Wahls; Michel Verhaegen

doi:10.1109/SPAWC51304.2022.9833949

Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation

Hamed Masoumi, Nitin Jonathan Myers, Geert Leus, Sander Wahls, Michel Verhaegen

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	978-1-6654-9455-7
ISBN (Print)	978-1-6654-9456-4
DOIs	https://doi.org/10.1109/SPAWC51304.2022.9833949
Publication status	Published - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Sparse recovery
mm-Wave
channel estimation

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10.1109/SPAWC51304.2022.9833949

Structured_Sensing_Matrix_Design_for_In-sector_Compressed_mmWave_Channel_EstimationFinal published version, 1.25 MB

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title = "Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation",

abstract = "Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.",

keywords = "Sparse recovery, mm-Wave, channel estimation",

author = "Hamed Masoumi and Myers, {Nitin Jonathan} and Geert Leus and Sander Wahls and Michel Verhaegen",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.",

year = "2022",

doi = "10.1109/SPAWC51304.2022.9833949",

language = "English",

isbn = "978-1-6654-9456-4",

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Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation. / Masoumi, Hamed ; Myers, Nitin Jonathan ; Leus, Geert et al.
Proceedings of the 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC). IEEE, 2022. 9833949.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation

AU - Masoumi, Hamed

AU - Myers, Nitin Jonathan

AU - Leus, Geert

AU - Wahls, Sander

AU - Verhaegen, Michel

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2022

Y1 - 2022

N2 - Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.

AB - Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.

KW - Sparse recovery

KW - mm-Wave

KW - channel estimation

U2 - 10.1109/SPAWC51304.2022.9833949

DO - 10.1109/SPAWC51304.2022.9833949

M3 - Conference contribution

SN - 978-1-6654-9456-4

BT - Proceedings of the 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

PB - IEEE

ER -

Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation

Abstract

Bibliographical note

Keywords

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