Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The existing self coherence restoral (SCORE) beamforming techniques have been shown to be capable of blindly extracting a desired signal in the presence of unknown noise and interference by exploiting the cyclostationarity of the signal of interest. The versions of SCORE which offer the best convergence properties require computation of the observed data cyclic correlation matrix. This can be a large computational burden, particularly if the number of antennas in the array is large.  introduces a method which requires only a column-wise subset of the cyclic correlation matrix. It is shown that in many cases the new method performs as ll as existing SCORE methods yet requires many fer computations.
['Thomas E. Biedka']
I am writing a research paper on a idea A method for reducing computations in cyclostationarity-exploiting beamforming
[{"from": "human", "value": "I am writing a research paper on a idea A method for reducing computations in cyclostationarity-exploiting beamforming"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The existing self coherence restoral (SCORE) beamforming techniques have been shown to be capable of blindly extracting a desired signal in the presence of unknown noise and interference by exploiting the cyclostationarity of the signal of interest. The versions of SCORE which offer the best convergence properties require computation of the observed data cyclic correlation matrix. This can be a large computational burden, particularly if the number of antennas in the array is large.  introduces a method which requires only a column-wise subset of the cyclic correlation matrix. It is shown that in many cases the new method performs as ll as existing SCORE methods yet requires many fer computations."}]