Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Standard linear diversity combining techniques are not effective in combating fading in the presence of non-Gaussian noise. An adaptive spatial diversity receiver is developed for wireless communication channels with slow, flat fading and additive non-Gaussian noise. The noise is modeled as a mixture of Gaussian distributions and the expectation-maximization (EM) algorithm is used to derive estimates for the model parameters. The transmitted signals are detected using a likelihood ratio test based on the parameter estimates. The new adaptive receiver converges rapidly, its bit error rate performance is very close to optimum when relatively short training sequences are used, and it appears to be relatively insensitive to mismatch beten the noise model and the actual noise distribution. Simulation results are included that illustrate various aspects of the adaptive receiver performance.
['Rick S. Blum', 'Richard J. Kozick', 'Brian M. Sadler']
I am writing a research paper on a idea An adaptive spatial diversity receiver for non-Gaussian interference and noise
[{"from": "human", "value": "I am writing a research paper on a idea An adaptive spatial diversity receiver for non-Gaussian interference and noise"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Standard linear diversity combining techniques are not effective in combating fading in the presence of non-Gaussian noise. An adaptive spatial diversity receiver is developed for wireless communication channels with slow, flat fading and additive non-Gaussian noise. The noise is modeled as a mixture of Gaussian distributions and the expectation-maximization (EM) algorithm is used to derive estimates for the model parameters. The transmitted signals are detected using a likelihood ratio test based on the parameter estimates. The new adaptive receiver converges rapidly, its bit error rate performance is very close to optimum when relatively short training sequences are used, and it appears to be relatively insensitive to mismatch beten the noise model and the actual noise distribution. Simulation results are included that illustrate various aspects of the adaptive receiver performance."}]