Reducing Recovery Error in Compressive Sensing with Limited Number of Base Stations

Prompong Pakawanwong, Vorapong Suppakitpaisarn, Liwen Xu, Naonori Kakimura

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

Abstract

We aim to decrease a communication cost of a network that uses compressive sensing, a technique that allows us to recover global information of sparse data by using only a small set of samples. Despite efficiency of the technique, collecting information from all samples is usually costly. Because the samples from previous works usually spread around the network, setting up a number of base stations does not help reducing the cost. In this paper, we propose a method that can utilize the base stations, while aiming to minimize the recovery error of compressive sensing. Based on theorem by Xu et al., which is for cost-aware compressive sensing, we derive a mathematical program that aims to maximize the preciseness in the setting. Then, we approximate the program by a convex quadratic program and prove that the approximation ratio is 0.63. Our simulation results show that, by using the coverage, the sampling error is decreased by at most thirty times.

Original languageEnglish
Title of host publication2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-7
Number of pages7
Volume2018-January
ISBN (Electronic)9781509050192
DOIs
Publication statusPublished - 2018 Jan 10
Externally publishedYes
Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
Duration: 2017 Dec 42017 Dec 8

Other

Other2017 IEEE Global Communications Conference, GLOBECOM 2017
CountrySingapore
CitySingapore
Period17/12/417/12/8

Fingerprint

Base stations
Costs
Sampling
Communication

Keywords

  • Approximation Algorithms
  • Compressive Sensing
  • Mathematical Program
  • Sensor Coverage
  • Sensor Networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

Cite this

Pakawanwong, P., Suppakitpaisarn, V., Xu, L., & Kakimura, N. (2018). Reducing Recovery Error in Compressive Sensing with Limited Number of Base Stations. In 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings (Vol. 2018-January, pp. 1-7). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2017.8253930

Reducing Recovery Error in Compressive Sensing with Limited Number of Base Stations. / Pakawanwong, Prompong; Suppakitpaisarn, Vorapong; Xu, Liwen; Kakimura, Naonori.

2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-7.

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

Pakawanwong, P, Suppakitpaisarn, V, Xu, L & Kakimura, N 2018, Reducing Recovery Error in Compressive Sensing with Limited Number of Base Stations. in 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-7, 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, Singapore, 17/12/4. https://doi.org/10.1109/GLOCOM.2017.8253930
Pakawanwong P, Suppakitpaisarn V, Xu L, Kakimura N. Reducing Recovery Error in Compressive Sensing with Limited Number of Base Stations. In 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-7 https://doi.org/10.1109/GLOCOM.2017.8253930
Pakawanwong, Prompong ; Suppakitpaisarn, Vorapong ; Xu, Liwen ; Kakimura, Naonori. / Reducing Recovery Error in Compressive Sensing with Limited Number of Base Stations. 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-7
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