TY - JOUR
T1 - Optimal capacity allocation for sampled networked systems
AU - Chen, Xudong
AU - Belabbas, Mohamed Ali
AU - Başar, Tamer
N1 - Funding Information:
Research of T. Başar was supported in part by the U.S. Air Force Office of Scientific Research (AFOSR) MURI grant FA9550-10-1-0573 and in part by National Science Foundation (NSF) grant CCF 11-11342; Research of M.-A. Belabbas was supported in part by National Science Foundation (NSF)ECCS 13-07791 and in part by National Science Foundation (NSF)ECCS CAREER 13-51586. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Tongwen Chen under the direction of Editor Ian R. Petersen.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/11
Y1 - 2017/11
N2 - We consider the problem of estimating the states of weakly coupled linear systems from sampled measurements. We assume that the total capacity available to the sensors to transmit their samples to a network manager in charge of the estimation is bounded above, and that each sample requires the same amount of communication. Our goal is then to find an optimal allocation of the capacity to the sensors so that the time-averaged estimation error is minimized. We show that when the total available channel capacity is large, this resource allocation problem can be recast as a strictly convex optimization problem, and hence there exists a unique optimal allocation of the capacity. We further investigate how this optimal allocation varies as the available capacity increases. In particular, we show that if the coupling among the subsystems is weak, then the sampling rate allocated to each sensor is nondecreasing in the total sampling rate, and is strictly increasing if and only if the total sampling rate exceeds a certain threshold.
AB - We consider the problem of estimating the states of weakly coupled linear systems from sampled measurements. We assume that the total capacity available to the sensors to transmit their samples to a network manager in charge of the estimation is bounded above, and that each sample requires the same amount of communication. Our goal is then to find an optimal allocation of the capacity to the sensors so that the time-averaged estimation error is minimized. We show that when the total available channel capacity is large, this resource allocation problem can be recast as a strictly convex optimization problem, and hence there exists a unique optimal allocation of the capacity. We further investigate how this optimal allocation varies as the available capacity increases. In particular, we show that if the coupling among the subsystems is weak, then the sampling rate allocated to each sensor is nondecreasing in the total sampling rate, and is strictly increasing if and only if the total sampling rate exceeds a certain threshold.
KW - Algebraic Riccati equations
KW - Capacity filtration
KW - Mean squared error estimation
KW - Optimal capacity allocation
KW - Sampled networked systems
UR - http://www.scopus.com/inward/record.url?scp=85027553146&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027553146&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2017.07.039
DO - 10.1016/j.automatica.2017.07.039
M3 - Article
AN - SCOPUS:85027553146
VL - 85
SP - 100
EP - 112
JO - Automatica
JF - Automatica
SN - 0005-1098
ER -