Week 04
Probability and Noise Models
I use the saved course file to reconnect sampling, baseband transmission, digital modulation, and error-rate analysis.
What I connect in this unit
A random variable describes possible noise samples and their probabilities, while the Gaussian model represents sums of many independent effects. Power spectral density distributes noise power over frequency, and SNR compares received signal and noise powers.
Concept map
- Random variables
- Gaussian noise
- Power spectral density
- Signal-to-noise ratio
Technical anchor
For two-sided AWGN density N0/2 and receiver noise-equivalent bandwidth Bn, output noise power is approximately N0Bn.
How I review it
Mark ±σ and ±2σ on a Gaussian density, then calculate both linear SNR = Ps/Pn and its decibel value from signal RMS and noise variance.
Connection to the full course
Unit 4 of 12. Unit numbers expand topics from the saved file into a concept sequence, not an attendance-week record.