Oversampled Adaptive Sensing via a Predefined Codebook

Bereyhi A, Asaad S, Müller R (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2021 1st IEEE International Online Symposium on Joint Communications and Sensing, JC and S 2021

Event location: Dresden DE

ISBN: 9781665441070

DOI: 10.1109/JCS52304.2021.9376332

Abstract

Oversampled adaptive sensing (OAS) is a Bayesian framework recently proposed for effective sensing of structured signals in a time-limited setting. In contrast to the conventional blind oversampling, OAS uses the prior information on the signal to construct posterior beliefs sequentially. These beliefs help in constructive oversampling which iteratively evolves through a sequence of time sub-frames. The initial studies of OAS consider the idealistic assumption of full control on sensing coefficients which is not feasible in many applications. In this work, we extend the initial investigations on OAS to more realistic settings in which the sensing coefficients are selected from a predefined set of possible choices, referred to as the codebook. We extend the OAS framework to these settings and compare its performance with classical non-adaptive approaches.

Authors with CRIS profile

Ali Bereyhi Lehrstuhl für Digitale Übertragung Saba Asaad Lehrstuhl für Digitale Übertragung Ralf Müller Professur für Informationsübertragung

How to cite

APA:

Bereyhi, A., Asaad, S., & Müller, R. (2021). Oversampled Adaptive Sensing via a Predefined Codebook. In 2021 1st IEEE International Online Symposium on Joint Communications and Sensing, JC and S 2021. Dresden, DE: Institute of Electrical and Electronics Engineers Inc..

MLA:

Bereyhi, Ali, Saba Asaad, and Ralf Müller. "Oversampled Adaptive Sensing via a Predefined Codebook." Proceedings of the 1st IEEE International Online Symposium on Joint Communications and Sensing, JC and S 2021, Dresden Institute of Electrical and Electronics Engineers Inc., 2021.

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