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Fading channel – complex baseband equivalent models

Keyfocus: Fading channel models for simulation. Learn how fading channels can be modeled as FIR filters for simplified modulation & detection. Rayleigh/Rician fading.

Introduction

A fading channel is a wireless communication channel in which the quality of the signal fluctuates over time due to changes in the transmission environment. These changes can be caused by different factors such as distance, obstacles, and interference, resulting in attenuation and phase shifting. The signal fluctuations can cause errors or loss of information during transmission.

Fading channels are categorized into slow fading and fast fading depending on the rate of channel variation. Slow fading occurs over long periods, while fast fading happens rapidly over short periods, typically due to multipath interference.

To overcome the negative effects of fading, various techniques are used, including diversity techniques, equalization, and channel coding.

Fading channel in frequency domain

With respect to the frequency domain characteristics, the fading channels can be classified into frequency selective and frequency-flat fading.

A frequency flat fading channel is a wireless communication channel where the attenuation and phase shift of the signal are constant across the entire frequency band. This means that the signal experiences the same amount of fading at all frequencies, and there is no frequency-dependent distortion of the signal.

In contrast, a frequency selective fading channel is a wireless communication channel where the attenuation and phase shift of the signal vary with frequency. This means that the signal experiences different levels of fading at different frequencies, resulting in a frequency-dependent distortion of the signal.

Frequency selective fading can occur due to various factors such as multipath interference and the presence of objects that scatter or absorb certain frequencies more than others. To mitigate the effects of frequency selective fading, various techniques can be used, such as equalization and frequency hopping.

The channel fading can be modeled with different statistics like Rayleigh, Rician, Nakagami fading. The fading channel models, in this section, are utilized for obtaining the simulated performance of various modulations over Rayleigh flat fading and Rician flat fading channels. Modeling of frequency selective fading channel is discussed in this article.

Linear time invariant channel model and FIR filters

The most significant feature of a real world channel is that the channel does not immediately respond to the input. Physically, this indicates some sort of inertia built into the channel/medium, that takes some time to respond. As a consequence, it may introduce distortion effects like inter-symbol interference (ISI) at the channel output. Such effects are best studied with the linear time invariant (LTI) channel model, given in Figure 1.

Linear time invariant channel model for simulating fading channels
Figure 1: Complex baseband equivalent LTI channel model

In this model, the channel response to any input depends only on the channel impulse response(CIR) function of the channel. The CIR is usually defined for finite length \(L\) as \(\mathbf{h}=[h_0,h_1,h_2, \cdots,h_{L-1}]\) where \(h_0\) is the CIR at symbol sampling instant \(0T_{sym}\) and \(h_{L-1}\) is the CIR at symbol sampling instant \((L-1)T_{sym}\). Such a channel can be modeled as a tapped delay line (TDL) filter, otherwise called finite impulse response (FIR) filter. Here, we only consider the CIR at symbol sampling instances. It is well known that the output of such a channel (\(\mathbf{r}\)) is given as the linear convolution of the input symbols (\(\mathbf{s}\)) and the CIR (\(\mathbf{h}\)) at symbol sampling instances. In addition, channel noise in the form of AWGN can also be included the model. Therefore, the resulting vector of from the entire channel model is given as

\[\mathbf{r} = \mathbf{h} \ast \mathbf{s} +\mathbf{n} \quad\quad (1) \]

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Simulation model for detection in flat fading channel

A flat-fading (also called as frequency-non-selective) channel is modeled with a single tap (\(L=1\)) FIR filter with the tap weights drawn from distributions like Rayleigh, Rician or Nakagami distributions. We will assume block fading, which implies that the fading process is approximately constant for a given transmission interval. For block fading, the random tap coefficient \(h=h[0]\) is a complex random variable (not random processes) and for each channel realization, a new set of complex random values are drawn from Rayleigh or Rician or Nakagami fading according to the type of fading desired.

Figure 2: LTI channel viewed as tapped delay line filter

Simulation models for modulation and detection over a fading channel is shown in Figure 2. For a flat fading channel, the output of the channel can be expressed simply as the product of time varying channel response and the input signal. Thus, equation (1) can be simplified (refer this article for derivation) as follows for the flat fading channel.

\[\mathbf{r} = h\mathbf{s} + \mathbf{n} \quad\quad (2) \]

Since the channel and noise are modeled as a complex vectors, the detection of \(\mathbf{s}\) from the received signal is an estimation problem in the complex vector space.

Figure 3: Simulation model for modulation and detection over flat fading channel

Assuming perfect channel knowledge at the receiver and coherent detection, the receiver shown in Figure 3(a) performs matched filtering. The impulse response of the matched filter is matched to the impulse response of the flat-fading channel as \( h^{\ast}\). The output of the matched filter is scaled down by a factor of \(||h||^2\) which is the total-energy contained in the impulse response of the flat-fading channel. The resulting decision vector \(\mathbf{y}\) serves as the sufficient statistic for the estimation of \(\mathbf{s}\) from the received signal \(\mathbf{r}\) (refer equation A.77 in reference [1])

\[\tilde{\mathbf{y}} = \frac{h^{\ast}}{||h||^2} \mathbf{r} = \frac{h^{\ast}}{||h||^2} h\mathbf{s} + \frac{h^{\ast}}{||h||^2} \mathbf{n} = \mathbf{s} + \tilde{\mathbf{w}} \quad\quad (3) \]

Since the absolute value \(|h|\) and the Eucliden norm \(||h||\) are related as \(|h|^2= \left\lVert h\right\rVert = hh^{\ast}\), the model can be simplified further as given in Figure 3(b).

To simulate flat fading, the values for the fading variable \(h\) are drawn from complex normal distribution

\[h= X + jY \quad\quad (4) \]

where, \(X,Y\) are statistically independent real valued normal random variables.

● If \(E[h]=0\), then \(|h|\) is Rayleigh distributed, resulting in a Rayleigh flat fading channel
● If \(E[h] \neq 0\), then \(|h|\) is Rician distributed, resulting in a Rician flat fading channel with the factor \(K=[E[h]]^2/\sigma^2_h\)

References

[1] D. Tse and P. Viswanath, Fundamentals of Wireless Communication, Cambridge University Press, 2005.↗

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Random Variables, CDF and PDF

Random Variable:

In a “coin-flipping” experiment, the outcome is not known prior to the experiment, that is we cannot predict it with certainty (non-deterministic/stochastic). But we know the all possible outcomes – Head or Tail. Assign real numbers to the all possible events (this is called “sample space”), say “0” to “Head” and “1” to “Tail”, and associate a variable “X” that could take these two values. This variable “X” is called a random variable, since it can randomly take any value ‘0’ or ‘1’ before performing the actual experiment.

Obviously, we do not want to wait till the coin-flipping experiment is done. Because the outcome will lose its significance, we want to associate some probability to each of the possible event. In the coin-flipping experiment, all outcomes are equally probable (given that the coin is fair and unbiased). This means that we can say that the probability of getting Head ( our random variable X = 0 ) as well that of getting Tail ( X =1 ) is 0.5 (i.e. 50-50 chance for getting Head/Tail).

This can be written as,

Cumulative Distribution Function:

Mathematically, a complete description of a random variable is given be “Cumulative Distribution Function”- FX(x). Here the bold faced “X” is a random variable and “x” is a dummy variable which is a place holder for all possible outcomes ( “0” and “1” in the above mentioned coin flipping experiment). The Cumulative Distribution Function is defined as,

If we plot the CDF for our coin-flipping experiment, it would look like the one shown in the figure on your right.
The example provided above is of discrete nature, as the values taken by the random variable are discrete (either “0” or “1”) and therefore the random variable is called Discrete Random Variable.

If the values taken by the random variables are of continuous nature (Example: Measurement of temperature), then the random variable is called Continuous Random Variable and the corresponding cumulative distribution function will be smoother without discontinuities.

Probability Distribution function :

Consider an experiment in which the probability of events are as follows. The probabilities of getting the numbers 1,2,3,4 individually are respectively. It will be more convenient for us if we have an equation for this experiment which will give these values based on the events. For example, the equation for this experiment can be given by where . This equation ( equivalently a function) is called probability distribution function.

Probability Density function (PDF) and Probability Mass Function(PMF):

Its more common deal with Probability Density Function (PDF)/Probability Mass Function (PMF) than CDF.

The PDF (defined for Continuous Random Variables) is given by taking the first derivate of CDF.

For discrete random variable that takes on discrete values, is it common to defined Probability Mass Function.

The previous example was simple. The problem becomes slightly complex if we are asked to find the probability of getting a value less than or equal to 3. Now the straight forward approach will be to add the probabilities of getting the values which comes out to be . This can be easily modeled as a probability density function which will be the integral of probability distribution function with limits 1 to 3.

Based on the probability density function or how the PDF graph looks, PDF fall into different categories like binomial distribution, Uniform distribution, Gaussian distribution, Chi-square distribution, Rayleigh distribution, Rician distribution etc. Out of these distributions, you will encounter Gaussian distribution or Gaussian Random variable in digital communication very often.

Mean:

The mean of a random variable is defined as the weighted average of all possible values the random variable can take. Probability of each outcome is used to weight each value when calculating the mean. Mean is also called expectation (E[X])

For continuos random variable X and probability density function fX(x)

For discrete random variable X, the mean is calculated as weighted average of all possible values (xi) weighted with individual probability (pi)

Variance :

Variance measures the spread of a distribution. For a continuous random variable X, the variance is defined as

For discrete case, the variance is defined as

Standard Deviation () is defined as the square root of variance

Properties of Mean and Variance:

For a constant – “c” following properties will hold true for mean

For a constant – “c” following properties will hold true for variance

PDF and CDF define a random variable completely. For example: If two random variables X and Y have the same PDF, then they will have the same CDF and therefore their mean and variance will be same.
On the otherhand, mean and variance describes a random variable only partially. If two random variables X and Y have the same mean and variance, they may or may not have the same PDF or CDF.

Gaussian Distribution :

Gaussian PDF looks like a bell. It is used most widely in communication engineering. For example , all channels are assumed to be Additive White Gaussian Noise channel. What is the reason behind it ? Gaussian noise gives the smallest channel capacity with fixed noise power. This means that it results in the worst channel impairment. So the coding designs done under this most adverse environment will give superior and satisfactory performance in real environments. For more information on “Gaussianity” refer [1]

The PDF of the Gaussian Distribution (also called as Normal Distribution) is completely characterized by its mean () and variance(),

Since PDF is defined as the first derivative of CDF, a reverse engineering tell us that CDF can be obtained by taking an integral of PDF.
Thus to get the CDF of the above given function,

Equations for PDF and CDF for certain distributions are consolidated below

Probability Distribution Probability Density Function(PDF) Cumulative Distribution Function (CDF)
Gaussian/Normal Distribution –

Reference :

[1] S.Pasupathy, “Glories of Gaussianity”, IEEE Communications magazine, Aug 1989 – 1, pp 38.

Topics in this chapter

Random Variables - Simulating Probabilistic Systems
● Introduction
● Plotting the estimated PDF
● Univariate random variables
 □ Uniform random variable
 □ Bernoulli random variable
 □ Binomial random variable
 □ Exponential random variable
 □ Poisson process
 □ Gaussian random variable
 □ Chi-squared random variable
 □ Non-central Chi-Squared random variable
 □ Chi distributed random variable
 □ Rayleigh random variable
 □ Ricean random variable
 □ Nakagami-m distributed random variable
● Central limit theorem - a demonstration
● Generating correlated random variables
 □ Generating two sequences of correlated random variables
 □ Generating multiple sequences of correlated random variables using Cholesky decomposition
● Generating correlated Gaussian sequences
 □ Spectral factorization method
 □ Auto-Regressive (AR) model

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