Channel Modelling Archives - GaussianWaves

Euclidean and Hamming distances

Mathuranathan — Wed, 23 Sep 2020 08:34:48 +0000

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

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Rician flat-fading channel – simulation

Mathuranathan — Sat, 15 Aug 2020 13:54:24 +0000

In wireless environments, transmitted signal may be subjected to multiple scatterings before arriving at the receiver. This gives rise to random fluctuations in the received signal and this phenomenon is called fading. The scattered version of the signal is designated as non line of sight (NLOS) component. If the number of NLOS components are sufficiently ... Read more

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BPSK bit error rate simulation in Python & Matlab

Mathuranathan — Thu, 23 Jul 2020 07:52:10 +0000

Key focus: Simulate bit error rate performance of Binary Phase Shift Keying (BPSK) modulation over AWGN channel using complex baseband equivalent model in Python & Matlab. Why complex baseband equivalent model The passband model and equivalent baseband model are fundamental models for simulating a communication system. In the passband model, also called as waveform simulation ... Read more

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Hidden Markov Models (HMM) – Simplified !!!

Mathuranathan — Tue, 17 Mar 2020 05:08:35 +0000

Markov chains are useful in computing the probability of events that are observable. However, in many real world applications, the events that we are interested in are usually hidden, that is we don’t observe them directly. These hidden events need to be inferred. For example, given a sentence in a natural language we only observe the ... Read more

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Maximum Ratio Combining (MRC) architecture simulation

Mathuranathan — Fri, 17 Jan 2020 06:13:44 +0000

In the previous post on Single Input Multiple Output (SIMO) models for receive diversity, various receiver diversity techniques were outlined. One of them is maximum ratio combining, the focus of the topic here. Channel model Assuming flat slow fading channel, the received signal model is given by where, is the channel impulse response, is the ... Read more

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Selection Combining – architecture simulation

Mathuranathan — Tue, 10 Dec 2019 09:50:23 +0000

In the previous post on Single Input Multiple Output (SIMO) models for receive diversity, various receiver diversity techniques were outlined. One of them is selection combining, the focus of the topic here. Channel model Assuming flat slow fading channel, the received signal model is given by where, is the channel impulse response, is the received ... Read more

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Receive diversity schemes – channel models

Mathuranathan — Fri, 06 Dec 2019 06:06:42 +0000

SIMO channel configuration is characterized by 1 transmit antenna and multiple receiver antennas (Figure 1). SIMO configuration is used to provide receive diversity, where the same information is received across independent fading channels to combat fading. When multiple copies of the same data are received across independently fading channels, the amount of fade suffered by each ... Read more

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Generate color noise using Auto-Regressive (AR) model

Mathuranathan — Wed, 04 Dec 2019 07:16:19 +0000

Key focus: Learn how to generate color noise using auto regressive (AR) model. Apply Yule Walker equations for generating power law noises: pink noise, Brownian noise. Auto-Regressive (AR) model An uncorrelated Gaussian random sequence can be transformed into a correlated Gaussian random sequence using an AR time-series model. If a time series random sequence is ... Read more

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Generating colored noise with Jakes PSD: Spectral factorization

Mathuranathan — Thu, 17 Oct 2019 06:16:55 +0000

The aim of this article is to demonstrate the application of spectral factorization method in generating colored noise having Jakes power spectral density. Before continuing, I urge the reader to go through this post: Introduction to generating correlated Gaussian sequences. In spectral factorization method, a filter is designed using the desired frequency domain characteristics (like ... Read more

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Generate correlated Gaussian sequence (colored noise)

Mathuranathan — Tue, 15 Oct 2019 06:43:03 +0000

Key focus: Colored noise sequence (a.k.a correlated Gaussian sequence), is a non-white random sequence, with non-constant power spectral density across frequencies. Introduction Speaking of Gaussian random sequences such as Gaussian noise, we generally think that the power spectral density (PSD) of such Gaussian sequences is flat.We should understand that the PSD of a Gausssian sequence ... Read more

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