TY - GEN
T1 - Reducing Recovery Error in Compressive Sensing with Limited Number of Base Stations
AU - Pakawanwong, Prompong
AU - Suppakitpaisarn, Vorapong
AU - Xu, Liwen
AU - Kakimura, Naonori
N1 - Funding Information:
This work was supported by JST ERATO Grant Number JPMJER1201, Japan.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
KW - Approximation Algorithms
KW - Compressive Sensing
KW - Mathematical Program
KW - Sensor Coverage
KW - Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85046444388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046444388&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2017.8253930
DO - 10.1109/GLOCOM.2017.8253930
M3 - Conference contribution
AN - SCOPUS:85046444388
T3 - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
Y2 - 4 December 2017 through 8 December 2017
ER -