Print Email Facebook Twitter Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation Title Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation Author Masoumi, H. (TU Delft Team Nitin Myers) Myers, N.J. (TU Delft Team Nitin Myers) Leus, G.J.T. (TU Delft Signal Processing Systems) Wahls, S. (TU Delft Team Sander Wahls) Verhaegen, M.H.G. (TU Delft Team Michel Verhaegen) Date 2022 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. Subject Sparse recoverymm-Wavechannel estimation To reference this document use: http://resolver.tudelft.nl/uuid:4c5b1846-860d-4af7-bdc4-da31335ac3ee DOI https://doi.org/10.1109/SPAWC51304.2022.9833949 Publisher IEEE Embargo date 2023-01-28 ISBN 978-1-6654-9456-4 Source Proceedings of the 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) 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. Part of collection Institutional Repository Document type conference paper Rights © 2022 H. Masoumi, N.J. Myers, G.J.T. Leus, S. Wahls, M.H.G. Verhaegen Files PDF Structured_Sensing_Matrix ... mation.pdf 1.32 MB Close viewer /islandora/object/uuid:4c5b1846-860d-4af7-bdc4-da31335ac3ee/datastream/OBJ/view