Channel Encoding & Decoding
Transcript of Channel Encoding & Decoding
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Channel Encoding & Decoding ViterbiViterbi Algorithm Algorithm
Stefan Kroll Martin SchoberChristoph Kaiser Peter MödlhammerRoland Ortner Hannes Graf
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Channel Encoding & Decoding
1. Digital Communication System 2. Viterbi Algorithm 3. Advantages of the Viterbi Algorithm 4. Presentation of a Demonstration
Software
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1. Digital communicationsystem
• Definition of Viterbi algorithm• Historical background of viterbi-coding• Parts of a digital communication system• Use of the Viterbi algorithm
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Digital communication system
• Problem of digital communitcation– Transmit much data via a noisy channel– Detect and correct errors
• Solution– Convolutional coding with– Forward error correction (FER)
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Viterbi Algorithm
The Viterbi-Algorithm is a basic part of thecoding and modulation method of the
digital data transmission.With the help of the Viterbi-algorithm it is
possible to recognise data errors andcorrect them at the receiver. [9]
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Viterbi Algorithm
"…efficient methode of optimum sequenceestimation of a finite-state process“.
„finding the shortest path through aweighted graph“
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Development
Elias P. introduced– Convolutional codes– Fixed decoding time– First real-time coding
< 1955< 1955
19551955
Channel Encoding was implemented by–Block codes–Linear block codes
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Development
Other researchers improved the channelcoding methodes– Found new convolutional codes– Improved the performance limits– Designed implementations in hard- & software
19671967
......
Andrew J. Viterbi–Founder of Qualcomm Corporation–Developed the Viterbi Algorithm
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Development
Viterbi & Reed-Solomon decoding– Used in space communication– geostationary satellite communication
19931993
19951995
Turbo coding merged–Parallel concatenated convolutional technique–Improves performence by chaining up:Viterbi decoder and Reed-Solomon decoder(data recycle through the decoder several times)
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
51. Information source•Speech of a person •Binary data of a machine e.g. computer=> Analog wave / sequence of discrete symbols
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
52. Source encoder (A/D converter) •Transformation of analogue signal into digital signal•Compression of signal=> Information sequence
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
53. Channel encoder•Transformation of information sequence into encoded sequence•Adding of redundant information for error recognition and correction=> Code word
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
54. Modulator•Modulation of a high frequency carrierby code word e.g. QPSK
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
55. ChannelTransmission via channel; Influence by noise• Non-linearity of system• Interferences in the atmosphere: rain, clouds, other radio waves• Reflections
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
56. Demodulator•Detection of the phase•Viterbi-decoding in real-time=> Received sequence
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
57. Channel decoder•Transformation of received sequence into a=> Binary sequence
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Information source
Source encoder
Channel encoder
Modulator
Destination Source decoder
Channel decoder
Demodulator
Noise
Digital communication system
1 2 3 4
9 8 7 6
58. Source decoder (D/A converter)Transformation into a continous waveform=> Analogue signal (speech)
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Channel coding: Code Types
Code Types:– Block coding– Convolutional coding
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1. Block coding
• Input:– Large message blocks with CRC
• Output:– Block code with different lenght and breaks
Block
CRCChecksum
t
ChannelDecoderChannelDecoder
Input Output
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1. Block coding
Example of Block Coding:• Reed-Solomon Block:
• Recognition and correction of 8 error-bytes
188 bytes 16 byte
Viterbi-coded block ++ CRC-block
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2. Convolutional Coding
• Input:– Serial data stream with includes redundant
information• Output:
– Decoded binary data stream
ChannelDecoderChannelDecoder
Viterbi Code Binary sequence
OutputInput
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Use of the Viterbi Algorithm
• Low Signal to noise ratio ratio
• Radio link• Satellite connection• Digital TV (MPEG-2/DVB)• Geostationary satellite networks (VSAT)• Speech recognition• Magnetic recoring• Direct broadcast satellite systems (DBS)
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2. Description of theAlgorithmus
• Generation of the data• Convolutional encoder• Parameters• Channel symbols mapping• Noise adding• Channel symbol quantizing• Viterbi Decoding
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Generation of the data
• needed for simulation• eg. using a random number genarator
– eg. rand (); in C– value less than half of the maximum value is
a zero– any value grater or equal to half of the
maximum value is a one
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Convolutional encoder
Output
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Convolutional encoder
State diagram Encoder2.
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Parameters
• eg. k=3– represent the code generator polynomials
• eg. m=2– number of shift-register
• eg.R=1/2– code rate (one inputbit two outputbits)
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Channel symbols mapping
• Is simply a matter of translating:
• zeros to +1• ones to –1
– method is called living zero
This can be accomplished by performing the operation: y = 1-2x
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Noise adding
• generating Gaussian random numbers• adding the scaled Gaussian random
numbers to the channel symbols values
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Channel symbol quantizing
• Hard-decision• Soft-decision
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Hard-descision
• quantized to one-bit precision
• < 0V = 1• > 0V = 0
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Soft-decision
• quantized with more than one bit ofprecision
• three or four bits of precision canperform about 2 dB better than hard-decision
• the usual quantization precision is threebits
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3 Bit Quantizer2.
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Viterbi Decoding
• Diagrams help to understand thealgorithm
– State diagram– Trellis diagram
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State diagram / Trellis diagram
State diagram Trellis diagram2.
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Viterbi decoding 12.
2.
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Viterbi decoding 22.
2.
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Trellis diagram
11111
00111
11001
00001
11110
00110
11000
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sended bitnextcurrent
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3. Advantages of theViterbi Algorithm
• Self-Correction of the Code• Minimization of the Transmitting Energy• Minimization of the Bandwidth• Other Advantages
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Adva
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Self-Correction of the Code
• very good ability to correct wrongtransmitted bits ⇒ forward errorcorrection
• done by adding of a redundantinformation
• state diagram offers a completedescription of the system
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Self-Correction of the Code
• State diagram
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Minimization of theTransmitting Energy
• BER
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Minimization of theTransmitting Energy
• good designed satellite transmissions:BER< 10-6
• professional transmissions: BER < 10-10
• transmitting power: 4 Watt
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Minimization of the Bandwidth
• possible to reconstruct lost data ⇒ usedto save bandwidth
• more bandwidth needed, because of allthe redundant information
• rate 1/2 ⇒ double of the bandwidth• don’t send every third bit ⇒ able to
reconstruct the dropped data
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Minimization of the Bandwidth
• called puncturing of the code• disadvantage: the transmitting energy
have to been increased
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Adva
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Other Advantages
• fixed decoding time• high transfer rate, up to 2 Mbps• implementation in hardware and software
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4. Presentation of the Software
• task:– constraint length: 7– code rate: 1/2– soft decision: 3 bit quantizer
4. P
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Pre
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Softw
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Viterbi Algorithm
Thank You for Your Attention!