Estimation theory provides a wide variety of tools and techniques which form the basis for several key applications in modern wireless communications and signal processing. Various signal processing procedures in communication systems such as channel estimation, equalization, synchronization etc., which are also employed in MIMO-OFDM based 3G/ 4G wireless systems, are based on fundamental concepts in estimation theory. Further, recent research developments in areas such as wireless sensor networks also employ several tools from estimation theory towards distributed parameter estimation etc. Therefore, principles of estimation are naturally of a significant interest in research and industry.
A clear grasp of the basic principles of estimation can significantly enhance understanding by providing deeper insights into various techniques in signal processing and communication. Beginning with a brief overview of the basic concepts of maximum likelihood (ML) and Least Squares Estimation (LS), this course will comprehensively cover several applications of estimation theory in wireless communications such as channel estimation, equalization, MIMO, OFDM. Further, we will also cover Bayesian Estimation, MMSE, LMMSE and illustrate applications in wireless sensor networks and other allied applications such as Radar.
Week 1- Basics of Estimation, Maximum Likelihood (ML)
Week 2- Vector Estimation, Least Squares Principle
Week 3- Applications: MIMO Channel Estimation, Synchronization
Week 4- Applications: Equalization, OFDM, Sequential Estimation
Week 5- Bayesian Estimation, MMSE Framework
Week 6- Applications: Sensor Networks, Linear MMSE (LMMSE) Estimation
Week 7- Applications: MIMO Channel Estimation, Equalization, OFDM
Week 8- Kalman Filter, Application: Time Varying MIMO Channel Estimation