Sparsity Enforcing with Toeplitz Matrix Reconstruction Method for Mmwave Ul Channel Estimation with One-Bit Adcs
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A4 Artikkeli konferenssijulkaisussa
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Date
2022
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en
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5
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2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022, pp. 141-145, Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Abstract
One-bit analog-to-digital converters enable digital beamforming in millimeter wave (mmWave) multi-input multi-output communication systems with low power consumption. Con-ventional signal processing tasks like channel estimation, though, are challenging due to the extreme quantization, making it challenging to implement uplink (UL) multiuser re-ceivers. We reformulate the UL channel estimation prob-lem as a multiplication of two specific matrices, and then we leverage Toeplitz matrix reconstruction in conjunction with the angular domain sparsity of the UL channel to recover UL channel via solving a properly designed optimization prob-lem. Our new approach is called the sparsity enforcing with Toeplitz matrix reconstruction (SE-TMR) method. Numerical simulations are carried out to showcase the advantages of SE-TMR over existing competitive methods in terms of normal-ized mean squared error in clustered narrowband channels.Description
Funding Information: This work was supported in part by Academy of Finland under Research Grant 319822. Publisher Copyright: © 2022 IEEE.
Keywords
angular domain, Multi-user MIMO, one-bit analog-to-digital converter, Toeplitz matrix reconstruction, uplink channel esti-mation
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Citation
Esfandiari, M, Vorobyov, S A & Heath, R W 2022, Sparsity Enforcing with Toeplitz Matrix Reconstruction Method for Mmwave Ul Channel Estimation with One-Bit Adcs . in 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022 . Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, IEEE, pp. 141-145, IEEE Sensor Array and Multichannel Signal Processing Workshop, Trondheim, Norway, 20/06/2022 . https://doi.org/10.1109/SAM53842.2022.9827806