TY - GEN
T1 - Doubly compressed diffusion LMS over adaptive networks
AU - El Khalil Harrane, Ibrahim
AU - Flamary, Remi
AU - Richard, Cedric
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Diffusion LMS is an efficient strategy for solving distributed optimization problems with cooperating agents. Nodes are interested in estimating the same parameter vector and exchange information with their neighbors to improve their local estimates. Successful implementation of such applications relies on a substantial amount of communication resources. In this paper, we introduce diffusion LMS strategies that offer significantly reduced communication load without compromising performance. We perform analyses in the mean and mean-square sense of these algorithms. Simulations results are provided to confirm the theoretical findings.
AB - Diffusion LMS is an efficient strategy for solving distributed optimization problems with cooperating agents. Nodes are interested in estimating the same parameter vector and exchange information with their neighbors to improve their local estimates. Successful implementation of such applications relies on a substantial amount of communication resources. In this paper, we introduce diffusion LMS strategies that offer significantly reduced communication load without compromising performance. We perform analyses in the mean and mean-square sense of these algorithms. Simulations results are provided to confirm the theoretical findings.
U2 - 10.1109/ACSSC.2016.7869515
DO - 10.1109/ACSSC.2016.7869515
M3 - Conference contribution
AN - SCOPUS:85016291496
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 987
EP - 991
BT - Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Y2 - 6 November 2016 through 9 November 2016
ER -