TY - GEN
T1 - Distributed estimation in wireless sensor networks with imperfect channel estimation
AU - Wang, Mingxi
AU - Yang, Chen Yang
PY - 2008
Y1 - 2008
N2 - In this paper, we study distributed estimation with wireless sensor networks (WSN) when channel estimation is imperfect. A robust distributed maximum likelihood (ML) estimator of the unknown parameter is proposed, which improves the performance of the traditional ML estimator with imperfect channel estimation. By maximizing the effective signal to noise ratio (SNR) at the fusion center (FC), we find that the optimal length of the training sequence is the square root of the length of the quantized observation at each node. Simulations are provided to evaluate the performance of the robust method and to validate the theoretical optimal length.
AB - In this paper, we study distributed estimation with wireless sensor networks (WSN) when channel estimation is imperfect. A robust distributed maximum likelihood (ML) estimator of the unknown parameter is proposed, which improves the performance of the traditional ML estimator with imperfect channel estimation. By maximizing the effective signal to noise ratio (SNR) at the fusion center (FC), we find that the optimal length of the training sequence is the square root of the length of the quantized observation at each node. Simulations are provided to evaluate the performance of the robust method and to validate the theoretical optimal length.
UR - https://www.scopus.com/pages/publications/67249158292
U2 - 10.1109/ICOSP.2008.4697693
DO - 10.1109/ICOSP.2008.4697693
M3 - 会议稿件
AN - SCOPUS:67249158292
SN - 9781424421794
T3 - International Conference on Signal Processing Proceedings, ICSP
SP - 2649
EP - 2652
BT - 2008 9th International Conference on Signal Processing, ICSP 2008
T2 - 2008 9th International Conference on Signal Processing, ICSP 2008
Y2 - 26 October 2008 through 29 October 2008
ER -