Detection for random vectors and processes
Introduction: A layered view of digital communication
Entropy and asymptotic equipartition property
Markov sources and Lempel-Ziv universal codes
High rate quantizers and waveform encoding
Discrete-time fourier transforms and sampling theorem
Nyquist theory, pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and frequency translation
Jointly Gaussian random vectors and processes and white Gaussian noise (WGN)
Linear functionals and filtering of random processes
Detection for random vectors and processes
Baseband detection and complex Gaussian processes
Doppler spread, time spread, coherence time, and coherence frequency
Discrete-time baseband models for wireless channels
Detection for flat rayleigh fading and incoherent channels, and rake receivers
Case study — code division multiple access (CDMA)