SparSDR: Sparsity-proportional Backhaul and Compute for SDRs

Moein Khazraee, Yeswanth Guddeti, Sam Crow, Alex C. Snoeren, Kirill Levchenko, Dinesh Bharadia, Aaron Schulman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present SparSDR, a resource-efficient architecture for software-defined radios whose backhaul bandwidth and compute power requirements scale in inverse proportion to the sparsity (in time and frequency) of the signals received. SparSDR requires dramatically fewer resources than existing approaches to process many popular protocols while retaining both flexibility and fidelity. We demonstrate that our approach has negligible impact on signal quality, receiver sensitivity, and processing latency. The SparSDR architecture makes it possible to capture signals across bandwidths far wider than the capacity of a radio’s backhaul through the addition of lightweight frontend processing and corresponding backend reconstruction to restore the signals to their original sample rate. We employ SparSDR to develop two wideband applications running on a USRP N210 and a Raspberry Pi 3+: an IoT sniffer that scans 100 MHz of bandwidth and decodes received BLE packets, and a wideband Cloud SDR receiver that requires only residential-class Internet uplink capacity. We show that our SparSDR implementation fits in the constrained resources of popular low-cost SDR platforms, such as the AD Pluto.

Original languageEnglish (US)
Title of host publicationMobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
PublisherAssociation for Computing Machinery, Inc
Pages391-403
Number of pages13
ISBN (Electronic)9781450366618
DOIs
StatePublished - Jun 12 2019
Event17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019 - Seoul, Korea, Republic of
Duration: Jun 17 2019Jun 21 2019

Publication series

NameMobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

Conference

Conference17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019
CountryKorea, Republic of
CitySeoul
Period6/17/196/21/19

Keywords

  • Cloud SDR
  • FFT
  • FPGA
  • Software Defined Radio
  • Sparsity

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

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  • Cite this

    Khazraee, M., Guddeti, Y., Crow, S., Snoeren, A. C., Levchenko, K., Bharadia, D., & Schulman, A. (2019). SparSDR: Sparsity-proportional Backhaul and Compute for SDRs. In MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (pp. 391-403). (MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services). Association for Computing Machinery, Inc. https://doi.org/10.1145/3307334.3326088