Signal Processing for Communication Systems
Key focus: Understand step by step, the least squares estimator for parameter estimation. Hands-on example to fit a curve using least squares estimation Background on least squares estimator The various estimation concepts/techniques like Maximum Likelihood Estimation (MLE), Minimum Variance Unbiased Estimation (MVUE), Best Linear Unbiased Estimator (BLUE) – all falling under the umbrella of classical … Read more
As discussed in the introduction to estimation theory, the goal of an estimation algorithm is to give an estimate of random variable(s) that is unbiased and has minimum variance. This criteria is reproduced here for reference $latex E\left\{\hat{f}_0 \right\} = f_0 $ $latex \sigma^{2}_{\hat{f}_0}= E \left\{ ( \hat{f}_0 – E [ \hat{f}_0 ] )^2 \right\} … Read more
Estimator bias: Systematic deviation from the true value, either consistently overestimating or underestimating the parameter of interest. Estimator Bias: Biased or Unbiased Consider a simple communication system model where a transmitter transmits continuous stream of data samples representing a constant value – ‘A’. The data samples sent via a communication channel gets added with White … Read more
Introduction to estimation theory: what an estimator is, bias vs variance, and how the path leads to Fisher information and the Cramér–Rao bound.