Signal Processing for Communication Systems
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
Key focus: Clearly understand the terms: power and energy of a signal, their mathematical definition, physical significance and computation in signal processing context. Energy of a signal: Defining the term “size”: In signal processing, a signal is viewed as a function of time. The term “size of a signal” is used to represent “strength of … Read more
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
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 transmitted symbols reliably. What is Symbol timing Recovery : When transmitting data across a communication system, three things are important: frequency of transmission, phase information and the symbol … Read more
Understand Fourier Series, Fourier Cosine Series, Fourier Sine Series, partial sums, even odd symmetry. Hands-on simulation with Matlab code given. Fourier analysis and Fourier Synthesis: Fourier analysis – a term named after the French mathematician Joseph Fourier, is the process of breaking down a complex function and expressing it as a combination of simpler functions. … Read more
Prerequisite: Sampling theorem – baseband sampling Intermediate Sampling or Under-Sampling A signal is a bandpass signal if we can fit all its frequency content inside a bandwidth . Bandwidth is simply the difference between the lowest and the highest frequency present in the signal. “In order for a faithful reproduction and reconstruction of a bandpass … Read more
For Matlab demo of sampling see here. “Nyquist-Shannon Sampling Theorem” is the fundamental base over which all the digital processing techniques are built. Processing a signal in digital domain gives several advantages (like immunity to temperature drift, accuracy, predictability, ease of design, ease of implementation etc..,) over analog domain processing. Analog to Digital conversion: In … Read more
Modified Duobinary Signaling is an extension of duobinary signaling. It has the advantage of zero PSD at low frequencies (especially at DC ) that is suitable for channels with poor DC response. It correlates two symbols that are 2T time instants apart, whereas in duobinary signaling, symbols that are 1T apart are correlated. The general … Read more
Spectral leakage due to FFT is caused by: mismatch between desired tone and chosen frequency resolution, time limiting an observation. Understand the concept using hands-on examples. Limits of frequency domain studies Frequency Transform is used to study a signal’s frequency domain characteristics. When using FFT to study the frequency domain characteristics of a signal, there … Read more
The moving average filter is a simple Low Pass FIR (Finite Impulse Response) filter commonly used for smoothing an array of sampled data/signal. It takes samples of input at a time and takes the average of those -samples and produces a single output point. It is a very simple LPF (Low Pass Filter) structure that … Read more