I am having an LMS block with 6 filter coefficients.
The value of filter coefficients are
0.0001
0.00045
0.2535
0.546536
0.0000243
0.3423
I have tried to implement the LMS algorithm in floating-point with $\mu=0.01$. LMS algorithm implemented in floating point is based on the below equations:(from figure) : v(n)=wT(n)u(n), e(n)=d(n)-v(n), w(n+1)=w(n)+mu e(n)*u(n) ,which is the weight updation equation. Here d(n) is the desired signal,u(n) is the input to lms block,v(n) is the lms filter output ,w(n) is the adaptive filter weights and e(n) is the error.
I have given a set of desired samples and input samples to the LMS filter and the LMS filter output data samples were analyzed. Now, to implement the same LMS filter in fixed-point representation, I will have to do scaling and rounding. Is scaling of filter coefficients by some factor $2^{s_1}$ enough and $\mu$ value be represented like $2^{-6}$ or should we consider any other factors while doing scaling operations. Finally, how to check the fixed-point implementation of LMS filter worked correctly?