Bayesian or Minimum Mean Squared Error (MMSE) estimation incorporates prior information for the parameter to be estimated and hence yields an improved estimation performance. It also has significant practical applications in MIMO-OFDM based 3G/ 4G wireless systems for channel estimation, equalization as well as in wireless sensor networks (WSNs) and cognitive radio systems. This is a sequel course in estimation and will cover the Bayesian i.e. Minimum Mean Squared Error (MMSE) framework for estimation and applications to MIMO/ OFDM wireless communications. However, it is NOT necessary for the student to have done the previous course as all the topics will be covered starting from the fundamentals. Thus students can independently do this course without knowledge of the previous course on Maximum Likelihood (ML) estimation.
Week 1: Basics of Estimation, MMSE Principle, Properties –Variance of Estimate
Week 2: Wireless Flat-Fading Channel Estimation, Pilot-based MMSE Estimate, Properties, Example of Channel Estimation.