Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive subband structures have been proposed with the objective of increasing the convergence speed and/or reducing the computational complexity of conventional adaptive algorithms, mainly for applications which require a large number of adaptive coefficients. ,  present a non-uniform subband structure with critical sampling, which is able of modeling an arbitrary FIR system with reduced aliasing. An LMS-type adaptation algorithm with normalized step-sizes, which works at the lost downsampling rate and minimizes the average of the subband squared-errors, is derived for the proposed structure. A convergence analysis of the adaptation algorithm is presented, from which the steady-state mean-square error can be estimated.
['Mariane R. Petraglia', 'Paulo B. Batalheiro']
I am writing a research paper on a idea Mean-Square Error Analysis of a Non-Uniform Subband Adaptive Filtering Structure with Critical Sampling
[{"from": "human", "value": "I am writing a research paper on a idea Mean-Square Error Analysis of a Non-Uniform Subband Adaptive Filtering Structure with Critical Sampling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive subband structures have been proposed with the objective of increasing the convergence speed and/or reducing the computational complexity of conventional adaptive algorithms, mainly for applications which require a large number of adaptive coefficients. ,  present a non-uniform subband structure with critical sampling, which is able of modeling an arbitrary FIR system with reduced aliasing. An LMS-type adaptation algorithm with normalized step-sizes, which works at the lost downsampling rate and minimizes the average of the subband squared-errors, is derived for the proposed structure. A convergence analysis of the adaptation algorithm is presented, from which the steady-state mean-square error can be estimated."}]