Signal Processing for Communication Systems
Key focus: AR, MA & ARMA models express the nature of transfer function of LTI system. Understand the basic idea behind those models & know their frequency responses. How do you describe a complex, random signal—like the sound of a human voice or the fluctuating power of a fading channel—using only a few numbers? The … Read more
Mathematical details of convolution, its relationship to polynomial multiplication and the application of Toeplitz matrices in computing linear convolution are discussed in the previous article. A short survey of different techniques to compute discrete linear convolution (with Matlab code) is given here. Definition Given an LTI (Linear Time Invariant) system with impulse response \(h[n]\) and … Read more
Convolution operation is ubiquitous in signal processing applications. The mathematics of convolution is strongly rooted in operation on polynomials. The intent of this text is to enhance the understanding on mathematical details of convolution. Polynomial functions: Polynomial functions are expressions consisting of sum of terms, where each term includes one or more variables raised to … Read more
Calculating the energy and power of a signal was discussed in one of the previous posts. Here, we will verify the calculation of signal power using Discrete Fourier Transform (DFT) in Matlab. Check here to know more on the concept of power and energy. The total power of a signal can be computed using the … Read more
Energy Ex = Σ|x|² vs power Px as a long-window average of |x|²; energy signals vs power signals; physical scaling by load Z; Python examples and links to Matlab verification.
Researchers, working on Ultra-Parallel Visible Light Communications (UP-VLC) project, have demonstrated 3.5Gpbs free space data transmission via three separate micro-LEDs that emit red, blue and green colours. In effect, combining the three colours (that makes up for white colour), researchers say that achieving data rates over 10Gbps is feasible. The technology enabler here is called … Read more
Digital Modulations Lab path:BPSK → QPSK → M-PSK simulation → QPSK symbol timing → π/2-BPSK (5G NR)Tools: BER vs Eb/N0 · EVM → SNR · OFDM CP overhead The goal of timing recovery is to estimate and correct the sampling instants and phase at the receiver, such that it allows the receiver to decode the … Read more
Fourier series fundamentals: representing periodic signals with harmonics, computing coefficients, and links to Gibbs phenomenon and Parseval.
Signals path 1. Analog & discrete → 2. Baseband sampling → 3. Bandpass sampling → 4. Power & energy Tools: Nyquist sampling demo · Bandpass sampling demo Interactive lab: open the bandpass sampling demo to drag Fs through valid and invalid zones for a chosen [fL, fH], read the alias map (e.g. 90–100 Hz @ 56 Hz), … Read more
Nyquist–Shannon sampling for baseband signals: sample faster than twice the highest frequency, or aliases fold into band.