Course Name: Bayesian/ MMSE Estimation for Wireless Communications -MIMO/ OFDM

Course abstract

Fundamentals of Statistical Signal Processing • Author: Steven M. Kay 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.


Course Instructor

Media Object

Prof. Aditya K. Jagannatham

Prof.Aditya.K.Jagannatham (http://home.iitk.ac.in/~adityaj/index.html) received his Bachelors degree from the Indian Institute of Technology, Bombay and M.S. and Ph.D. degrees from the University of California, San Diego, U.S.A.. From April 07 to May 09 he was employed as a senior wireless systems engineer at Qualcomm Inc., San Diego, California, where he worked on developing 3G UMTS/WCDMA/HSDPA mobile chipsets as part of the Qualcomm CDMA technologies division. His research interests are in the area of next-generation wireless communications and networking, sensor and ad-hoc networks, digital video processing for wireless systems, wireless 3G/4G cellular standards and CDMA/OFDM/MIMO wireless technologies. He has contributed to the 802.11n high throughput wireless LAN standard and has published extensively in leading international journals and conferences. He was awarded the CAL(IT)2 fellowship for pursuing graduate studies at the University of California San Diego and in 2009 he received the Upendra Patel Achievement Award for his efforts towards developing HSDPA/HSUPA/HSPA+ WCDMA technologies at Qualcomm. Since 2009 he has been a faculty member in the Electrical Engineering department at IIT Kanpur, where he is currently an Associate Professor, and is also associated with the BSNL-IITK Telecom Center of Excellence (BITCOE). At IIT Kanpur he has been awarded the P.K. Kelkar Young Faculty Research Fellowship (June 2012 to May 2015) for excellence in research. His popular video lectures for the NPTEL (National Programme on Technology Enhanced Learning) course on Advanced 3G and 4G Wireless Mobile Communications can found at the following YouTube link ( NPTEL 3G/4G ).
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Teaching Assistant(s)

ANUPAMA RAJORIYA

B.Tech in Electronics and Communication, NIT Bhopal
Pursuing M.tech in Signal Processing, Communication and Networks

IIT Kanpur

APOORVA CHAWLA

B.Tech in Electronics and Communication, Inderprastha Engineering College, Ghaziabad
Pursuing Ph.D. in Electrical Engineering (Signal Processing, Communications and Networks)

IIT Kanpur

 Course Duration : Jul-Sep 2016

  View Course

 Enrollment : 23-May-2016 to 18-Jul-2016

 Exam registration : 02-Aug-2016 to 19-Aug-2016

 Exam Date : 18-Sep-2016

Enrolled

1117

Registered

62

Certificate Eligible

47

Certified Category Count

Gold

9

Silver

0

Elite

26

Successfully completed

12

Participation

4

Success

Elite

Gold





Legend

>=90 - Elite + Gold
60-89 - Elite
40-59 - Successfully Completed
<40 - No Certificate

Final Score Calculation Logic

  • Assignment Score = Average of best 6 out of 8 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Bayesian/ MMSE Estimation for Wireless Communications -MIMO/ OFDM - Toppers list

BALVINDER 96%

IIT KANPUR

SHASHANK SHEKHAR 96%

NIT SILCHAR

THAVALAPILL SMITH SUDHAKARAN 96%

DHARMSINH DESAI UNIVERSITY

Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.