Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel diffusion subband adaptive filtering algorithm for distributed networks. To achieve a fast convergence rate and small steady-state errors, a variable step size and a new combination method is developed. For the adaptation step, the upper bound of the mean-square deviation (MSD) of the algorithm is derived and the step size is adaptive by minimizing it in order to attain the fastest convergence rate on every iteration. Furthermore, for a combination step realized by a convex combination of the neighbor-node estimates, the proposed algorithm uses the MSD, which contains information on the reliability of the estimates, to determine combination coefficients. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the convergence rate and the steady-state errors. A subband adaptive filtering algorithm is adopted for distributed estimation.The upper bound of the intermediate MSD and its recursion with local MSD are investigated.The MSD-optimal step size achieves the fastest convergence rate on every iteration.The combination coefficients are assigned using the intermediate MSD.The algorithm achieves fast convergence rate and small steady-state errors.
['Ji-Hye Seo', 'Sang Mok Jung', 'PooGyeon Park']
I am writing a research paper on a idea A diffusion subband adaptive filtering algorithm for distributed estimation using variable step size and new combination method based on the MSD
[{"from": "human", "value": "I am writing a research paper on a idea A diffusion subband adaptive filtering algorithm for distributed estimation using variable step size and new combination method based on the MSD"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel diffusion subband adaptive filtering algorithm for distributed networks. To achieve a fast convergence rate and small steady-state errors, a variable step size and a new combination method is developed. For the adaptation step, the upper bound of the mean-square deviation (MSD) of the algorithm is derived and the step size is adaptive by minimizing it in order to attain the fastest convergence rate on every iteration. Furthermore, for a combination step realized by a convex combination of the neighbor-node estimates, the proposed algorithm uses the MSD, which contains information on the reliability of the estimates, to determine combination coefficients. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the convergence rate and the steady-state errors. A subband adaptive filtering algorithm is adopted for distributed estimation.The upper bound of the intermediate MSD and its recursion with local MSD are investigated.The MSD-optimal step size achieves the fastest convergence rate on every iteration.The combination coefficients are assigned using the intermediate MSD.The algorithm achieves fast convergence rate and small steady-state errors."}]