Network Coding Theory:Network Coding Theory:Consolidation and ExtensionsConsolidation and Extensions

Raymond Yeung

Joint work with Bob Li, Ning Cai and Zhen Zhan

Outline

Single-Source Network Coding Global and Local Descriptions of a Network

Code Linear Multicast, Broadcast, and Dispersion Static codes

Multi-Source Network Coding Fundamental Limits of Linear Codes

Based on an upcoming paper to appear in Foundation and Trends in Communications and Information Theory (Editor: Sergio Verdu).

Single-Source Network Coding

Network is acyclic. The message x, a -dimensional row vector

in F, is generated at the source node. A symbol in F can be sent on each channel.

Global Description

The symbol sent on channel e is a function of the message, called the global encoding mapping for channel e.

For any node v, the global encoding mappings have to satisfy the local constraints, i.e., the local encoding mapping for every node v is well defined.

A Globally Linear Network Code

A code is globally linear if all the global encoding mappings are linear (and all the local constraints satisfied).

A globally linear code is the most general linear code that can possibly be defined.

The global encoding mapping for channel e is characterized by a column vector fe, s.t. the symbol sent on e is x fe.

It can be proved that if a code is globally linear, then it is also locally linearly, i.e., all local encoding mappings are linear.

Global Description vs Local Description

Since the local encoding mapping at a node v is linear, it follows that

for any e Out(v), fe is a linear combination of fe’, e’ In(v).

Global description (Li-Yeung-Cai). These linear combination forms the local

encoding kernel.

Local description (Koetter-Medard)

Global Description = Local Description

The global description and the local description are the two sides of a coin: They are equivalent. Both can describe the most general form of a

(block) linear network code!

Generic Network Code

Definition (LYC)

A linear network code is said to be generic if:

For every set of channels {e1, e2, … , en}, where n and ej Out(vj), the vectors fe1, fe2, … , fen are linearly independent provided that

{fd: d In(vj)} {fek: k j} for 1 j n.

The idea: Whenever a collection of vectors can possibly be linear independent, they are.

Special Cases of a Generic Network Code

Generic network code

Linear dispersion

Linear Broadcast

Linear Multicast

Each notion is strictly weaker than the previous notion!

Linear Multicast

For each node v, if maxflow(v) , then the message x can be recovered.

Linear Broadcast

For every node v, If maxflow(v) , the message x can be

received. If maxflow(v) < , maxflow(v) dimensions of

the message x can be recovered. Linear Broadcast Linear Multicast

Linear Dispersion

For every collection of nodes P, If maxflow(P) , the message x can be received. If maxflow(P) < , maxflow(P) dimensions of the

message x can be recovered. Linear Dispersion Linear Broadcast

Linear Mulicast (Generic network code implies all) For a linear dispersion, a new comer who wants to

receive the message x can do so by accessing a collection of nodes P such that maxflow(P) , where each individual node u in P may have maxflow(u) < .

Code Constructions

A generic network code exists for all sufficiently large F and can be constructed by the LYC algorithm.

A linear dispersion, a linear broadcast, and a linear multicast can potentially be constructed with decreasing complexity since they satisfy a set of properties of decreasing strength.

In particular, a polynomial time algorithm for constructing a linear multicast has been reported independently by Sanders et al. and Jaggi et al.

Static Codes

Static linear multicast was introduced by KM which finds applications in robust network multicast.

Static versions of linear broadcast and linear dispersion can be defined accordingly.

The LYC algorithm can be modified for constructing a static generic network code.

This means that the static versions of a linear dispersion, a linear broadcast, and a linear multicast can all be constructed.

Multi-Source Network Coding

A network is given. Independent information sources of rates

= (1, 2, …, S)

are generated at possibly different nodes, and each source is to be multicast to a specific sets of nodes.

The set of all achievable rates is called the achievable information rate region R.

If all the sources are multicast to the same set of nodes, then it reduces to a single-source network coding problem, otherwise it does not.

A multi-source network coding problem cannot be decomposed into single-source network coding problems even when all the information sources are generated at the same node (Yeung 95).

Special multi-source network coding problems have been shown to be decomposable (Roche, Hau, Yeung, Zhang 95-99).

An Example of Indecomposability(with Wireless Application)

Independent sources need to be coded jointly

b1 b2

b1 b2

b1+b2

b1 b2

Characterization of the Information Rate region R Inner and outer bounds on R acyclic

networks can be expressed in term of the region of all entropy functions of random variables (Yeung 97, Yeung-Zhang 99, Song et al. 03).

A computable outer bound on R, called RLP, has also been obtained.

Only existence proofs by random coding are available no code construction.

The region Γ*

Let Γ* be the set of all entropy functions of a collection of random variables labeled by the information sources and the channels.

Outer Bound Rout

If an information rate tuple is achievable, then there exists h closure(Γ*) which satisfies a set of constraints denoted by C which specifies

1. the independence of the information sources

2. the rate tuple

3. local constraints of the code

4. the channel capacity constraints

5. the multicast requirements.

C is a collection of hyperplanes in the Eucledian space.

Linear Codes for Multiple Sources

The global description for a linear network code can be generalized to multiple sources.

Each channel is characterized by a column vector of an appropriate dimension.

The existence of a linear code is nothing but the existence of a collections of vectors satisfying the set of constraints C.

The Region *

Let * be the set of all rank functions for a collection of -dimensional column vectors labeled by the information sources and the channels over some finite field F, where

1.

Linear Codes vs Nonlinear Codes

Linear codes Rlinear

An information rate tuple is linearly achievable iff there exists h closure(*) which satisfies the set of constraints C.

Note: Rlinear includes all rate tuples that are inferior to some rate tuples achievable by mixing linear codes.

Nonlinear codes outer bound Rout

If an information rate tuple is achievable, then there exists h closure(Γ*) which satisfies the set of constraints C.

Similarity between Rank and Entropy

The rank function satisfies 1. 0 rank(A).2. rank(A) rank(B) if A B.3. rank(A) + rank(B) rank(AB) + rank(AB).4. rank(A) |A|.

The entropy function in general satisfies1. 0 H(A).2. H(A) H (B) if A B.3. H(A) + H (B) H (AB) + H (AB).

1 - 3 are called the polymatroidal axioms.

The Bridge from Rank to Entropy

Theorem 1:

Let F be a finite field, Y be an -dimensional random row vector that distributes uniformly on F, and A be an l matrix. Let Z = Y·A. Then H(Z) = rank(A) log |F|.

Using this theorem, it can be shown that

* Γ*.

A Gap between * and Γ*

In addition to the polymatroidal axioms, the rank function also satisfies the Ingleton inequality:

r(A13)+ r(A14)+ r(A23)+ r(A24)+ r(A34)

r(A3)+ r(A4)+ r(A12)+ r(A134)+ r(A234) The Ingleton inequality is satisfied by algebraic

structures as general as Abelian groups. The corresponding inequality is not satisfied by the

entropy function (Zhang-Yeung 99), so there is a gap between * and Γ*.

This gap between * and Γ* suggests that nonlinear codes may actually perform better for some multi-source problems.

Vector Linear Codes

Vector Linear Codes (Riis, Lehman2, Medard, Effros, Ho, Karger, Koetter) It can be regarded as a linear code over a network

obtained by expanding all the capacities by an integer factor.

It has been shown that some multi-source problems do not have linear solutions but have vector linear solutions.

Question 1: Are these vector linear solutions better than all mixtures of linear solutions?Question 2: Do these vector linear solutions exceed the Ingleton inequality? (If so, the answer to Q1 is yes.)

Codes Beyond Fields

Dougherty, Frieling and Zeger have recently shown that there exist a multi-source problem that has no linear solution even in the more general algebraic context of modules, which includes all finite rings and Abelian groups.

Question 1: Is the nonlinear solution given by DFZ better than all mixtures of linear solutions?Question 2: Does the nonlinear solution given by DFZ exceed the Ingleton inequality? (If so, the answer to Q1 is yes.)

Ingleton Inequality Classification

Codes abide by the Ingleton inequality Linear codes, module codes

Codes not necessarily abide by the Ingleton inequality Vector linear codes (abide by the Ingleton

inequality in an extended space) Codes not abide by the Ingleton inequality

Non-Abelian group codes are asymptotically as good as all nonlinear codes (Chan, submitted to ISIT 2005).

Thank You