To address the problem of slow convergence rate of the distributed least mean square algorithms for highly correlated input signals and the problem of high computational complexity of the distributed affine projection algorithms

this paper proposes two distributed subband adaptive filtering algorithms

i.e.

the incremental and diffusion subband adaptive filtering algorithms.The distributed subband adaptive filtering algorithms partition the signals of nodes to reduce their correlation so that their convergence rate can be increased.The computational complexities of the distributed subband adaptive filtering algorithms are close to those of their corresponding distributed least mean square algorithms due to decimation operations included in the filter banks used for subband partition.Simulation results show that the distributed subband adaptive filtering algorithms exhibit good performance as compared to the corresponding distributed least mean square algorithms.