Aim & A ; Aims: Modeling a communicating system for following coevals broadband wireless entree systems utilizing a rate A? , 2/3, A? & A ; 5/6 Convolutional Encoders and decrypting the codifications utilizing Viterbi Decoders and farther obtaining a curve of spot error rate Vs signal to resound over an linear white Gaussian noise channel ( AWGN )
Deliverables: In accomplishing this undertaking undertaking, mathematical package MATLAB will be used chiefly because of its handiness and broad scope of it ‘s communicating tool box
Introduction to communicating system
Figure 1: Typical communicating system.
A communicating system is the procedure of acquiring information from the information beginning to the information sink ( finish ) ; but in so making there is tremendous informations that is sent and because bandwidth is expensive there is hence need to cut down the size every bit good as bound the mistakes acquiring to the finish.
In this undertaking the technique used for the forward mistake rectification is known as convolutional encoding with Viterbi as the decipherer
Convolutional encryption and Viterbi decryption are an error rectification technique that is widely used in radio broadband system chiefly because it improves the spot error rate ( BER ) public presentation.
In other to accomplish this undertaking of encoding and decrypting every bit good as acquiring to demo the decreased mistake rate ; the information is foremost Generated and so whirl encoded before go throughing it will be passed through a noisy channel and thenceforth executing the Viterbi decryption to retrieve the input information spots.
Convolutional encoding with Viterbi decryption is a FEC technique that is peculiarly suited to a channel in which the familial signal is corrupted chiefly by linear white Gaussian noise ( AWGN )
The Source Cryptography: this is used to take unwanted spots in the channel
Encoding: Adding redundancy to forestall unauthorised users from accessing the original message
Channel Cryptography: this in itself is to add redundancy spots to cut down noise Internet Explorer unwanted signal in the channel.
1.1 Additive White Gaussian Noise ( AWGN )
AWGN is a channel theoretical account whereby the lone mutilation to the communicating informations is a additive add-on of white noise and that implies a random signal with same power within a fixed bandwidth at a centre frequence ( Flat Power spectral Density in units Watts/Hz of a bandwidth ) . The AWGN is suited for mathematical theoretical accounts because it gives an penetration of the behavior of the system and farther is a good theoretical account for broadband radio systems, orbiter and deep infinite communications.
Error Detection and Correction
Error sensing and rectification is really of import in communicating system but both sensing and rectification work manus in manus and it can be said that it is unpointed to observe mistake and non able to rectify it in a communicating system.
There are fundamentally 2 types of mistake sensing and rectification
Automatic repetition petition
Forward Error Correction
Automatic repetition petition is a signifier of mistake control procedure whereby mistake sensing is combined with ability to re -request for the information. In other words if an mistake is detected the receiving system will inquire for the information to be re-sent.
Forward mistake rectification is a procedure whereby the information is encoded before it is transmitted and as such must be decoded before it is received.
2.1 Block codifications
In this type of coding a block of K data figures is encoded by a codification word of n figures. It is different to Convolutional codification because in the latter cryptography is done on a uninterrupted footing whereby the coded sequence of n-digits depends non merely on K informations figures but besides on the old informations figures.
2.2 Reed-Solomon Coded.
Reed Solomon codifications are block- based codifications with high codification rate and efficiency for burst mistake rectification. It can observe and rectify multiple random symbol mistakes. The reed Solomon codifications are used in deep-space applications every bit good as in Compact Disks ( Cadmium ) and DVD ‘s.
2.3 Convolutional codification
Figure 2: Convolutional codification
Rate: A? Constraint length: 7 Generator multinomials: G1 =1111001, G2 = 1011011
the restraint length is the figure of informations that can unite to give an end product.
The rate signifies the figure of input that produces an end product eg rate A? means for every input signal there is 2 end product signal and the Generator polynomials the combination of signals modulo 2 that produces the needed end product.
3.0 Viterbi decipherer
A Viterbi decipherer uses an algorithm of happening the most likely sequence for decrypting a bitstream that was encoded utilizing the Convolutional codification. “ The algorithm makes a figure of premises. First, both the ascertained events and concealed events must be in a sequence. This sequence frequently corresponds to clip. Second, these two sequences need to be aligned, and an case of an ascertained event demands to match to precisely one case of a concealed event. Third, calculating the most likely hidden sequence up to a certain point T must depend merely on the ascertained event at point T, and the most likely sequence at point T a?’ 1. These premises are all satisfied in a first-order hidden Markov theoretical account ” .
Figure 3: treillage diagram for Viterbi decrypting
4.0 Project design plane and undertakings.
Undertaking 1: -Modeling the Convolutional Encoder of codification rate 1/2.
Undertaking 2: – Modeling the Viterbi Decoder for Task 2.
Undertaking 3: -Obtain a curve of BER ( Bit Error Rate ) vs Signal-to-Noise ( Eb/No ) for Tasks 2 & A ; 3 over an AWGN channel.
Undertaking 4: -Modelling the Convolutional Encoder with configurable codification rates 1/2, 2/3, Task 5: -Modelling the Viterbi Decoder for Task 5.
Undertaking 6: -Obtain curves of BER ( Bit Error Rate ) vs Signal-to-Noise ( Eb/No ) for Tasks 5 & A ; 6 over an AWGN channel.
Undertaking 7: study composing
Figure 4. Undertaking Gantt chart
5.0 Decision
Mention
Lin, Ming-Bo, “ New Path History Management Circuits for Viterbi Decoders, ” IEEE Transactions on Communications, vol. 48, October, 2000, pp. 1605-1608.
G. D. Forney, Jr. , “ Convolutional Codes II: Maximum-Likelihood Decoding, ” Information Control, vol. 25, June, 1974, pp. 222-226.
K. S. Gilhousen et. al. , “ Coding Systems Study for High Data Rate Telemetry Links, ” Final Contract Report, N71-27786, Contract No. NAS2-6024, Linkabit Corporation, La Jolla, CA, 1971.
J. A. Heller and I. M. Jacobs, Viterbi Decoding for Satellite and Space Communications, ” IEEE Transactions on Communication Technology, vol. COM-19, October, 1971, pp. 835-848.
K. J. Larsen, “ Short Convolutional Codes with Maximal Free Distance for Rates 1/2, 1/3, and 1/4, ” IEEE Transactions on Information Theory, vol. IT-19, May, 1973, pp. 371-372.
J. P. Odenwalder, “ Optimum Decoding of Convolutional Codes, ” Ph. D. Dissertation, Department of Systems Sciences, School of Engineering and Applied Sciences, University of California at Los Angeles, 1970.
A. J. Viterbi, “ Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm, ” IEEE Transactions on Information Theory, vol. IT-13, April, 1967, pp. 260-269
hypertext transfer protocol: //home.netcom.com/~chip.f/viterbi/tutorial.html
Modeling of Convolutional Encoders with Viterbi Decoders for Next Generation Broadband Wireless Access Systems
Introduction
IEEE STD 802.16-2009 has defined following coevals broadband wireless entree systems, in which an FEC with the concatenation of a Reed-Solomon outer codification and a rate-compatible convolutional inner codification should be supported. In this undertaking, pupils are required to make a literature reappraisal on channel coding for following coevals broadband wireless entree systems and so compose a C/MATLAB theoretical account for convolutional Encoders with Viterbi Decoders for IEEE STD 802.16-2009.
Design Undertakings
The MSc undertaking has the following specific design undertakings:
1. Literature reappraisal on channel coding for following coevals broadband radio entree
systems, which will be one of the Chapters in your concluding MSc thesis.
2. Modeling the Convolutional Encoder of codification rate 1/2.
3. Modeling the Viterbi Decoder for Task 2.
4. Obtain a curve of BER ( Bit Error Rate ) vs Signal-to-Noise ( Eb/No ) for Tasks 2 & A ; 3
over an AWGN channel.
5. Modeling the Viterbi Decoder with configurable codification rates 1/2, 2/3, 3/4 and 5/6.
6. Modeling the Viterbi Decoder for Task 5.
7. Obtain curves of BER ( Bit Error Rate ) vs Signal-to-Noise ( Eb/No ) for Tasks 5 & A ; 6
over an AWGN channel.
Modeling Tools
Microsoft Visual C++ Express Edition/Matlab
Undertaking Labs:
The 4th floor computing machine lab, Tower edifice
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Formatting
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