R23
ECE · 2-1
✓ VERIFIED VS. PUBLISHED SYLLABUS 2026-07-15
Probability Theory and Stochastic Process
JNTUK R23 · ECE · semester 2-1 · syllabus
This JNTUK R23 basic science course is listed for Electronics and Communication Engineering (ECE). It is listed in semester 2-1. It carries 3 credits. The published coverage runs across 5 units, from Probability and Random Variable through Noise Sources and Information Theory.
Verified vs. published syllabus
Checked 15 Jul 2026
SOURCE DOCKET
Published evidence
✓ Verified
Checked 15 Jul 2026
Record 5 published units
Verification scope Subject name, credits, unit-wise syllabus, and course outcomes are sourced directly from JNTUK's own official R23 course structure and syllabus document, not an autonomous college's adaptation.
What you'll be able to do
Perform operations on single and multiple Random variables. Determine the Spectral and temporal characteristics of Random Signals. Characterize LTI systems driven by stationary random process by using ACFs and PSDs. Understand the concepts of Noise and Information theory in Communication systems
5 units, from Probability and Random Variable to Noise Sources and Information Theory. Tick off units as you cover them — your progress stays on this device.
Unit-wise syllabus
0 / 5 COVERED
UNIT 01 — Probability and Random Variable
Probability introduced through sets and relative frequency: experiments and sample spaces, discrete and continuous sample spaces, events · Probability definitions and axioms; joint probability, conditional probability, total probability, Bayes' theorem, independent events · Random variable: definition, conditions for a function to be a random variable; discrete, continuous and mixed random variables; distribution and density functions and their properties · Binomial, Poisson, uniform, Gaussian, exponential, Rayleigh distributions; conditioning event, conditional distribution and conditional density
UNIT 02 — Operations on Single and Multiple Random Variables - Expectations
Expected value of a random variable; function of a random variable; moments about the origin, central moments, variance and skew; Chebyshev's inequality; characteristic function; moment generating function · Transformations of a random variable: monotonic and non-monotonic transformations of continuous random variables; transformation of a discrete random variable · Vector random variables; joint distribution function and its properties; marginal distribution functions; conditional distribution and density (point and interval conditioning); statistical independence · Sum of two and several random variables; central limit theorem (proof not expected); unequal and equal distributions · Joint moments about the origin, joint central moments, joint characteristic functions; jointly Gaussian random variables (two and N random variable cases); transformations of multiple random variables; linear transformations of Gaussian random variables
UNIT 03 — Random Processes - Temporal Characteristics
The random process concept; classification of processes; deterministic and nondeterministic processes; distribution and density functions; stationarity and statistical independence · First-order, second-order, wide-sense, N-order and strict-sense stationarity; time averages and ergodicity; mean-ergodic and correlation-ergodic processes · Autocorrelation and cross-correlation functions and their properties; covariance functions; Gaussian random processes; Poisson random process · Random signal response of linear systems: system response via convolution, mean and mean-squared value of system response, autocorrelation function of response, cross-correlation functions of input and output
UNIT 04 — Random Processes - Spectral Characteristics
The power spectrum: properties; relationship between power spectrum and autocorrelation function · The cross-power density spectrum: properties; relationship between cross-power spectrum and cross-correlation function · Spectral characteristics of system response: power density spectrum of response; cross-power density spectrums of input and output
UNIT 05 — Noise Sources and Information Theory
Resistive/thermal noise source; arbitrary noise sources; effective noise temperature; noise equivalent bandwidth · Average noise figures; average noise figure of cascaded networks; narrow band noise; quadrature representation of narrow band noise and its properties · Entropy, information rate; source coding: Huffman coding, Shannon-Fano coding · Mutual information; channel capacity of a discrete channel; Shannon-Hartley law; trade-off between bandwidth and SNR