I'm dealing with a channel equalization problem where the channel is modeled as a WSS process.
I understand LMS utilities a Wiener-like approach, ie it converges to the optimal (wiener) solution.
I understand RLS converges to the normal solution, assuming the statistics of the WSS process are unknown.
I would like to know:
- What class of predictors can Wiener, LMS, and RLS be classified within? Is it correct to define Wiener as 'Optimal (in MSE sense)', LMS as 'Stochastic gradient predictors', and RLS as a 'Linear predictor'? And what class of filters?
- When is one solution preferred to the other and why?
- In particular, when defining the cost function with the MSE, what can we achieve? And when we define it simply with the squared error?