Tag Archives: WiMAX

Building an LTE Channel Simulator

As discussed previously building an LTE fading simulator is a three step procedure.

1. Generate a temporally correlated Rayleigh fading sequence. This step would be repeated for each channel tap and transmit receive antenna combination e.g. for a 2×2 MIMO system and EPA channel model with 7 taps the number of fading sequences to be generated is 4×7=28. The temporal correlation of these fading sequences is controlled by the Doppler frequency. A higher Doppler frequency results in faster channel variations and vice versa.

2. Introduce spatial correlation between the parallel paths e.g. for a 2×2 MIMO system a 4×4 antenna corelation matrix would be used to introduce spatial correlation between the 4 parallel paths h11, h12, h21 and h22. This can be thought of as a weighted average. A channel coefficient between Tx-1 and Rx-1 would be calculated as h11=w1*h11+w2*h12+w3*h21+w4*h22. In this case the weight ‘w1’ would have a value of 1 whereas the other weights would have a value less than 1. If w2=w3=w4=0 there is no correlation between h11 and other channel coefficients.

2-Transmit 2-Receive Channel Model
2-Transmit 2-Receive Channel Model

3. Once the sequences with the desired temporal and spatial correlation have been generated their mean power would have to be adjusted according to the power delay profile of the selected channel model (EPA, EVA or ETU). The number of channel coefficients increases exponentially with the number of transmit and receiver antennas e.g. for a 4×4 MIMO system each filter tap would have to be calculated after performing a weighted average of 16 different channel taps. And this step would have to be repeated for each filter tap resulting in a total of 16×7=112 fading sequences.

We have already discussed step 1 in detail. We would now elaborate on step 2 i.e. generation of spatially correlated fading sequences.

 

 

Can We Do Without a Cyclic Prefix

Have you ever thought that Cyclic Prefix in OFDM is just a gimmick and we could do equally well by using a guard period i.e. a period of no transmission between two OFDM symbols. Well, one way to find out if this is true is by running a bit error rate simulation with and without a cyclic prefix (only a vacant guard period). We use the 64-QAM OFDM simulation that we developed previously. The channel is modeled as 7-tap FIR filter with each tap having a Rayleigh distribution.

BER with and without Cyclic Prefix
BER with and without Cyclic Prefix

We simulate the case of a vacant guard period by inserting zeros in the time slot dedicated for the CP (32 samples). It is observed that there is a vast difference in the bit error rate (BER) for the two cases. In fact in the case of no CP the BER hits an error floor at around 20 dB. Increasing the signal to noise ratio does not improve the BER performance any further.

Now to answer the question “why does the CP work” we have to revisit the concept of circular convolution from our DSP course. It is well known that performing circular convolution of two sequences in the time domain is equivalent to multiplication of their DFT’s in the frequency domain. So if a wireless channel performed circular convolution we could do simple division to recover the signal after the FFT operation in the receiver.

Y(k)=X(k)*H(k) Effect of Wireless Channel

X(k)=Y(k)/H(k) Recovery of the Signal

But the wireless channel does not perform circular convolution, it performs linear convolution. So the trick is to make this linear convolution appear as circular convolution by appending a cyclic prefix. The result is that equalization can be performed at the receiver by simple division.

For a more elaborate discussion on this you may visit CP-1 or for a mathematical description you may visit CP-2.

Note: In a actual system there would be AWGN noise added to the received signal as well. Giving us the following relationships.

Y(k)=X(k)*H(k)+W(k) Effect of Wireless Channel

X(k)’=Y(k)/H(k)=X(k)+W(k)/H(k) Recovery of the Signal

LTE Fading Simulator

As discussed previously an LTE channel can be modeled as an FIR filter. The filter taps are described by the EPA, EVA and ETU channel models.

If x(k) is the original signal then the signal at the output of the FIR filter y(k) is given as:

y(k)=x(k)*c(0)+x(k-1)*c(1)+…..+x(k-L+2)*c(L-2)+x(k-L+1)*c(L-1)

Channel as FIR Filter
Channel as FIR Filter

Since the wireless channel is time varying the channel taps c(0) c(1)…..c(L-1) are also time varying with either Rayleigh or Rician distribution. It is quite easy to generate Rayleigh random variables with the desired power and distribution, however, when these Rayleigh random variables are required to have temporal correlation the process becomes a bit complicated. Temporal correlation of these variables depends upon the Doppler frequency which is turn depends upon the speed of the mobile device. The Doppler frequency is defined as:

fd=v*cos(theta)/lambda

where

fd is the Doppler Frequency in Hz
v is the receiver velocity in m/sec
lambda is the wavelength in m
and theta is the angle between the direction of arrival of the signal and the direction of motion

A simple method for generating Rayleigh random variables with the desired temporal correlation was devised by Smith [1]. His method was based on Clark and Gans fading model and has been widely used in simulation of wireless communication systems.

The method for generating the Rayleigh fading envelope with the desired temporal correlation is given below (modified from Theodore S. Rappaport Text).

1. Define N the number of Gaussian RVs to be generated, fm the Doppler frequency in Hz, fs the sampling frequency in Hz, df the frequency spacing which is calculated as df=(2*fm)/(N-1) and M total number of samples in frequency domain which is calculated as M=(fs/df).

2. Generate two sequences of N/2 complex Gaussian random variables. These correspond to the frequency bins up to fm. Take the complex conjugate of these sequences to generate the N/2 complex Gaussian random variables for the negative frequency bins up to -fm.

3. Multiply the above complex Gaussian sequences g1 and g2 with square root of the Doppler Spectrum S generated from -fm to fm. Calculate the spectrum at -fm and +fm by using linear extrapolation.

4. Extend the above generated spectra from -fs/2 to +fs/2 by stuffing zeros from -fs/2 to -fm and fm to fs/2. Take the IFFT of the resulting spectra X and Y resulting in time domain signals x and y.

5. Add the absolute values of the resulting signals x and y in quadrature. Take the absolute value of this complex signal. This is the desired Rayleigh distributed envelope with the required temporal correlation.

The Matlab code for generating Rayleigh random sequence with a Doppler frequency of fm Hz is given below.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RAYLEIGH FADING SIMULATOR BASED UPON SMITH'S METHOD
% N is the number of paths
% M is the total number of points in the frequency domain
% fm is the Doppler frequency in Hz
% fs is the sampling frequency in Hz
% df is the step size in the frequency domain
% Copyright RAYmaps (www.raymaps.com)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all;
close all;                          
 
N=10;
fm=300;
df=(2*fm)/(N-1);
fs=7.68e6;
M=round(fs/df);
T=1/df;
Ts=1/fs;                                
 
% Generate two sequences of N complex Gaussian random variables 
g=randn(1,N/2)+j*randn(1,N/2);
gc=conj(g);
g1=[fliplr(gc), g];                                  
 
g=randn(1,N/2)+j*randn(1,N/2);
gc=conj(g);
g2=[fliplr(gc), g];                 
 
% Generate Doppler Spectrum S
f=-fm:df:fm;
S=1.5./(pi*fm*sqrt(1-(f/fm).^2));
S(1)=2*S(2)-S(3);
S(end)=2*S(end-1)-S(end-2);   
 
% Multiply the complex sequences with the Doppler Spectrum S, take IFFT
X=g1.*sqrt(S);
X=[zeros(1,round((M-N)/2)), X, zeros(1,round((M-N)/2))];
x=abs(ifft(X,M));                             
 
Y=g2.*sqrt(S);
Y=[zeros(1,round((M-N)/2)), Y, zeros(1,round((M-N)/2))];
y=abs(ifft(Y,M));                             
 
% Find the resulting Rayleigh faded envelope
z=x+j*y;
r=abs(z);                       
 
t=0:Ts:T-Ts;
plot(t,10*log10(r/mean(r)),'r')
xlabel('Time(sec)')
ylabel('Envelope (dB)')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

The above code generates a Rayleigh random sequence with samples spaced at 0.1302 usec. This corresponds to a sampling frequency of 7.68 MHz which is the standard sampling frequency for a bandwidth of 5MHz. Similarly, the sampling rate for 10 MHz and 20 MHz is 15.36 MHz and 30.72 MHz respectively.  The Doppler frequency can also be changed according to the scenario. LTE standard defines 3 channel models EPA, EVA and ETU with Doppler frequencies of 5 Hz, 70 Hz and 300 Hz respectively. These are also known as Low Doppler, Medium Doppler and High Doppler respectively.

The above code generated Rayleigh sequences of varying lengths for the three cases. But in all the cases it is in excess of 10 msec and can be used as the fading sequence for an LTE frame. Just to recall an LTE frame is of 10 msec duration with 20 time slots of 0.5 msec each. If each slot contains 7 OFDM symbols the total length of a fading sequence is 140 symbols.

Rayleigh Fading 5Hz, 70Hz, 300Hz

Envelope and Phase Distribution

It is seen that the fluctuation in the channel increases with Doppler frequency. The channel is almost static for a Doppler frequency of 5 Hz and varies quite rapidly for a Doppler frequency of 300 Hz. It is also shown above that the envelope of z is Rayleigh distributed and phase of z is Uniformly distributed. However, the range of phase is from 0 to pi/2. This needs to be further investigated. The level crossing rate and average fade duration can also be measured.

This is the process for generating one Rayleigh distributed channel tap. This step would have to be repeated for the number of taps in the channel model which could be 7 or 9 for the LTE channel models. A Ricean distributed channel tap can be generated in a similar fashion. MIMO channel taps can also be generated using the above described method, however, we would need to understand the concept of antenna correlation before we do that.

Level Crossing Rate and Average Fade Duration

Level crossing rate (LCR) is defined as number of times per second the signal envelope crosses a given threshold. This could be either in the positive direction or negative direction. Average fade duration (AFD) is the average duration that the signal envelope remains below a given threshold once it crosses that threshold. Simply it is the average duration of a fading event. The LCR and AFD are interconnected and the product of these two quantities is a constant. The program given below calculates the LCR and AFD of the above generated envelope r.

 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PROGRAM TO CALCULATE THE LEVEL CROSSING RATE AND AVERAGE FADE DURATION
% Rth: Level to calculate the LCR and AFD
% Rrms: RMS level of the signal r
% rho: Ratio of defined threshold and RMS level
% Copyright RAYmaps (www.raymaps.com)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Rth=0.30;
Rrms=sqrt(mean(r.^2));
rho=Rth/Rrms;

count1=0;
count2=0;

r=r/mean(r);
for n=1:length(r)-1

     if r(n)<Rth && r(n+1)>Rth
        count1=count1+1;
     end
    if r(n) < Rth
        count2=count2+1;
    end

end
LCR=count1/(T)
AFD=((count2*Ts)/T)/LCR

LCR_num=sqrt(2*pi)*fm*rho*exp(-(rho^2))
AFD_num=(exp(rho^2)-1)/(rho*fm*sqrt(2*pi))
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

The program calculates both the simulated and theoretical values of LCR and AFD e.g. for a threshold level of 0.3 (-5.22 dB) the LCR and AFD values calculated for fm=70 Hz and N=32 are:

LCR simulation = 45.16

LCR theoretical = 43.41

AFD simulation = 0.0015 sec

AFD theoretical = 0.0016 sec

It can be seen that the theoretical and simulation results match quite well. This gives us confidence that are generated envelope has the desired statistical characteristics.

Note:

1. According to Wireless Communications Principles and Practice by Ted Rappaport “Perform an IFFT on the resulting frequency domain signals from the in-phase and quadrature arms to get two N-length time series, and add the squares of each signal point in time to create an N-point time series. Note that each quadrature arm should be a real signal after IFFT”. Now this point about the signal being real after IFFT is not always satisfied by the above program. The condition can be satisfied by playing around with the value of N a bit.

2. Also, we take the absolute value of both the time series after IFFT operation to make sure that we get a real valued sequence. However, taking the absolute value of both the in-phase and quadrature terms  makes z fall in the first quadrant and the phase of z to vary from 0 to pi/2. A better approach might be to use the ‘real’ function instead of ‘abs’ function so that the phase can vary from 0 to 2pi.

3. A computationally efficient method of generating Rayleigh fading sequence is given here.

[1] John I. Smith, “A Computer Generated Multipath Fading Simulation for Mobile Radio”, IEEE Transactions on Vehicular Technology, vol VT-24, No. 3, August 1975.

LTE Multipath Channel Models

When a wireless signal travels from a transmitter to a receiver it follows multiple paths. The signal may travel directly following the line of sight between the transmitter and receiver, it may bounce off the ground and reach the receiver or it may be reflected by multiple buildings on the way to the receiver. When these copies of the same signal arrive at the receiver they are delayed and attenuated based upon the path length that they have followed and various other factors.

A well known technique to model such a wireless channel is to model it as an FIR (Finite Impulse Response) filter. The wireless channel thus performs the convolution operation on the transmitted signal. The multipath profile of three well known LTE channel models is shown below.

LTE Channel Models
LTE Channel Models

The channel profile quantifies the delays and relative powers of the multipath components. It can be observed that the EPA model has 7 multipath components whereas the EVA and ETU models have 9 multipath components each. However there is a small caveat here. The multipath components described in the above table are not uniformly spaced in the time domain. So if an FIR filter has to perform convolution operation on a signal uniformly sampled at 100 MHz (Ts=10 nsec) the number of filter taps would be much larger. To be exact the FIR filters corresponding to the above channel models would have 42, 252 and 501 filter taps respectively. Most of these taps would have no power so the FIR filter can be efficiently implemented in hardware.

Also, if the channel is time-varying as most wireless channels are, each filter tap can be modeled to have a Rayleigh or Ricean distribution with a mean value described in the table above. Lastly, the variation in the value of a channel tap from one sample to the next depends upon the Doppler frequency which in turn depends upon the speed of the mobile unit. Higher the velocity of a mobile unit higher would be the Doppler frequency and greater would be the variations in the channel. The Doppler frequency is defined as:

fd=v*cos(theta)/lambda

where

‘fd’ is the Doppler Frequency
‘v’ is the receiver velocity
‘lambda’ is the wavelength
and ‘theta’ is the angle between the direction of arrival of the signal and the direction of motion

The exact method of generating Rayleigh distributed channel co-efficients with the desired temporal correlation requires some more explanation and would be the subject of a future post.

Note:
1. The above multipath channel models have a maximum delay of 410 nsec, 2510 nsec and 5000 nsec which is well within the range of a long cyclic prefix of length 16.67 usec (16670 nsec). So the Intersymbol Interference (ISI) would not adversely effect the system performance.
2. If you are doing simulation of a system that operates on individual symbols then temporal correlation between channel co-efficients is not that important. But if the system operates on blocks of symbols or bits (as an interleaver or convolutional encoder does) then temporal correlation plays an important part in determining the system performance.

Antenna Radiation Pattern and Antenna Tilt

An introductory text in Communication Theory would tell you that antennas radiate uniformly in all directions and the power received at a given distance ‘d’ is proportional to 1/(d)^2. Such an antenna is called an isotropic radiator. However, real world antennas are not isotropic radiators. They transmit energy in only those directions where it is needed. The Gain of a antenna is defined as the ratio of the power transmitted (or received) in a given direction to the power transmitted in that direction by an isotropic source and is expressed in dBi.

Although antenna Gain is a three dimensional quantity, the Gain is usually given along horizontal and vertical planes passing through the center of the antenna. The Horizontal and Vertical Gain patterns for a popular base station antenna Kathrein 742215 are shown in the figure below.

Kathrein 742215 Gain Pattern
Kathrein 742215 Gain Pattern

The actual Gain is given with respect to the maximum Gain which is a function of the frequency e.g. in the 1710-1880 MHz band the maximum Gain has a value of 17.7dBi. Another important parameter is the Half Power Beam Width (HPBW) which has values of 68 degree and 7.1 degree in the horizontal and vertical planes respectively. HPBW is defined as the angle in degrees within which the power level is equal to or above the -3 dB level of the maximum.

Also shown in the above figure are approximate Horizontal Gain patterns for two antennas that have been rotated at 120 degrees and 240 degrees. Together these three antennas cover the region defined as a cell. There would obviously be lesser coverage in areas around the intersection of two beams.

A somewhat more interesting pattern is in the vertical direction where the HPBW is only 7.1 degrees. Thus it is very important to direct this beam in the right direction. A perfectly horizontal beam would result in a large cell radius but may also result in weak signal areas around the base station. A solution to this problem is to give a small tilt to the antenna in the downward direction, usually 5-10 degrees. This would reduce the cell radius but allow for a more uniform distribution of energy within the cell. In reality the signal from the main beam and side lobes (one significant side lobe around -15 dB) would bounce off the ground and buildings around the cell site and spread the signal around the cell.

Antenna Tilt of 10 Degrees
Antenna Tilt of 10 Degrees

The above figure gives a 2D view of signal propagation from an elevated antenna with a downward tilt of 10 degrees in an urban environment.

Base Station Antenna Tilt and Path Loss

Path loss is basically the difference in transmit and receive powers of a wireless communication link. In a Free Space Line of Sight (LOS) channel the path loss is defined as:

L=20*log10(4*pi*d/lambda)

where ‘d’ is the transmit receive separation and ‘lambda’ is the wavelength. It is also possible to include the antenna gains in the link budget calculation to find the end to end path loss (cable and connector losses may also be factored in). Antenna gains are usually defined along a horizontal plane and vertical plane passing through the center of the antenna. The antenna gain can then be calculated at any angle in 3D using the gains in these two planes.

Although 3D antenna gains are quite complex quantities simplified models are usually used in simulations e.g. a popular antenna Kathrein 742215 has the following antenna gain models [1] along the horizontal and vertical planes:

Gh(phi)=-min(12*(phi/HPBWh)^2, FBRh)+Gm

Gv(theta)=max(-12*((theta-theta_tilt)/HPBWv)^2, SLLv)

where

Gm=18 dBi
HPBWh=65 degrees
HPBWv=6.2 degrees
SLLv=-18 dB

We are particularly interested in the gain in the vertical plane and the effect of base station antenna tilt on the path loss. We assume that the mobile antenna station has uniform gain in all directions. The path loss can be then calculated as:

L=20*log10(4*pi*d/lambda)+Gv(theta)+Gh(phi)

where we have assumed that Gh(phi)=0 for all phi (this is a reasonable simplification since changing the distance along the line of sight would not change Gh(phi) ). Using the above expression the path loss in free space is calculated for a frequency of 1805 MHz, base station antenna height of 30 m and an antenna tilt of 5 degrees.

Effect of Antenna Tilt on Path Loss
Effect of Antenna Tilt on Path Loss

It is observed that there is a sudden decrease in path loss at distances where the antenna main beam is directed. If the antenna tilt is increased this behavior would be observed at smaller distances. Since we have used a side lobe level that is fixed at -18 dB we see a rapid change in behavior at around 100 m. If a more realistic antenna model is used we would see a gradual decrease in path loss at this critical distance.

[1] Fredrik Gunnarsson, Martin N Johansson, Anders Furuskär, Magnus Lundevall, Arne Simonsson, Claes Tidestav, Mats Blomgren, “Downtilted Base Station Antennas – A Simulation Model Proposal and Impact on HSPA and LTE Performance”,
Ericsson Research, Ericsson AB, Sweden. Presented at VTC 2008.