Faculty of Electrical EngineeringUniversiti Teknologi Malaysia

GROUP PROJECT ASSIGNMENT BRIEF SET 3583 Digital Communication System

0809 semester 2

Lecturer: Dr. Sharifah Kamilah Yusof, P02-325

Project Objective:I To study one aspect of digital communications in detailI To gain greater understanding of the underlying theory and practical application

Submission Date: Monday, April 13th, 2009. Assignment Descriptor:

I Each project is to be group effort.I Your group (4 members per group) must work together on the simulation

programming and report writing must be done according to the work delegation assigned in each group.

I You are allowed to discuss with other groups but you are not allowed to COPY the group’s work.

I If a group disobeys this instruction ZERO marks will be given to the group.Assignment format:

I All simulation projects intend to explain and illustrate the principles and ideas behind this technique.

I The implementation will be as software.I Your simulation work needs to be compared to the conventional communication

system where the enhancement of the can be clearly observed and illustrated.I Monte Carlo simulations must be performed to evaluate bit error probabilities.I Using proper documentation methods. Your programming needs to be heavily

commented.I Describe your group’s schedule, number of discussion sessions, hours spent at the

computer and your project organisational approach to solve this problem.I A project report (typed) is required for all projects. Provide a f o r m al docu m en t a t i on

using IEEE journal report writing formatI The report should also include a CD with a MATLAB simulation that demonstrates

your results.I The report will be part of your student portfolio for this course.

In-class activities:Group work discussion (10 minutes/week for 2 weeks)

Out-of-class activities:Project work development

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Appended are several suggested projects. Alternative projects must be arranged in advance with the instructor.

1. Error Control Coding IThe objective of this project is to select, analyse, and implement an (n,k) linear block code with dmin at least 3 and k at least 10. (or, alternatively, you can select a rete- ½ convolutional code with at least an 8-state trellis, and use a Viterbi decoder). The implementation will be as software. The software should be broken up into at least twoprograms. The encoder will accept input bits (in groups of one or more blocks of k) and generate encoded bits (in groups of one or more blocks of n). The decoder will accept n-tuples and produce decoded k-tuples. Additional software must allow for the insertion of errors in the encoded bits stream. These results should then be compared to the theoretical expressions.

2. Error Control Coding IIThe objective of this project is to select, analyse, and implement a ½-rate convolutionalcode with at least an 4-state trellis, and use a Viterbi hard-decision decoder. The software should be broken up into at least two programs. The encoder will accept input bits (in groups of one or more blocks of k) and generate encoded bits (in groups of one or more blocks of n). The decoder will accept n-tuples and produce decoded k-tuples. Additional software must allow for the insertion of errors in the encoded bits stream. These results should then be compared to the theoretical expressions.

3. Partial Response SignalingThe objective of this project is to implement a class-IV partial response signaling, with decoding options that include both hard decision and soft decision decoding. Thesimulation will be for the discrete-time equivalent of the partial response signaling method, and should allow for channel noise to be added. Specifically, the input bits are to be coded using precoding, the level shifting, and the PRS equations

b(n) = a(n) ⊕ b (n − 2)q(n) = 2b(n) − 1s(n) = q(n) − q(n − 2)

Independent and identically distributed Gaussian noise samples of variance σ 2

added to form the receiver sampleR(n) = s(n) + z(n)

are

Detection is done using either hard-decision decoding or soft-decision (Viterbi)decoding. Monte Carlo simulations should be run to determine the decoded bit error rates for various channel noise variances and the hard and soft decision decoding. A onebit/symbol transmission is adequate.

4. Direct Sequence Spread SpectrumDesign a simulation that illustrates the concepts of spectral spreading and despreadingin Direct Sequence Spread Spectrum. To start use fm = 200Hz. All frequencies have to be scaled down accordingly.Illustrate the transmission and detection process for:

a) Baseband Transmission b) Passband Transmission

Illustrate the case for: (i) Single user, (ii) two users (iii) Inc l ude t he a r ri v al of m u lti p a t h si g na l s in your simulation. Provides frequency and time domain plots of the signal as itpasses through your system.

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5. Lempel-Ziv data compression encoder/decoderDesign and implementation of Lempel-Ziv data compression encoder/decoder in anAWGN channel.

6. Huffman data compression encoder/decoderDesign and implementation of Huffman data compression encoder/decoder in anAWGN channel.

7. Block Coded ModulationDesign and implementation of linear block codes and M-ary Modulation in an AWGNchannel

8. Combined convolutional codes and Hybrid BASK/MPSK modulationDesign and implementation of a combined convolutional codes and HybridBASK/MPSK modulation scheme and comparison of their performances in Gaussian channels.

9. EqualizerDesign an equalization technique to reduce the errors due to ISI in a wirelesscommunication link. Show the effect of the channel and noise on the performance of the technique on the communication link.

10. Frequency-Hopping Spread SpectrumDesign a simulation that illustrates the concepts of hopping and dehopping inFrequency-Hopping Spread Spectrum.Illustrate the transmission and detection process for:

a) Baseband Transmission b) Passband Transmission

Illustrate the case for: (i) Single user, (ii) two users (iii) Inc l ude t he a r ri v al of m u lti p a t h si g na l s in your simulation. Provides frequency and time domain plots of the signal as it passes through your system.

11. Cyclic Encoder/DecoderDesign a cyclic encoder/encoder circuit that is capable of correcting a 1-bit error.Demonstrate the performance of your design. You are encouraged to use Quartus in this design work.

12. Bandlimited ChannelYou are required to study and simulate on the effects of bandlimited channels in datatransmission system. Use a technique to solve the problems in this scenario.

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Generic Skill Assessed:1. Critical Thinking & Problem Solving2. Communication/writing Skills

Generic Skills Addressed:1. Lifelong Learning2. Ethics and Integrity

Assessment:Simulation : 50%Report writing : 50%TOTAL : 100%

G r oup P r o j e ct A s si g n m ent A ss e ss m ent G u i d e li n e

Grade Descriptors

A to A- The simulation shows evidence of critical understanding of the subject materials. Substantial simulation efforts have been put to solve/evaluate the problems critically and analytically. The simulation is correctly done and the report is well-organised and deliverable.

B- to B+ The simulation shows evidence of good understanding of the subject materials.Good efforts have been put to solve/evaluate the problems critically and analytically. The simulation is correctly done and the report is generally well-organised and deliverable.

C- to C+ The simulation shows evidence of fair understanding of the subject materials.Sufficient efforts have been put to solve/evaluate the problems critically and analytically. The simulation is partly correct and report is organised and deliverable.

D to D+ The simulation shows evidence of insufficient understanding of the subjectmaterials. Insufficient efforts have been put to solve/evaluate the problems critically and analytically. The simulation is wrong and report is fairly organised and deliverable.

E The simulation shows evidence of no understanding of the subject materials.Evidence of plagiarism on the simulation. No efforts have been put to solve/evaluate the problems critically and analytically. The simulation is wrongand report is poorly-organised and deliverable.

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APPENDIX:

Monte Carlo simulation is a commonly used tool to model communication system performance when subjected to noise presence. This is most simply thought of as many repetitions of an experiment, and the performance determined by averages over the different experiments. For example, suppose we want to evaluate the performance of a modulations system subject to additive white channel noise, such as for the PRS systems in project option2. The we can generate a sequence of N transmitted levels, s(n), based on an input sequence of N bits (and suitable initial conditions). We use a random number generator to generate N pseudo-random Gaussian samples of a selected variance. These are added to the s(n) to form r(n). The demodulator then produces the decoded bits (using, in this case, either hard-or soft- decision decoding). We then compare the decoded bits to the original (known) bits and count the number of errors. If we have K bit errors in the N bits, then, for this one simulation “run” the overall bit error rate is estimated as K/N. In practice, one repeats this experiment many times, say M times, and then averages the K/N estimates over the M repetitions. The size, N, must be selected depending on the probability of bit error expected. For example, if we expect a bit error probability of about 10-2, then N needs to be significantly larger than 1/10-2. A value of 10,000 would be reasonable. Choosing M = 100 would then be a good combination. Note that if the probability of bit error gets small, then the Monte Carlo complexity becomes prohibitive. For example, to measure bit error rates of about 10-6 we would need about input bit strings of length about 108. Don’t try to do Monte Carlo simulations for bit error rates less than 10-4.

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