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Estimation for Wireless Communications:
MIMO/OFDM Cellular and Sensor Networks

Estimation for Wireless Communications: MIMO/OFDM Cellular and Sensor Networks. Instructor: Prof. Aditya K. Jagannatham, Department of Electrical Engineering, IIT Kanpur. 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. (from nptel.ac.in)

Sensor Network and Noisy Observation Model


Lecture 01 - Basics - Sensor Network and Noisy Observation Model
Lecture 02 - Likelihood Function and Maximum Likelihood (ML) Estimate
Lecture 03 - Properties of Maximum Likelihood (ML) Estimate - Mean and Unbiasedness
Lecture 04 - Properties of Maximum Likelihood (ML) Estimate - Variance and Spread around Mean
Lecture 05 - Reliability of Maximum Likelihood (ML) Estimate - Number of Samples of Required
Lecture 06 - Estimation of Complex Parameters - Symmetric Zero Mean Complex Gaussian Noise
Lecture 07 - Wireless Fading Channel Estimation - Pilot Symbols and Likelihood Function
Lecture 08 - Wireless Fading Channel Estimation - Pilot Training based Maximum Likelihood Estimate
Lecture 09 - Wireless Fading Channel Estimation - Mean and Variance of Pilot Training based Maximum Likelihood
Lecture 10 - Example of Wireless Fading Channel Estimation for Downlink Mobile Communication
Lecture 11 - Cramer Rao Bound (CRB) for Parameter Estimation
Lecture 12 - Cramer Rao Bound (CRB) Example - Wireless Sensor Network
Lecture 13 - Vector Parameter Estimation - System Model for Multi Antenna Downlink Channel Estimation
Lecture 14 - Likelihood Function and Least Squares Cost Function for Vector Parameter Estimation
Lecture 15 - Least Squares Cost Function for Vector Parameter Estimation, Vector Derivative Gradient
Lecture 16 - Least Squares Solution, Maximum Likelihood (ML) Estimate, Pseudo Inverse
Lecture 17 - Properties of Least Squares Estimate - Mean Covariance and Distribution
Lecture 18 - Least Squares Multi Antenna Downlink Maximum Likelihood Channel Estimation
Lecture 19 - Multiple Input Multiple Output (MIMO) Channel Estimation - Least Squares Maximum Likelihood
Lecture 20 - Example of Least Squares MIMO Channel Estimation
Lecture 21 - Channel Equalization and Inter Symbol Interference (ISI) Model
Lecture 22 - Least Squares based Zero Forcing Channel Equalizer
Lecture 23 - Example of ISI Channel and Least Squares based Zero Forcing
Lecture 24 - Equalization and Approximation Error for Zero Forcing Channel Equalizer
Lecture 25 - Example of Equalization and Approximation Error for Zero Forcing Channel Equalizer
Lecture 26 - Introduction to OFDM - Cyclic Prefix (CP) and Circular Convolution
Lecture 27 - Introduction to OFDM - FFT at Receiver and Flat Fading
Lecture 28 - Channel Estimation across Each Subcarrier in OFDM
Lecture 29 - Example of OFDM - Transmission of Samples with Cyclic Prefix
Lecture 30 - Example of OFDM - FFT at Receiver and Channel Estimation across Subcarriers
Lecture 31 - Comb Type Pilot (CTP) based OFDM Channel Estimation - Block Structure
Lecture 32 - Comb Type Pilot (CTP) based OFDM Channel Estimation - Estimation Scheme
Lecture 33 - Example of Comb Type Pilot (CTP) based OFDM Channel Estimation
Lecture 34 - Frequency Domain Equalization for Inter Symbol Interference Removal in Wireless Systems
Lecture 35 - Example of FDE for Inter Symbol Interference Removal in Wireless Systems - Transmission Structure
Lecture 36 - Example of FDE for ISI Removal in Wireless Systems - Estimation in Frequency/Time Domains
Lecture 37 - Introduction to Sequential Estimation - Application in Wireless Channel Estimation
Lecture 38 - Sequential Estimation of Wireless Channel Coefficient - Estimate and Variance Update Equations
Lecture 39 - Example of Sequential Estimation of Wireless Channel Coefficient

References
Estimation for Wireless Communications: MIMO/OFDM Cellular and Sensor Networks
Instructor: Prof. Aditya K. Jagannatham, Department of Electrical Engineering, IIT Kanpur. This course will comprehensively cover several applications of estimation theory in wireless communications such as channel estimation, equalization, MIMO, OFDM.