Performance Analysis of Incremental LMS Over Flat Fading Channels

We study the effect of fading in the communication channels between sensor nodes on the performance of incremental least mean square (ILMS) algorithm, and derive steady state performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE) and mean-square 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 the 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.

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