This is an advanced course on coding theory, which is a sequel to EE621. We continue with more in-depth treatment of LDPC and turbo codes followed by some recent developments in coding theory including rateless codes and dirty paper coding. Topics covered are: codes on graphs, message-passing, irregular LDPC code ensembles, density evolution, concentration theorem, stability condition, thresholds, capacity-achieving sequences for BEC, EXIT chart, EXIT function and area theorem, multi-edge type LDPC codes, LDGM, rateless, LT, and Raptor codes, efficient encoding for LDPC codes, Code design in Euclidean space, coding and shaping gains, lattice strategies for coding, dirty paper coding.
This course is the advanced course dealing with methods for correcting and detecting errors in data and covers finite field theory, cyclic code, BCH code, Reed-Solomon code, convolutional code, trellis-coded modulation, turbo code, LDPC code, space-time code, and adaptive coding. (Prerequisite: EE522, EE528)
The purpose of this course is to provide the fundamental background behind detection and estimation theories based on likelihood functions as well as on Bayesian principles. Topics to be covered are decision theory, hypothesis testing, performance analysis, detection and estimation from waveform observation, linear and nonlinear parameter estimations. (Prerequisite: EE528 recommended)
This course covers the core concept of information theory, including the fundamental source and channel coding theorems, coding theorem for Gaussian channel, rate-distortion theorem, vector quantization, multiple user channel, and multiple access channels.
(Prerequisite: CC511, EE528)
This course aims to learn fundamental technologies for signal modeling and estimation and covers deterministic and random signal modeling, lattice filter realization, parameter, and signal estimation, Wiener and Kalman filter design, parametric and nonparametric spectrum estimation, and adaptive filtering. (Prerequisite: EE432, EE528)