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Muhammad Shoaib Bin Altaf
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Outline
Motivation
Actual Flow
Optimizations
Approach
Results
Conclusion
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Motivation Convolutional coding with Viterbi decoding a
powerful
method for FEC in Communication Systems
Viterbi Algorithm is based on Maximum LikelihoodEstimation which
is sequential. Thus slow.
Modern Communications Standards like Wimaxsupport very high
throughput
Data speed is increasing so is the need for high speedViterbi
decoding
We are looking for such a scheme which givesvectorized output
bits
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Actual Algorithmic Flow
We have done this stuff in our Homework as well
On building trellis, at each stage path metric will be
computed
Best path metric computation at each stage
Traceback decoding done bit by bit
Each clock cycle, one bit will be decoded
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OptimizationVA is sequential but the Good thing is, its
Recursive
Various optimization possibilities can be employed for
speed-up. Since the purpose was to have vectorized output,
the
only viable option is Look Ahead Transformation
Discussed Look Ahead transformation for Hoffman
decoding in the class Block processing of the data
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Optimization Contd. Decoding using 2 Look Ahead step.
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Optimization Contd. Increasing the number of Look Ahead
steps
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Optimization Contd. Instead of 2 paths, we have to select the
minimumamong the 4 possible paths
Lookup table needs to be changed
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Approach Matlab Simulation
N=10^5 bits of data
Two implementations of VA Constraint Length K=3
One based on simple decoding
Other based on Look Ahead Transformation
Performance comparison to justify the correctness ofthe
suggested approach
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Results
Data processing speed nearly doubles on taking asingle Look
Ahead step.
Sequential VA Optimized VA
Execution time inSeconds
38.3294 20.0305
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Results Contd.. Performance Comaprsion
0 1 2 3 4 5 6 7 8 9 10
10-5
10-4
10-3
10-2
10-1
Eb/No, dB
BitErrorRate
BER comparision for different Viterbi decoding imlementations
for BPSK in AWGN
theory - uncoded
simulation - Viterbi sequential
simulation - Viterbi parallel
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Conclusion Look Ahead Transformation is very attractive for
increasing the throughput for Recursive Algorithms
No loss in decoding abilities Depending on the Application Look
Ahead step can be
increased to any value
The extra hardware cost is nominal as compared to the
achieved performance In this Project the main focus was on
speeding up the
decoding rate irrespective of the extra hardware
costincurred
