Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Due to the difficulty in its exact analysis, the ergodic (average) channel capacity of correlated multiple-input multiple-output (MIMO)-fading channels is evaluated mainly by resorting to bounding techniques. Most of bounding techniques, hover, are focused on the upper bound, and exploit the information in a Wishart-distributed sample covariance matrix solely in the form of its determinant or mean value. ,  rigorously represent the determinant of form det(I + /spl gamma/S) in terms of all possible principal submatrices of S, thereby allowing us to exploit the fine structure of S to derive an upper bound for the channel capacity. To obtain a lor bound,  carefully construct a multivariate function and verify its multivariate convexity. Besides their simplicity in mathematics, the new bounds show superior tightness, as evidenced by various numerical examples.
['Q. T. Zhang', 'Xiaowei Cui', 'Xianglan Li']
I am writing a research paper on a idea Very tight capacity bounds for MIMO-correlated Rayleigh-fading channels
[{"from": "human", "value": "I am writing a research paper on a idea Very tight capacity bounds for MIMO-correlated Rayleigh-fading channels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Due to the difficulty in its exact analysis, the ergodic (average) channel capacity of correlated multiple-input multiple-output (MIMO)-fading channels is evaluated mainly by resorting to bounding techniques. Most of bounding techniques, hover, are focused on the upper bound, and exploit the information in a Wishart-distributed sample covariance matrix solely in the form of its determinant or mean value. ,  rigorously represent the determinant of form det(I + /spl gamma/S) in terms of all possible principal submatrices of S, thereby allowing us to exploit the fine structure of S to derive an upper bound for the channel capacity. To obtain a lor bound,  carefully construct a multivariate function and verify its multivariate convexity. Besides their simplicity in mathematics, the new bounds show superior tightness, as evidenced by various numerical examples."}]