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Spectrum Blind Recovery and Application in Non-uniform Sampling Based Automatic Modulation Classifier

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Abstract

Multi-standard wireless communication radios (MWCRs) capable of digitizing wideband signal to support wide variety of data-intensive services are desired. Limited reconfigurability of the analog front end along with hardware and cost constraints of high-speed analog-to-digital converters have generated significant interest in non-uniform (sub-Nyquist) sampling (NUS) and digital reconstruction-based MWCRs. Existing reconstruction approaches require prior knowledge of sparsity which may not be available in the dynamic spectrum environment. To alleviate this problem, a blind and adaptive reconstruction approach has been proposed in this paper. The proposed approach employs multi-armed Bandit framework to estimate the spectrum occupancy. Simulation results show that the average normalized mean square error of the proposed approach is 10–20% lower than other reconstruction approaches. Next, cumulant and machine learning-based automatic modulation classifier (AMC) is designed to validate the usefulness of the proposed approach in practical applications. Simulation results show that the classification accuracy of NUS-based AMC approaches, uniform sampling-based AMC with increase in signal-to-noise ratio and proposed approach is superior to others. The simulation results are further verified on the proposed universal software radio peripheral testbed in real radio environment. Experimental results demonstrate the close resemblance with simulation results.

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Acknowledgements

This work is supported by the funding received from Council of Scientific and Industrial Research (CSIR), India, under Junior Research Fellowship (JRF) Scheme (Grant No. 09/1117(0005)/2017-EMR-I) and Department of Science and Technology (DST), India, under Innovation in Science Pursuit for Inspired Research (INSPIRE) faculty fellowship (Grant No. IFA-14-ENG-105).

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, 110020, India

    Himani Joshi & Sumit J. Darak

  2. SCEE, IETR, CentraleSupélec, CS 47601, 35576, Cesson-Sévigné Cedex, France

    Yves Louët

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  1. Himani Joshi
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  2. Sumit J. Darak
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  3. Yves Louët
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Correspondence to Himani Joshi.

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Joshi, H., Darak, S.J. & Louët, Y. Spectrum Blind Recovery and Application in Non-uniform Sampling Based Automatic Modulation Classifier. Circuits Syst Signal Process 37, 3457–3486 (2018). https://doi.org/10.1007/s00034-017-0715-2

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  • DOI: https://doi.org/10.1007/s00034-017-0715-2

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