Say the channel model is an univariate FIR filter with true coefficients $h=[1,a_1, a_2 ]$. I am learning algorithms for system identification and channel equalization. For this, I am implementing programs provided in Matlab. One such Algorithm is Constant Modulus Algorithm for blind channel equalization.
Code is the link for the code : lines 80-82 present the variables $L,ChL,EqD$ which denotes the smoothing length, length of the channel & channel equalization delay respectively. I have confusion regarding the terms. Shall appreciate explanation with an example pertaining to my case.
1. Smoothing length : In text books, $L$ stands for the channel order, which for my model, Moving Average, MA(3), the order then = 3 ( I guess including the 1). FOr Autoregressive model AR(p) $p$ is the model order. I am confused if smoothing length has got anything to do with Kalman Smoother ? What is the difference, if any, between smoothing length and channel order? What will $L$ be for the FIR model that I have?
2. Length of the Channel, ChL = Is this related to number of data samples ?
3. Equalization delay, EqD = In general, I was under the impression that the model order is determined by the number of delays. So for MA(q) : $q$ is the number of delays, similarly for AR(p) : $p$ is the number of delays. Then what is Equalization delay?
- When considering blind algorithm, we assume no knowledge of the process noise that drives the model. But, When we assume that the model order is known, then does it still become blind or non-blind estimation / equalization technique?
Please correct me where wrong.