  We study the performance of centralized least mean-squares (CLMS) algorithms
in wireless sensor networks where nodes transmit their data over fading
channels to a central processing unit (e.g., fusion center or cluster head),
for parameter estimation. Wireless channel impairments, including fading and
path loss, distort the transmitted data, cause link failure and degrade the
performance of the adaptive solutions. To address this problem, we propose a
novel CLMS algorithm that uses a refined version of the transmitted data and
benefits from a link failure alarm strategy to discard severely distorted data.
Furthermore, to remove the bias due to communication noise from the estimate,
we introduce a bias-elimination scheme that also leads to a lower steady-state
mean-square error. Our theoretical findings are supported by numerical
simulation results.
