Sedghi, T., Shafei, S. (2013). Application of Non-Linear Functions at Distribution of Output SINR Gaussian Interference Channels. Journal of Artificial Intelligence in Electrical Engineering, 2(7), 16-23.

Tohid Sedghi; Shahin Shafei. "Application of Non-Linear Functions at Distribution of Output SINR Gaussian Interference Channels". Journal of Artificial Intelligence in Electrical Engineering, 2, 7, 2013, 16-23.

Sedghi, T., Shafei, S. (2013). 'Application of Non-Linear Functions at Distribution of Output SINR Gaussian Interference Channels', Journal of Artificial Intelligence in Electrical Engineering, 2(7), pp. 16-23.

Sedghi, T., Shafei, S. Application of Non-Linear Functions at Distribution of Output SINR Gaussian Interference Channels. Journal of Artificial Intelligence in Electrical Engineering, 2013; 2(7): 16-23.

Application of Non-Linear Functions at Distribution of Output SINR Gaussian Interference Channels

We have examined the convergence behavior of the LSCMA in some simple environments. Algorithms such as Multi¬ Target CMA, Multistage CMA, and Iterative Least Squares with Projection can be used for this purpose. The results presented here can form a basis for analysis of these multi-signal extraction techniques. Clearly, the variance and distribution of output SINR obtained with the LSCMA is also an important area for investigation. We finally comment on the hard-limit non-linearity. For high SIR, the hard-limiter is the optimal non-linearity when the desired signal has a constant envelope. However, at low SIR other non-linearities can yield greater SIR gain. Thus, it is possible that non-linear functions other than the hard-limit can be used to develop blind adaptive algorithms, which converge faster for low initial SINR.

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