Ec 3501 WIRELESS COMMUNICATION
LABORATORY
III YEAR- V SEM
2023-2024
(ODD SEMESTER)
1
2
DEPARTMENT OF ECE
PROGRAM EDUCATIONAL OBJECTIVES (PEOs)
PEO1: To excel in professional career and
higher education by acquiring knowledge in the area of Electronics and
Communication Engineering.
PEO2: To analyze and design appropriate
Electronics and Communication systems to provide solutions that are technically
advanced, economically feasible and socially acceptable.
PEO3: To produce Electronics and
Communication Engineering graduates with sufficient breadth in electronics and its related fields
enabling to work in multidisciplinary
environments.
PEO4: To produce graduates with a
professional outlook who can communicate effectively and interact responsibly in their profession.
PEO5: To pursue lifelong learning by
adapting present and future trends to render service to the nation.
PROGRAM OUTCOMES (POs)
On completion of the B.E (ECE) degree
the Electronics and Communication graduates will be able to
PO1: Apply knowledge of mathematics, science and Electronics
& Communication Engineering fundamentals on the solutions of complex
engineering problems. (Engineering
knowledge skill)
PO2: Identify, formulate and analyze complex Electronics and
Communication Engineering problems. (Problem
analysis skill)
PO3: Design solutions for complex problems and design
systems or processes for a specific need with real time constraints. (Design/ Development of solutions)
PO4: Conduct experiments as well as design, analysis and
acquire results for complex Electronics and Communication problems. (Conduct investigations of complex problems)
PO5: Select and apply proper techniques and modern
engineering tools which are relevant to Electronics and Communication
Engineering applications. (Modern tool
usage)
PO6: Apply reasoning informed by the contextual knowledge to
assess societal, health, safety, legal and cultural issues and consequent
responsibilities relevant to the professional engineering practice. (The engineer and society)
PO7: Examine the impact of engineering solutions in social
and environmental context and apply knowledge for continuous development. (Creative skill and sustainability)
PO8: Develop consciousness of professional and ethical
responsibilities in the field of Electronics and Communication Engineering. (Professional Integrity/Ethics)
PO9: Apply skill to function as an individual and as a
member or a leader in a multidisciplinary team. (Individual and team work)
3
PO10: Communicate effectively on complex Electronics and
Communication Engineering activities with the engineering community and with
society at large, such as, being able to comprehend and write effective reports
and design documentation, make effective presentations.
(Communication skill)
PO11: Manage Projects and finance in multidisciplinary
environment as a member or leader in a team using engineering and management
principles. (Project finance management
skill)
PO12: Recognize the need to engage
in lifelong learning. (Life-long
learning skill)
PROGRAM SPECIFIC OUTCOMES (PSOs)
PSO1: An ability to apply creativity in design and
development of electronic circuits, equipment, components and systems. (Hardware designing skill)
PSO2: An ability to apply contextual knowledge of microprocessor,
micro controller, DSP processor and software tools in embedded systems. (Software and Hardware Interface)
PSO3: An ability to comprehend the knowledge of wired,
wireless communication networks in
telecommunication industries. (Communication
Systems)
4
EC3501 WIRELESS
COMMUNICATION L T P C 3 0 2 4
COURSE OBJECTIVES:
●
To study and understand the concepts
and design of a Cellular System.
●
To Study And Understand Mobile Radio
Propagation And Various Digital Modulation Techniques.
●
To Understand The Concepts Of
Multiple Access Techniques And Wireless Networks
UNIT-I
THE CELLULAR CONCEPT-SYSTEM DESIGN FUNDAMENTALS
9
Introduction-FrequencyReuse-Channel
Assignment Strategies-Handoff Strategies:Prioritizing Handoffs, Practical
Handoff Considerations. Interference And System Capacity: Co-Channel
Interference And System Capacity-Channel Planning For Wireless Systems,
Adjacent Channel Interference, Power Control For Reducing Interference,
Trunking And Grade Of Service. Improving Coverage And Capacity In Cellular
Systems: Cell Splitting, Sectoring.
UNIT-II
MOBILE RADIO PROPAGATION 9
Large Scale Path Loss: Introduction
To Radio Wave Propagation - Free Space Propagation Model – Three Basic
Propagation Mechanism: Reflection – Brewster Angle- DiffractionScattering.Small
Scale Fading And Multipath: Small Scale Multipath Propagation, Factors
Influencing Small-Scale Fading, Doppler Shift, Coherence Bandwidth, Doppler
Spread And Coherence Time. Types Of Small- Scale Fading: Fading Effects Due To
Multipath Time Delay Spread, Fading Effects Due To Doppler Spread.
UNIT- III
MODULATION TECHNIQUES AND EQUALIZATION AND DIVERSITY 9
Digital Modulation – An Overview:
Factors That Influence The Choice Of Digital Modulation,Linear Modulation
Techniques: Minimum Shift Keying
(MSK), Gaussian Minimum ShiftKeying(GMSK), Spread Spectrum Modulation
Techniques: Pseudo- Noise (PN)
Sequences,Direct Sequence Spread Spectrum (DS-SS)- Modulation Performance In
Fading And MultipathChannels- Equalization, Diversity And Channel Coding:
Introduction-Fundamentals Of Equalization- Diversity Techniques: Practical
Space Diversity Considerations, Polarization Diversity, Frequency Diversity,
Time Diversity.
UNIT-
IV MULTIPLE ACCESS TECHNIQUES 9
Introduction: Introduction To
Multiple Access- Frequency Division Multiple Access(FDMA)- TimeDivision
Multiple Access(TDMA)- Spread Spectrum Multiple Access-Code Division
MultipleAccess(CDMA)- Space Division Multiple Access(SDMA)- Capacity Of
Cellular Systems: Capacity Of Cellular CDMA, Capacity Of CDMA With Multiple
Cells.
UNIT-
V WIRELESS NETWORKING 9
Introduction: Difference Between
Wireless And Fixed Telephone Networks, The Public Switched Telephone
Network(PSTN), Development Of Wireless Networks: First Generation Wireless
Networks, Second Generation Wireless Networks, Third Generation Wireless
Networks, Fixed Network Transmission Hierarchy, TrafficRoutingInWireless
Networks: Circuit Switching, Packet
Switching- Personal Communication Services/ Networks(PCS/PCNs):Packet Vs
Circuit Switching For PCN, Cellular Packet- Switched Architecture- Packet
Reservation Multiple Access(PRMA)- Network Databases: Distributed Database For
Mobility Management- Universal Mobile Telecommunication Systems(UMTS).
45 PERIODS
PRACTICAL
EXERCISES: 30 PERIODS
1.
Modeling of wireless communication
systems using Matlab(Two ray channel and
Okumura –Hata model)
2.
Modeling and simulation of Multipath
fading channel
3.
Design, analyze and test Wireless
standards and evaluate the performance measurements such as BER, PER, BLER, throughput, capacity,
ACLR, EVM for 4G and 5G using Matlab
4.
Modulation: Spread Spectrum – DSSS
Modulation & Demodulation 5
5.
Wireless Channel equalization:
Zero-Forcing Equalizer (ZFE),MMSE
Equalizer(MMSEE),Adaptive Equalizer
(ADE),Decision Feedback Equalizer (DFE)
6.
Modeling and simulation of TDMA,
FDMA and CDMA for wireless communication
TOTAL:75 PERIODS
COURSE OUTCOMES :
Upon successful completion of the
course the student will be able to:
CO1:Understand The Concept And
Design Of A Cellular System.
CO2:Understand Mobile Radio
Propagation And Various Digital Modulation Techniques.
CO3:Understand
The Concepts Of Multiple Access Techniques And Wireless Networks
CO4:Characterize a wireless channel and evolve the system design specifications
CO5:Design a cellular system based on resource availability and traffic
demands.
TEXT BOOK :
1. Rappaport,T.S.,-Wireless communications”, Pearson Education,
Second Edition, 2010.
REFERENCES :
1. Wireless Communication –Andrea
Goldsmith, Cambridge University Press, 2011 2. Van Nee, R. and Ramji Prasad,
―OFDM for wireless multimedia
communications, Artech House, 2000 3. David Tse and Pramod Viswanath,
―Fundamentals of Wireless Communication, Cambridge University Press, 2005.
4. Upena Dalal, ―Wireless
Communication”, Oxford University Press, 2009. 5. Andreas.F. Molisch, ―Wireless
Communications”, John Wiley – India, 2006. 6. Wireless Communication and
Networks –William Stallings ,Pearson Education, Second Edition 2002
6
COURSE OUTCOMES:
On Completion of the course students will be able to
|
Course Outcome |
Statement |
|
CO1 |
Understand The Concept And Design Of A Cellular System |
|
CO2 |
Understand Mobile Radio Propagation And Various Digital
Modulation Techniques. |
|
CO3 |
Understand The Concepts Of Multiple Access Techniques And
Wireless Networks |
|
CO4 |
Characterize a wireless channel and evolve the system
design specifications |
|
CO5 |
Design a cellular system based on resource availability
and traffic demands. |
TABLE
OF CONTENTS
|
S.NO |
DATE |
NAME OF THE EXPERIMENT |
Signature |
|
1. |
|
Modeling of wireless communication systems using
Matlab(Two ray channel and Okumura
–Hata model) |
|
|
2. |
|
Modeling and simulation of Multipath fading channel |
|
|
3. |
|
Design, analyze and test Wireless standards and evaluate
the performance measurements such as
BER, PER, BLER, throughput, capacity, ACLR, EVM for 4G and 5G using Matlab |
|
|
4. |
|
Modulation: Spread Spectrum – DSSS Modulation &
Demodulation |
|
|
5. |
|
Wireless Channel equalization: Zero-Forcing Equalizer
(ZFE),MMSE |
|
|
6. |
|
Equalizer(MMSEE),Adaptive Equalizer (ADE),Decision
Feedback Equalizer (DFE) |
|
Expt. No: 1
Modeling of wireless communication
systems using Matlab
(Two ray channel and
Okumura –Hata model)
AIM:
To generate
basic modeling of wireless communication systems using MATLAB.
SOFTWARE REQUIRED:
System with
MATLAB 9.0
PROGRAM FOR TWO RAY CHANNEL
lambda =
0.3; ht100=100; ht30=30; ht2=2; hr=2;
axis=[];
p100=[]; p30=[]; p2=[]; pfsl=[];
for
i=1000:5000 d=10^(i/1000); axis =[axis d];
fspower
= (lambda/(4*3.1415*d))^2 ; power100
= fspower * 4 *(sin(2*3.1415*hr*ht100/(lambda*d)))^2; power30
= fspower* 4 *(sin(2*3.1415*hr*ht30/(lambda*d)))^2; power2
= fspower * 4 *(sin(2*3.1415*hr*ht2/(lambda*d)))^2;
p100 =[p100, 10*log10(power100)]; p30 =[p30, 10*log10(power30)]; p2 =[p2, 10*log10(power2)]; pfsl=[pfsl, 10*log10(fspower)]; end
text('FontSize',18)
semilogx(axis,p100,
'g-',axis,p30, 'b-',axis,p2, 'r-',axis,pfsl,'y-')
xlabel('distance in m'); ylabel('pathloss');
text(1000,-66,'blue : hr=30m');
text(1000,-74,'red : hr=2m');
text(1000,-58,'red : hr=100m');
text(1000,-50,'yellow:
free space');
text(50,-180,'lambda
= 0.30 m'); text(50,-190,'hr = 2 m');
PROGRAM FOR OKUMURA – HATA MODEL clc;
clear all;
close all;
fc=1500;%
frequency of transmission in MHz fc2=150;
hb= 70;%
effective height of transmitting base station antenna in meters hm=1.5;%
effective receiving mobile device antenna height in meters hm1=(1.1*log10(fc)-0.7)*hm-(1.56*log10(fc)-0.8);%Open
hm2=8.29*(log10(1.54*hm))^2-1.1;%Metropolitan fc<200
hm3=3.2*(log10(11.75*hm))^2-4.92;%Metropolitan fc>200
C=-2*(log10(fc/28))^2-5.4;
C2=-4.78*(log10(fc))^2+18.33*log10(fc)-40.98;
C3=0;
d =
(1:50:100);
A=69.55+26.16*log10(fc)-13.82*log10(hb)-hm1;%Open
A2=69.55+26.16*log10(fc2)-13.82*log10(hb)-hm2;%Metropolitan
fc<=200
A3=69.55+26.16*log10(fc)-13.82*log10(hb)-hm3;%Metropolitan
fc>=200
B=44.9-6.55*log10(hb);
Plopen=A+B*log10(d)+C
%Suburban
Plsub=A+B*log10(d)+C2
%Open
%Plmp1=A2+B*log10(d)+C3
%Metropolitan fc<=200
Plmp2=A3+B*log10(d)+C3
%Metropolitan fc>=200 plot(d,Plopen,d,Plsub,d,Plmp2,'linewidth',2.5);
legend('Suburban','Open','Metropolitan') title('Path-Loss for diffrent
Envirnment ') xlabel('Distance') ylabel('Path-Loss in dB') grid on
Path-Loss for diffrent
Envirnment
RESULT:
Thus the
modeling of wireless communication systems are generated using MATLAB.
Expt. No: 2
Modeling and simulation of Multipath
fading channel AIM:
To generate
Modeling and simulation of Multipath fading channel using MATLAB.
SOFTWARE REQUIRED:
System with
MATLAB 9.0
PROGRAM FOR FREQUENCY SELECTIVE FADING CHANNEL FROM POWER
DELAY
PROFILE
clc; close
all; clear all;
% Frequency
selective fading channel from power delay profile
t=[0,0.2,0.5,1.6,2.3,5] ;
pdp=[0.189,0.379,0.293,0.095,0.061,0.037]; LP
= length(pdp); % number of taps paths_r = sqrt(pdp/2).*sqrt((randn(1,LP)).^2 +
(randn(1,LP)).^2); %these are paths_random tap_coeff=zeros(1,51);
tap_coeff(ceil(t./0.1)+1)=paths_r; %filling in the non-zero values in the
channel response totP=sum(abs(tap_coeff).^2); % summation of total power
coefficients h=tap_coeff/sqrt(totP); %
normalization of power h;
stem(h); xlabel('Time delay'); ylabel('Power '); title('Frequency selective
fading channel'); legend('Frequency selective fading channel');
PROGRAM FOR FLAT FADING CHANNEL clc; close all; clear all;
% Flat
fading channel h=alpha*exp(j*theta)
N=10;
p=2*pi*rand(1,N); pf=p./sqrt(var(p)); y1=randn(1,N); y2=randn(1,N); y1=y1/sqrt(var(y1)); y2=y2/sqrt(var(y2)); alpha=sqrt(y1.^2+y2.^2); alpha1=mean(alpha.^2); h=alpha1.*complex(cos(pf),sin(pf)); stem(abs(h)); xlabel('Samples'); ylabel('Magnitude '); title('Flat Frequency fading
channel'); legend('Flat fading');
RESULT:
Thus the
Modeling and simulation of Multipath fading channel are generated using MATLAB.
Expt. No: 3
Design, analyze and test Wireless standards and evaluate the
performance measurements such as BER,
PER, BLER, throughput, capacity, ACLR, EVM for 4G and 5G using Matlab.
AIM:
To design,
analyze and test Wireless standards and evaluate the performance
measurements such as BER, PER, BLER,
throughput, capacity, ACLR, EVM for 4G and 5G using Matlab.
SOFTWARE REQUIRED:
System with
MATLAB 9.0
PROGRAM FOR
BER
coderate
= 1/4; % Code rate
% Create a
structure, dspec, with information about the distance spectrum.
Define the energy per bit to noise power spectral density ratio (Eb/N0) sweep range and generate
the theoretical bound results.
dspec.dfree
= 10; % Minimum free distance of code dspec.weight = [1 0 4 0 12 0 32 0 80 0
192 0 448 0 1024 ...
0 2304 0 5120 0]; % Distance spectrum of
code EbNo = 3:0.5:8;
berbound
= bercoding(EbNo,'conv','soft',coderate,dspec);
%Plot the theoretical bound results.
semilogy(EbNo,berbound)
xlabel('E_b/N_0 (dB)'); ylabel('Upper
Bound on BER');
title('Theoretical
Bound on BER for Convolutional Coding'); grid on;
RESULT
BLER
% Set
up DL-SCH coding parameters
TBS =
3816; % Transport block size,
a positive integer codeRate = 308/1024;
% Target code rate, a real number between 0 and 1 rv = 0; % Redundancy version, 0-3
modulation
= 'QPSK'; % Modulation scheme, QPSK,
16QAM, 64QAM, 256QAM nlayers = 1;
% Number of layers, 1-4 for a transport block cbsInfo =
nrDLSCHInfo(TBS,codeRate); disp('DL-SCH coding parameters') disp(cbsInfo)
switch modulation case 'QPSK' bitsPerSymbol = 2; case '16QAM' bitsPerSymbol = 4; case '64QAM' bitsPerSymbol = 6; case '256QAM' bitsPerSymbol = 8; end
% Set
up AWGN channel EbNo = 1.25; % in dB outlen = ceil(TBS/codeRate); snrdB =
convertSNR(EbNo,"ebno",...
BitsPerSymbol=bitsPerSymbol,CodingRate=TBS/outlen);
%
Random transport block data generation in = randi([0 1],TBS,1,'int8'); %
Transport block CRC attachment tbIn = nrCRCEncode(in,cbsInfo.CRC); % Code block
segmentation and CRC attachment cbsIn =
nrCodeBlockSegmentLDPC(tbIn,cbsInfo.BGN);
% LDPC
encoding
enc =
nrLDPCEncode(cbsIn,cbsInfo.BGN); % Rate matching and code block concatenation
chIn =
nrRateMatchLDPC(enc,outlen,rv,modulation,nlayers);
%
Symbol mapping
symOut
= nrSymbolModulate(chIn,modulation);
% AWGN
channel
[rxSig,
noiseVar] = awgn(symOut,snrdB);
%
Symbol demapping
rxllr
= nrSymbolDemodulate(rxSig,modulation,noiseVar);
% Rate
recovery
raterec
= nrRateRecoverLDPC(rxllr,TBS,codeRate,rv,modulation,nlayers); % LDPC decoding,
with early termination and at most 12 iterations decBits =
nrLDPCDecode(raterec,cbsInfo.BGN,12);
% Code
block desegmentation and CRC decoding
[blk,~]
= nrCodeBlockDesegmentLDPC(decBits,cbsInfo.BGN,TBS+cbsInfo.L);
%
Transport block CRC decoding
[out,~]
= nrCRCDecode(blk,cbsInfo.CRC);
%
Compare blockError = any(out~=in)
EVM FOR 4G AND 5G
%
Select one of the Release 15 NR-TMs for FR1 and FR2 among:
%
"NR-FR1-TM1.1","NR-FR1-TM1.2","NR-FR1-TM2",
%
"NR-FR1-TM2a","NR-FR1-TM3.1","NR-FR1-TM3.1a",
%
"NR-FR1-TM3.2","NR-FR1-TM3.3","NR-FR2-TM1.1",
%
"NR-FR2-TM2","NR-FR2-TM2a","NR-FR2-TM3.1","NR-FR2-TM3.1a"
% or
%
Select one of the Release 15 FRCs for FR1 and FR2 among:
%
"DL-FRC-FR1-QPSK","DL-FRC-FR1-64QAM",
%
"DL-FRC-FR1-256QAM","DL-FRC-FR2-QPSK",
%
"DL-FRC-FR2-16QAM","DL-FRC-FR2-64QAM"
rc =
"NR-FR1-TM3.2"; % Reference channel (NR-TM or FRC)
%
Select the NR waveform parameters bw = "10MHz"; % Channel bandwidth
scs = "30kHz"; % Subcarrier spacing dm = "FDD"; % Duplexing
mode targetRNTIs = []; displayEVM = true; plotEVM = true; if displayEVM
fprintf('Reference Channel = %s\n', rc);
end
evm3GPP
= false; phaseNoiseOn = true; IQImbalanceON = true; filterOn = true;
nonLinearityModelOn = true; OSR = 5; % oversampling factor
%
Create waveform generator object
tmwavegen
= hNRReferenceWaveformGenerator(rc,bw,scs,dm);
%
Waveform bandwidth
bandwidth
= tmwavegen.Config.ChannelBandwidth*1e6;
if OSR
> 1
% The |Config| property in |tmwavegen|
specifies the configuration of
% the standard-defined reference waveform.
It is a read-only property.
% To customize the waveform, make the
|Config| property writable. tmwavegen
= makeConfigWritable(tmwavegen);
% Increase the waveform sample rate by
multiplying the nominal sample
% rate with |OSR|
nominalSampleRate =
getNominalSampleRate(tmwavegen.Config);
tmwavegen.Config.SampleRate = nominalSampleRate*OSR; else
filterOn = false; end
%
Generate the waveform and get the waveform sample rate
[txWaveform,tmwaveinfo,resourcesinfo]
=
generateWaveform(tmwavegen,tmwavegen.Config.NumSubframes);
sr = tmwaveinfo.Info.SamplingRate; % waveform sample rate txWaveform =
txWaveform/max(abs(txWaveform),[],'all'); txWaveform = repmat(txWaveform,2,1);
if phaseNoiseOn % Carrier frequency
if tmwavegen.Config.FrequencyRange ==
"FR1" % carrier frequency for FR1
fc = 4e9;
else % carrier frequency for FR2 fc = 30e9; end
% Calculate the phase noise level
foffsetLog = (4:0.2:log10(sr/2)-0.001); %
model offset from 10e3Hz to %
almost sr/2. To avoid
%
aliasing, the sample rate
%
must be greater than twice
%
the largest value specified %
by FrequencyOffset foffset =
10.^foffsetLog; % linear frequency
offset PN_dBc_Hz = hPhaseNoisePoleZeroModel(foffset,fc,'C'); figure; semilogx(foffset,PN_dBc_Hz); xlabel('Frequency offset (Hz)'); ylabel('dBc/Hz');
title('Phase noise model'); grid on
% Apply phase noise to the waveform
pnoise = comm.PhaseNoise('FrequencyOffset',foffset,'Level',PN_dBc_Hz,'SampleRate',sr); pnoise.RandomStream = "mt19937ar with
seed"; rxWaveform =
pnoise(txWaveform); release(pnoise);
else
rxWaveform = txWaveform; %#ok<UNRCH>
end
RESULT
RESULT:
Thus the
design, analyze and test Wireless standards and evaluate the performance
measurements such as BER, PER, BLER,
throughput, capacity, ACLR, EVM for 4G and 5G was generated successfully using
Matlab.
Expt. No: 4
Modulation: Spread Spectrum – DSSS
Modulation & Demodulation AIM:
To simulate
Spread Spectrum – DSSS Modulation & Demodulation using MATLAB.
SOFTWARE REQUIRED:
System with
MATLAB 9.0
PROGRAM FOR
MODULATION: SPREAD SPECTRUM – DSSS MODULATION &
DEMODULATION
% Direct
Sequence Spread Spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc clear
% Generating
the bit pattern with each bit 20 samples long b=round(rand(1,30));
pattern=[];
for k=1:30 if b(1,k)==0 sig=-ones(1,20); else
sig=ones(1,20); end
pattern=[pattern sig];
end
subplot(4,1,1) plot(pattern); axis([-1 620 -1.5 1.5]); title('Original Bit
Sequence');
% Generating
the pseudo random bit pattern for spreading d=round(rand(1,120)); pn_seq=[];
carrier=[];
t=[0:2*pi/4:2*pi]; % Creating 5 samples for one cosine for k=1:120 if d(1,k)==0 sig=-ones(1,5); else
sig=ones(1,5); end
c=cos(t); carrier=[carrier
c];
pn_seq=[pn_seq sig];
end
% Spreading
of sequence spreaded_sig=pattern.*pn_seq;
subplot(4,1,2)
plot(spreaded_sig) axis([-1 620 -1.5 1.5]);
title('Spreaded
signal');
% BPSK
Modulation of the spreaded signal bpsk_sig=spreaded_sig.*carrier; % Modulating the signal
subplot(4,1,3);
plot(bpsk_sig) axis([-1 620 -1.5 1.5]); title('BPSK Modulated Signal');
%Plotting the FFT of DSSS signal y=abs(fft(xcorr(bpsk_sig))); subplot(4,1,4)
plot(y/max(y)) xlabel('Frequency') ylabel('PSD')
%Demodulation
and Despreading of Received Signal
figure
rxsig=bpsk_sig.*carrier; demod_sig=[]; for i=1:600 if rxsig(i)>=0 rxs =1; else rxs =-1;
end
demod_sig=[demod_sig rxs]; end subplot(3,1,1) plot(demod_sig) axis([-1
620 -1.5 1.5]); title('Demodulated Signal') despread_sig=demod_sig.*pn_seq;
subplot(3,1,2)
plot(despread_sig) axis([-1 620 -1.5 1.5]); title('Despreaded data')
%Power
Spectrum of Despreaded data
z=0.5+0.5*despread_sig; y=abs(fft(xcorr(z))); subplot(3,1,3)
plot(y/max(y)) axis([0 500 0 1.5]) xlabel('Frequency') ylabel('PSD')
Demodulated Signal
Frequency
RESULT:
Thus the
simulation of Spread Spectrum – DSSS
Modulation & Demodulation was generated using
MATLAB.
Expt. No: 5
Wireless Channel equalization:
Zero-Forcing Equalizer (ZFE), MMSE AIM:
To Model and
simulate the Wireless Channel Equalization: Zero-Forcing Equalizer (ZFE), MMSE
using
MATLAB.
SOFTWARE REQUIRED:
System with
MATLAB 9.0
PROGRAM FOR ZERO FORCING clc; close
all; clear all;
N=10;
p=2*pi*rand(1,N); pf=p./sqrt(var(p)); y1=randn(1,N); y2=randn(1,N); y1=y1/sqrt(var(y1)); y2=y2/sqrt(var(y2)); alpha=sqrt(y1.^2+y2.^2); alpha1=mean(alpha.^2); h=alpha1.*complex(cos(pf),sin(pf)); stem(abs(h));
% zero forcing equalizer equalizer=
conj(h);
equalizedh = h.* equalizer;
stem(abs((equalizer)));
PROGRAM ON ESTIMATION AND MMSE EQUALIZATION FOR OFDM SYSTEM
clc close
all clear all
H =
comm.QPSKModulator('BitInput',true);
Hdemod =
comm.QPSKDemodulator('BitOutput',true);
% hScope =
commscope.ScatterPlot;
%
hScope.Constellation = [0.7071+0.7071i -0.7071+0.7071i -0.7071-0.7071i
0.7071-0.7071i];
%
hScope.SamplesPerSymbol = 1; n=16; %no
of random data... r_data = randint(n,1); %random numbers generator...
data_qpsk=[];
data_qpsk = step(H,r_data); %Data converted in QPSK symbols... d1=data_qpsk(1);
d5=data_qpsk(5); data_qpsk(1) = 0.7071+0.7071i; % adding two pilots at location
1 and 5. data_qpsk(5) = 0.7071+0.7071i; % update(hScope, data_qpsk);
dawgn=awgn(data_qpsk,0);
% Adding white Gaussian Noise est(1)=dawgn(1); est(2)=dawgn(5); % MMSE starts
here...... des=[0.7071+0.7071i 0.7071+0.7071i];% desired data symbols
rec=[est(1) est(2)]; % received data symbols... z=filter([1 0 0 0 0 0 0
0],[1],rec);
Rxx=xcorr(rec);
Rxz=xcorr(des,z);
x=toeplitz([Rxx
zeros(1,5)],zeros(1,8)) cof=x\([Rxz zeros(1,5)].'); % coefficients for MMSE
equalizer... det1=filter(cof,[1],dawgn); det1 for i=1:8
det(i)=filter(cof,[1],dawgn(i));
end
det.'
%
update(hScope, det);
RESULT:
Thus the
Modeling and simulation of the Wireless Channel Equalization: Zero-Forcing
Equalizer (ZFE), MMSE was generated using MATLAB.
Expt. No: 6
Equalizer(MMSEE),Adaptive Equalizer (ADE),Decision Feedback
Equalizer (DFE)
AIM:
To Model and
simulate the Equalizer(MMSEE),Adaptive Equalizer (ADE),Decision Feedback
Equalizer (DFE) using MATLAB.
SOFTWARE REQUIRED:
System with
MATLAB 9.0
PROGRAM FOR ADAPTIVE EQUALIZER
%%%%%%%%%%%%adaptive
system identification%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%Manolis
Tsakiris%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all close all
%simulation
length
N = 1000;
%channel to
be identified M = 9; wo = randn(M,1);
wo = wo /
norm(wo);
%excitation
signal
u =
randn(1,N);
%channel
output
y =
filter(wo,1,u);
%additive
noise to the channel output
SNR = 30;
var_v = var(y) * 10^(-SNR/10);
v =
var_v^0.5 * randn(1,N);
%desired
signal
d = y + v;
%NLMS
adaptive system identification w = zeros(M,1); u_regressor = zeros(1,M);
step = 0.5;
epsilon = 10^(-6); msd = zeros(1,N); for k = 1 : N
u_regressor
= [u(k) u_regressor(1:M-1)]; e = d(k) - u_regressor * w;
w = w + step
* u_regressor' * e / (u_regressor * u_regressor' + epsilon); msd(k) = (w-wo)' *
(w-wo);
end
figure;
plot(10*log10(msd));
ylabel('MSD(dB)');
xlabel('iterations');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%
And this is
a sample code for adaptive channel equalization:
%%%%%%%%%adaptive
channel equalization%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%Manolis
Tsakiris%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all
close all
%simulation
length
N = 1000;
%channel
length
M = 5;
%number of
independent trials
T = 100;
cascade_impulse_response
= zeros(1,2*M-1); for j = 1 : T
%training
signal u = randn(1,N);
%channel to
be equalized c = randn(M,1);
c = c /
norm(c);
%channel
output z = filter(c,1,u);
%additive
noise to the channel output
SNR = 30;
var_v =
var(z) * 10^(-SNR/10);
v =
var_v^0.5 * randn(1,N);
%input to
the equalizer
x = z + v;
%NLMS
channel equalization w = zeros(M,1); x_regressor = zeros(1,M);
step = 0.1;
epsilon = 10^(-6); for k = 4 : N
x_regressor
= [x(k) x_regressor(1:M-1)]; e = u(k-3) - x_regressor * w;
w = w + step
* x_regressor' * e / (x_regressor * x_regressor' + epsilon); end
cascade_impulse_response
= cascade_impulse_response + conv(w,c)'; display(j);
end
figure;
stem(cascade_impulse_response/T);
title('cascade
channel-equalizer impulse response'); xlabel('taps');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%
PROGRAM FOR DECISION FEEDBACK EQUALIZER
% decision
feedback equalizer
%
References: See Section 5.1.8 in the book "Digital Communications and
% Signal
Processing" by K Vasudevan
% QPSK
modulation
clear all
close all clc
training_len
= 10^4; %length of the training sequence snr_dB = 10; % snr in dB
ff_filter_len
= 30; % feedforward filter length fb_filter_len = 20; % feedback filter length
data_len = 10^6; % length of the data sequence
% snr
parameters snr = 10^(0.1*snr_dB);
noise_var_1D
= 0.5*2*1/(2*snr); % noise variance
%
--------------- training phase ------------------------------------------
% source
training_a =
randi([0 1],1,2*training_len);
% qpsk
mapper
training_seq
= 1-2*training_a(1:2:end) + 1i*(1-2*training_a(2:2:end));
% impulse
response of the channel
fade_chan =
[0.9+0.9i 0.1+0.1i 0.1+0.1i 0.1+0.1i 0.1+0.1i];
fade_chan =
fade_chan/norm(fade_chan); chan_len = length(fade_chan);
% awgn
noise = normrnd(0,sqrt(noise_var_1D),1,training_len+chan_len-1)+normrnd(0,sqrt(noise_var_1D),1,training_len+chan_len-
1);
% channel
output
chan_op =
conv(fade_chan,training_seq)+noise;
%
------------ LMS update of taps------------------------------------------ ff_filter
= zeros(1,ff_filter_len); % feedforward filter initialization fb_filter =
zeros(1,fb_filter_len); % feedback filter initialization ff_filter_ip =
zeros(1,ff_filter_len); % feedforward filter input vector fb_filter_ip =
zeros(1,fb_filter_len); % feedback filter input vector fb_filter_op = 0; %
feedback filter output symbol
% estimating
the autocorrelation of received sequence at zero lag
Rvv0 =
(chan_op*chan_op')/(training_len+chan_len-1);
% maximum
step size
max_step_size
= 2/(ff_filter_len*(Rvv0)+fb_filter_len*(2)); step_size = 0.125*max_step_size;
% step size for i1=1:training_len-ff_filter_len+1 % steady state part
ff_filter_ip(2:end)=ff_filter_ip(1:end-1); ff_filter_ip(1) = chan_op(i1);
ff_filter_op = ff_filter*ff_filter_ip.';
% feedforward filter output
ff_and_fb =
ff_filter_op-fb_filter_op;
error = ff_and_fb-training_seq(i1); %
instantaneous
% hard decision temp1 = real(ff_and_fb)<0; temp2 = imag(ff_and_fb)<0;
quantizer_op = 1-2*temp1 +
1i*(1-2*temp2);
% LMS update
ff_filter=ff_filter-step_size*error*conj(ff_filter_ip);
fb_filter=fb_filter+step_size*error*conj(fb_filter_ip);
fb_filter_ip(2:end)=fb_filter(1:end-1); fb_filter_ip(1) = quantizer_op;
fb_filter_op =
fb_filter*fb_filter_ip.'; end
%------- data transmission
phase----------------------------
% source
data_a =
randi([0 1],1,2*data_len);
% qpsk
mapper
data_seq =
1-2*data_a(1:2:end)+1i*(1-2*data_a(2:2:end));
% awgn noise
= normrnd(0,sqrt(noise_var_1D),1,data_len+chan_len-1)+...
1i*normrnd(0,sqrt(noise_var_1D),1,data_len+chan_len-1);
% channel
output
chan_op =
conv(fade_chan,data_seq)+noise; dec_seq = zeros(1,data_len-ff_filter_len+1);%
output from dfe ff_filter_ip = zeros(1,ff_filter_len); % feedforward filter
input fb_filter_ip = zeros(1,fb_filter_len); % feedback filter input fb_filter_op
= 0; % feedback filter output symbol for i1=1:data_len-ff_filter_len+1 % steady
state part
ff_filter_ip(2:end)=ff_filter_ip(1:end-1); ff_filter_ip(1) = chan_op(i1); ff_filter_op =
ff_filter*ff_filter_ip.';
ff_and_fb = ff_filter_op-fb_filter_op;
% hard decision temp1 = real(ff_and_fb)<0; temp2 = imag(ff_and_fb)<0;
dec_seq(i1) = 1-2*temp1
+1i*(1-2*temp2);
fb_filter_ip(2:end)=fb_filter(1:end-1); fb_filter_ip(1) = dec_seq(i1);
fb_filter_op =
fb_filter*fb_filter_ip.'; % feedback filter output end
% demapping
symbols back to bits dec_a = zeros(1,2*(data_len-ff_filter_len+1));
dec_a(1:2:end) = real(dec_seq)<0; dec_a(2:2:end) = imag(dec_seq)<0;
% bit error
rate
ber =
nnz(dec_a-data_a(1:2*(data_len-ff_filter_len+1)))/(2*(data_len-ff_filter_len+1))
RESULT:
Thus the
Modeling and simulation of the Equalizer (MMSEE),Adaptive Equalizer
(ADE),Decision Feedback Equalizer (DFE)
was generated using MATLAB.
For full manual in pdf click the link
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