Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  use the method of Lagrange multipliers to derive the optimum ighting for adaptive array antennas in a spectrum-sharing environment. The formula proposed for calculating the ights yields the structure of the special cases of conventional models as described by Frost and Applebaum. An evaluation of performance is given in terms of the signal to interference por ratio and the results are compared with those of a matched filter, a null-space based method, and a linearly constraint adaptive array.  also show the transmit por control based on the residual interference level works ll and reveal the relationship beten the channel estimation accuracy and achievable channel capacity under the spectrum sharing environments.
['Hiromasa Fujii', 'Takahiro Asai', 'Tomoyuki Ohya']
I am writing a research paper on a idea Optimum Weighting for Adaptive Array Antennas under Spectrum Sharing Environments
[{"from": "human", "value": "I am writing a research paper on a idea Optimum Weighting for Adaptive Array Antennas under Spectrum Sharing Environments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  use the method of Lagrange multipliers to derive the optimum ighting for adaptive array antennas in a spectrum-sharing environment. The formula proposed for calculating the ights yields the structure of the special cases of conventional models as described by Frost and Applebaum. An evaluation of performance is given in terms of the signal to interference por ratio and the results are compared with those of a matched filter, a null-space based method, and a linearly constraint adaptive array.  also show the transmit por control based on the residual interference level works ll and reveal the relationship beten the channel estimation accuracy and achievable channel capacity under the spectrum sharing environments."}]