(Chapter 14): Trellis based decoding of linear block codes
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Transcript of (Chapter 14): Trellis based decoding of linear block codes
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(Chapter 14): Trellis based decoding of linear block codes
• Previous discussion on trellis based decoding of convolutional codesapplies directly to any code with a (moderately complex) trellis
• Viterbi
• SOVA
• BCJR
• MAP
• Log-MAP
• Max-log-MAP etc.
• Irregular trellises: May be more difficult to design HW decoder
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Branch complexity• Let Ii=2 if there is an information symbol corresponding to
output bit i, Ii =1 otherwise.• Then the branch complexity is the number of one-symbol branches
= the number of additions in the Viterbi algorithm= Na = i=0...n-1 Ii 2i
• Let Ji=0 if there is only one branch entering each state at time i, Ji= 1 otherwise
• Then the number of comparisons in the Viterbi algorithm= Nc = i=0...n-1 Ji 2i+1 Example: (8,4) RM
Na = 44Nc = 11
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Trellis sectionalization• Sectionalization combines adjacent bits and provides a trellis with
fewer time instances, in order to simplify the decoders.
• Let ={t0, t1,…,t}, for n. Delete all state spaces (and their adjacent branches) at time instances not in , and connect every pair of states, one in tj and one in tj+1, iff there were a path between these states in the original trellis, label the new branches by the old path labels.
• A set of parallel paths forms a composite branch
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Sectionalization: Example
Three trellis sections
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Decoding of Sectionalized trellis
• For each composite branch,
• find the best single path among those that form the composite branch. Make a note of who is the winner.
• The branch metric of the composite branch is by definition equal to the branch metric of this winning branch.
• The metrics of the composite branch is added to the path metrics of the state it starts in.
• The rest proceeds as the ordinary Viterbi algorithm
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Complexity of sectionalized trellis decoding
• Depends on choice of section boundaries
• Optimal sectionalization: Minimum number of additions+comparisons (Lafourcade & Vardy)
(x,y)= number of computations to calculate section from time x to time y
min(x,y)= minimum number of computations to calculate section from time x to time y
min(0,y)=min{ (0,y) , min0<x<y{min(0,x) + (x,y)} }
• Algorithm:
• Calculate (x,y) for 0x<yn
• Use these values to calculate min(0,y) for successive values of y
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Example
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Recursive ML decoding• Sectionalized trellis
• Recursive combining of the path metrics
• Start with minimal trellis for an (n,k) block code
• Consider state sx at time x and state sy at time y
• L(sx,sy) is the set of parallel branches from sx to sy (forming one composite branch)
• L(sx =zero state ,sy =zero state ) is a linear block code Cx,y
• Each L(sx,sy) is a coset of Cx,y , that is a coset in px,y(C) /Cx,y
• The number of distinct such sets is
= 2k(px,y(C))-k(Cx,y)
• So each coset appears (Chapter 9)
= 2k - k(C0,x) - k(Cy,n) - k(px,y(C)) times as a composite path
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Composite Path Metric Table• For each distinct L(sx,sy), store in CPMTx,y
• Label: Best path within composite branch, l(L(sx,sy))
• Metric: The metric of the best path, m(L(sx,sy))
• Eventually, CPMT0,n will contain just one path
• Construct CPMTx,y by
1. For small y-x: Compute metrics for each path in composite path.
This requires 2k(Cx,y)(y-x-1) additions 2k(Cx,y)
-1 comparisons
= 2k(Cx,y)(y-x) – 1 operations.
2. For larger y-x : Recursively, i. e. from CPMTx,z and CPMTz,y
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Recursive MLD (continued)
• L(sx,sy) = sz L(sx,sz) L(sz,sy)
• m(L(sx,sy)) = maxsz { m( L(sx,sz)) + m( L(sz,sy)) }
• l(L(sx,sy)) = l( L(sx,s*z)) l( L(s*z,sy))
• This computation requires z additions and z -1 comparisons,
• where z = the number of states at time z, = 2k(Cx,y) - k(Cx,z) - k(Cz,y)
• Forming the CPMTx,y requires 2k(px,y(C))-k(Cx,y) (2z -1) operations
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Comparison
• Per L(sx,sy) :
• Direct: 2k(Cx,y)(y-x) – 1
• Recursively: (2z -1) = 2(2k(Cx,y) - k(Cx,z) - k(Cz,y)-1) – 1)
• Recursive computation significantly faster if k(Cx,z) and k(Cz,y) are large.
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Computation• Instead of forming the trellis section Tx,y from the complete trellis:
• Construct the special trellis T({x,z,y,nx,y}):
Each state at time x
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An RMLD-IV algorithm• Divide code into very short sections
• Apply MakeCPMT procedure to these.
• MakeCPMT-I: ”brute-force”
• Apply CombCPMT to the smaller sections
• CombCPMT-V: Use Viterbi for the comparisons
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Optimum Sectionalization
• Lafourcady-Vardy algorithm applied to RMLD
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Complexity comparisons
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Humble remark
• First such algorithm devised in [14,15]
• But...
• Similar algorithm for convolutional codes in
– Marianne Fjelltveit and Øyvind Ytrehus. On Viterbi decoding of high rate convolutional codes. In Abstracts of Papers of the 1994 IEEE International Symposium on Information Theory, Trondheim, 1994.
– Marianne Fjelltveit and Øyvind Ytrehus. Two-step trellis decoding of partial unit memory convolutional codes. IEEE Transactions on Information Theory, IT-43:324–330, January 1997.
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Suggested exercises
• 14.1-14.5