Articles | Page 28 of 34

Computing noise power within a specified bandwidth

August 15, 2016 Wireless/SDR

Additive White Gaussian Noise (AWGN)

The performance of a digital communication system is quantified by the probability of bit detection errors in the presence of thermal noise. In the context of wireless communications, the main source of thermal noise is addition of random signals arising from the vibration of atoms in the receiver electronics. You can also watch the video below. The term additive white Gaussian noise (AWGN) originates due to the following reasons: [Additive] The noise is additive, i.e., the received signal is equal to the transmitted signal plus noise. This gives the most widely used equality in communication systems. \begin{equation}\label{eqIntroductionAWGNadditive} r(t) = s(t)

Coverage and throughput in different bands

December 20, 2021 Wireless/SDR

Channel Propagation Effects in mmWave Systems

In a previous article, we have discussed in detail the free space propagation in mmWave systems. We saw that the received power at any distance is independent of the carrier frequency as long as the effective antenna aperture is taken into account. Today, we describe the role of atmospheric effects such as water vapors, oxygen, rain and penetration loss in materials that impact the signal propagation at higher carrier frequencies. Important parameters of small-scale fading in a wireless channel such as delay spread and Doppler spread are also explained in the context of mmWave systems. Atmospheric Effects In realistic channels,

August 14, 2016 Wireless/SDR

Modulation – From Numbers to Signals

The purpose of digital communications is to send digital data across a channel which can be wireless telephone lines coaxial cable optical fiber Ethernet USB chips on a printed circuit board Considering the examples shown in Figure above, clearly neither a bit sequence nor a symbol sequence can be transmitted on their own through these channels — as they are nothing more than a set of numbers. Therefore, a signal waveform is an appropriate tool that can travel down the channel and carry the required information — just like a train running on its track and carrying the load. For

August 13, 2016 DSP

Transforming a Signal

Transforming a discrete-time signal — whether in time or amplitude — is certainly possible, and often in interesting ways. In practice, scaling and time shifting are the two most important signal modifications encountered. Scaling changes the values of dependent variable on amplitude-axis while time shifting affects the values of independent variable on time-axis. Below we describe addition and multiplication of two signals as well as scaling and time shifting a signal in detail. Addition For addition of two discrete-time signals, say $x[n]$ and $y[n]$, add the two signals sample-by-sample: $z[n] = x[n] + y[n]$ for every $n$, e.g., \begin{align*} z[0]

Magnitude and phase of the DFT of a rectangular signal for L = 7, N= 16 and starting sample shifted by one

August 12, 2016 DSP

DFT Examples

For understanding what follows, we need to refer to the Discrete Fourier Transform (DFT) and the effect of time shift in frequency domain first. Here, we discuss a few examples of DFTs of some basic signals that will help not only understand the Fourier transform but will also be useful in comprehending concepts discussed further. A Rectangular Signal A rectangular sequence, both in time and frequency domains, is by far the most important signal encountered in digital signal processing. One of the reasons is that any signal with a finite duration, say $T$ seconds, in time domain (that all practical