Gibbs phenomenon — overshoot at Fourier discontinuities
Gibbs phenomenon: why partial Fourier sums overshoot near jumps, that the overshoot does not vanish with more terms, and a visual demonstration.
Signal Processing for Communication Systems
Gibbs phenomenon: why partial Fourier sums overshoot near jumps, that the overshoot does not vanish with more terms, and a visual demonstration.
Fading & Diversity — 6 lessons (same on every page in this path) 1. Rayleigh BER → 2. Clarke’s model → 3. Young’s model (you are here) → 4. SIMO models → 5. Selection combining → 6. MRC Tools: BER vs Eb/N0 · 3GPP TDL · CDL / Doppler Lesson 3 of 6. Another standard … Read more
Fading Lab path: Fading models → Young → Clarke SOS → BPSK BER Rayleigh → MRC Keyfocus: Fading channel models for simulation. Learn how fading channels can be modeled as FIR filters for simplified modulation & detection. Rayleigh/Rician fading. Introduction A fading channel is a wireless communication channel in which the quality of the … Read more
This post contains interactive python code which you can execute in the browser itself. Central Limit Theorem – What is it ? The central limit theorem (CLT) is a fundamental concept in statistics and probability theory that explains how the sum of independent and identically distributed random variables behaves. The theorem states that as … Read more
Keywords: maximum likelihood estimation, statistical method, probability distribution, MLE, models, practical applications, finance, economics, natural sciences. Introduction Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution by finding the set of values that maximize the likelihood function of the observed data. In other words, MLE is … Read more
Detection path: ML estimation → ML decoding → Hard vs soft decision → Hamming codes In one sentence: Maximum-likelihood decoding picks the codeword that maximizes the channel likelihood of the received word — on a BSC that reduces to nearest Hamming neighbor when the crossover probability is below one half. Introduction Maximum likelihood decoding … Read more
Hard decision keeps 0/1; soft decision keeps reliability (LLRs). Why soft decoding gains ~2 dB and how it ties to ML decoding.
Introduction The Hamming codes described in previous articles are suitable for random bit errors. However, if the communication medium is prone to burst errors (contiguous blocks of bits being corrupted), Hamming codes are no longer efficient. For these scenarios, we use a class of Error Correcting Codes called Reed-Solomon (RS) Codes. Applications of RS … Read more
How Hamming codes work: parity-bit placement, syndrome decoding, single-error correction, and why (7,4) Hamming is the classic teaching example.
Information theory path: BPSK AWGN → Shannon capacity → Shannon power-efficiency limit → Capacity calculator In one sentence: Shannon capacity is the highest rate (bits per channel use, or bits per second for a continuous-time bandlimited AWGN channel) at which information can be sent with arbitrarily small error probability. Shannon theorem dictates the maximum … Read more