1. Zero-error source-channel coding with source side information at the decoder J. Nayak, E. Tuncel and K. Rose University of California, Santa Barbara

2. Outline The problem Asymptotically vanishing error case Zero error Unrestricted Input Restricted Input How large are the gains? Conclusions

3. The Problem Is separate source and channel coding optimal? S-C Encoder S-C Decoder Û V U X Channel p(y|x) Y n nn n n  sc Channel Encoder Channel Decoder ÛU X Channel p(y|x) Y n n n n  c c Source Encoder Source Decoder V n s   s i î Does an encoder-decoder pair exist?

4. Asymptotically Vanishing Probability of Error Source coding: R>H(U|V) Slepian-Wolf code Channel coding: R<C Source-channel code (Shamai et. al.) Communication not possible if H(U|V)>C Separate source and channel coding asymptotically optimal

5. Channel Characteristic graph of the channel Examples Noiseless channel: Edge free graph Conventional channel: Complete graph Channel transition probability p(y|x), y  Y, x  X

6. Channel Code Code = symbols from an independent set 1-use capacity = log 2  (G x ) n uses of the channel Graph = G X n, n-fold AND product of G X Zero error capacity of a graph Depends only on characteristic graph G X

7. Source With Side Information (U,V)  U x V ~ p(u,v) Support set S UV = {(u,v)  U x V : p(u,v)>0} Confusability graph on U : G U =( U,E U ) Examples U=V : Edge free graph U,V independent: Complete graph

8. Source Code Rate depends only on G U Connected nodes cannot receive same codeword  Encoding=Coloring G U Rate = log 2  (G U ) Two cases Unrestricted inputs Restricted inputs

9. Unrestricted Input (u,v) not necessarily in S UV Decode correctly if (u,v)  S UV 1-instance rate: log 2  (G U ) n-instance graph Graph = G u (n), n-fold OR product of G u Asymptotic rate for UI code

10. Restricted Input (u,v) in S UV 1-instance rate: log 2 [  (G U )] n-instance graph Graph = G u n, n-fold AND product of G u Asymptotic rate = Witsenhausen rate of source graph

11. Source-Channel Coding 1 source instance -1 channel use code Encoder Decoder u 1 and u 2 are not distinguishable given side information   sc 1 (u 1 ) and  sc 1 (u 2 ) should not result in same output y u 1 and u 2 connected in G U   sc 1 (u 1 ) and  sc 1 (u 2 ) are not connected in G X and  sc 1 (u 2 )   sc 1 (u 1 )

12. Source-Channel Coding If n-n UI (RI) code exists for some n, ( G U,G X ) is a UI (RI) compatible pair ( G, G ) is always a UI and RI compatible pair

13. Unrestricted Input Source a b cd e Channel E A B C D C(G X5 ) = log 2 [  5 ] R UI (G U5 ) = log 2 [ 5/2 ]> C(G X5 ) Source = Complement of channel abcde ADB EC A B C D E =

14. Restricted Input Previous example not useful R W (G U5 ) = log 2 [  5 ] = C(G X5 ) Source graph G U = complement of channel graph G X Approach: Find f(G) such that C(G)  f(G)  R W (G) If either inequality strict, done!

15. Lovász theta function: Lovász: Key result:

16. Restricted Input G U = Schläfli graph ( 27 vertex graph) = G X Haemers Code exists since G U = G X

17. How large are the gains? Channel uses per source symbol Alon ‘90: There exist graphs such that C(G) < log k and Given l, there exist G such that

18. Conclusions Under a zero error constraint separate source and channel coding is asymptotically sub-optimal. Not so for the asymptotically vanishing error case. In the zero-error case, the gains by joint coding can be arbitrarily large.

19. Scalar Code Design Complexity Instance: Source graph G Question: Does a scalar source-channel code exist from G to channel H? Equivalent to graph homomorphism problem from G into H NP-complete for all H (Hell & Nesetril ’90)

20. Future Work Do UI compatible pairs ( G U,G X ) exist with R W (G U ) < C(G X ) < R UI (G U ) ? For what classes of graphs is separate coding optimal?