TY - GEN
T1 - Optimal Channel-Aware Bayesian Estimation with 1-Bit Quantization
AU - Paudel, Santosh
AU - Chen, Hao
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - We address the optimal quantizer design problem for distributed Bayesian parameter estimation where each sensor quantizes its local observation into one bit and transmit it through non-ideal channels to the Fusion Center. We first develop an asymptotic performance limit (PL) as a performance bound for any quantizer design with a known prior. Aided by this PL, we derive the optimal quantizer and near optimal quantizer with set of observation models that achieves the PL, thus solve a set of optimal quantizer design problem for distributed estimation.
AB - We address the optimal quantizer design problem for distributed Bayesian parameter estimation where each sensor quantizes its local observation into one bit and transmit it through non-ideal channels to the Fusion Center. We first develop an asymptotic performance limit (PL) as a performance bound for any quantizer design with a known prior. Aided by this PL, we derive the optimal quantizer and near optimal quantizer with set of observation models that achieves the PL, thus solve a set of optimal quantizer design problem for distributed estimation.
KW - Cramer-Rao lower bound
KW - One-bit quantization
KW - asymptotic performance limit
KW - distributed Bayesian estimation
KW - non-ideal channel
UR - https://www.scopus.com/pages/publications/85127081407
UR - https://scholarworks.boisestate.edu/electrical_facpubs/546
U2 - 10.1109/IEEECONF53345.2021.9723208
DO - 10.1109/IEEECONF53345.2021.9723208
M3 - Conference contribution
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 752
EP - 756
BT - 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Y2 - 31 October 2021 through 3 November 2021
ER -