  We study the effect of fading in the communication channels between sensor
nodes on the performance of the incremental least mean square (ILMS) algorithm,
and derive steady state performance metrics, including the mean-square
deviation (MSD), excess mean-square error (EMSE) and meansquare error (MSE). We
obtain conditions for mean convergence of the ILMS algorithm, and show that in
the presence of fading channels, the ILMS algorithm is asymptotically biased.
Furthermore, the dynamic range for mean stability depends only on the mean
channel gain, and under simplifying technical assumptions, we show that the
MSD, EMSE and MSE are non-decreasing functions of the channel gain variances,
with mean-square convergence to the steady states possible only if the channel
gain variances are limited. We derive sufficient conditions to ensure
mean-square convergence, and verify our results through simulations.
