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Digitization, compression and reconstruction of a large-scale radio traffic for the Internet of Things

Abstract : The multiplicity of novel use cases for the Internet of Things (IoT) calls for new telecommunications protocols and modulations. However, the necessity to roll out a dedicated infrastructure to collect and process data sent by IoT devices can hinder the development of new standards for communications. In this thesis, we propose and study a technology-agnostic infrastructure aimed at collecting and processing information transmitted by IoT devices deployed on the field. Because communications from IoT devices can be sporadic, in particular when sensor networks are concerned, we consider that the signals received by the infrastructure access points are sparse in the time and/or frequency domains. Under such a condition, the Compressed Sensing (CS) framework offers the possibility to lower the sampling rate from the standard Nyquist rate. Our proposed infrastructure for data collection relies on the Multi-Rate Sampler (MRS), a CS-based sampling scheme that permits the reduction of the sampling rate and a relaxation of hardware constraints related to high-speed sampling. In this thesis, we provide a comprehensive analysis to adequately size an MRS-based infrastructure. Major results include a provision based on coprime integers to define the sampling rates of the MRS, the estimation of the noise variance and of the frequency-domain spectrum support for frequency-sparse signals, and a complete analysis of the MRS performance depending on a variety of parameters (number of samplers, signal sparsity level, bit error rate, and others).
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Submitted on : Wednesday, November 24, 2021 - 3:53:12 PM
Last modification on : Friday, November 26, 2021 - 3:13:07 AM

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  • HAL Id : tel-03447017, version 1

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Esteban Selva. Digitization, compression and reconstruction of a large-scale radio traffic for the Internet of Things. Signal and Image processing. CentraleSupélec, 2021. English. ⟨NNT : 2021CSUP0005⟩. ⟨tel-03447017⟩

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