Normalized power gain of dipole antennas

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Antenna models — reading order Maxwell’s equations Retarded potentials Near-field / far-field boundary Far-field retarded potentials Array pattern multiplication Dipole power gain patterns (this article) Introduction You have almost certainly seen the short-dipole pattern: nulls along the wire, maximum broadside. We will derive the normalised power patterns for the Hertzian dipole, the half-wave dipole, and … Read more

Bayes’ theorem

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Key focus: Bayes’ theorem is a method for revising the prior probability for specific event, taking into account the evidence available about the event. Introduction In statistics, the process of drawing conclusions from data subject to random variations – is called “statistical inference”. Usually, in any random experiment, the observations are recorded and conclusions have … Read more

Line code – demonstration in Matlab and Python

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Line code is the signaling scheme used to represent data on a communication line. There are several possible mapping schemes available for this purpose. Lets understand and demonstrate line code and PSD (power spectral density) in Matlab & Python. Line codes – requirements When transmitting binary data over long distances encoding the binary data using … Read more

Euclidean and Hamming distances

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Key focus: Euclidean & Hamming distances are used to measure similarity or dissimilarity between two sequences. Used in Soft & Hard decision decoding. Distance is a measure that indicates either similarity or dissimilarity between two words. Given a pair of words a=(a0,a1, … ,an-1) and b=(b0,b1,…,bn-1) , there are variety of ways one can characterize … Read more

Rician flat-fading simulation — line-of-sight plus scattered power

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Fading & Diversity — 6 lessons (same on every page in this path) 1. Rayleigh BER → 2. Clarke’s model (you are here) → 3. Young’s model → 4. SIMO models → 5. Selection combining → 6. MRC Tools: BER vs Eb/N0 · 3GPP TDL · CDL / Doppler Optional branch from Fading & Diversity. … Read more

Statistical measures for stochastic signals

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Key focus: Discuss statistical measures for stochastic signals : mean, variance, skewness, kurtosis, histogram, scatterplot, cross-correlation and auto-correlation. Deterministic and stochastic signals A deterministic signal is exactly predictable for the given time span of interest. It could be expressed using analytic form (example: x(t) = sin (2 π fc t) ). Many of the signals … Read more