  This letter presents an improved version of diffusion least mean ppower (LMP)
algorithm for distributed estimation. Instead of sum of mean square errors, a
weighted sum of mean square error is defined as the cost function for global
and local cost functions of a network of sensors. The weight coefficients are
updated by a simple steepest-descent recursion to minimize the error signal of
the global and local adaptive algorithm. Simulation results show the advantages
of the proposed weighted diffusion LMP over the diffusion LMP algorithm
specially in the non-uniform noise conditions in a sensor network.
