Model-Based Online Learning for Resource Sharing in Joint Radar-Communication Systems

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A4 Artikkeli konferenssijulkaisussa
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Date

2022

Department

Department of Signal Processing and Acoustics

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Language

en

Pages

5

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2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings, pp. 4103-4107, IEEE International Conference on Acoustics, Speech and Signal Processing ; Volume 2022-May

Abstract

The ever-increasing congestion in the radio spectrum has made coexistence and co-design for radar and communication systems an important problem to address. The radio spectrum is a rapidly time-frequency-space varying resource, and learning is required to use the spectrum and mitigate the interference. This paper proposes a model-based online learning (MBOL) framework to enable a structured way to formulate efficient online learning algorithms for resource sharing in joint radar-communication (JRC) systems. As an example, we apply the MBOL framework for allocating frequency resources in non-cooperative shared spectrum scenarios. The proposed MBOL algorithm learns a predictive model using online convex optimization (OCO) and chooses the best frequency channels in uncertain interference environments. The algorithm outperforms the considered baseline algorithms in terms of regret that quantifies the cost of learning.

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Publisher Copyright: © 2022 IEEE

Keywords

coexistence, joint radar-communication systems, model-based learning, online convex optimization

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DOI

10.1109/ICASSP43922.2022.9747269

Citation

Pulkkinen, P & Koivunen, V 2022, Model-Based Online Learning for Resource Sharing in Joint Radar-Communication Systems. in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2022-May, IEEE, pp. 4103-4107, IEEE International Conference on Acoustics, Speech, and Signal Processing, Singapore, Singapore, 23/05/2022. https://doi.org/10.1109/ICASSP43922.2022.9747269

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https://urn.fi/URN:NBN:fi:aalto-202206224172

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[article-cris] Sähkötekniikan korkeakoulu / ELEC