Performance analysis of quantized incremental LMS algorithm for distributed adaptive estimation

Recently distributed adaptive estimation algorithms have been proposed as a solution to the issue of linear estimation over distributed networks. In all previous works, the performance of such algorithms is considered only for infinite-precision arithmetic implementation. In this paper we study the performance of distributed incremental least mean square (DILMS) estimation algorithm when it is implemented in finite-precision arithmetic. To this aim, we first derive the quantized version of the DILMS algorithm. Then a spatial–temporal energy conservation argument is used to derive theoretical expressions that evaluate the steady-state performance of individual nodes in the network. Simulation results show that there is a good match between the theory and simulation.