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Network Coding: Mixin’ it up
Sidharth Jaggi
Michelle Effros
Michael Langberg
Tracey Ho
Philip Chou
Kamal Jain
Muriel Médard Peter Sanders
Ludo Tolhuizen
Sebastian Egner
Network CodingR. Ahlswede, N. Cai, S.-Y. R. Li and R. W. Yeung,"Network information flow," IEEE Trans. on Information
Theory, vol. 46, pp. 1204-1216, 2000.
http://tesla.csl.uiuc.edu/~koetter/NWC/Bibliography.html 131 papers as of last night(≈2 years)
NetCod Workshop, DIMACS working group, ISIT 2005 - 4+ sessions
Several patents, theses
“The core notion of network coding is to allow and encourage mixing of data at intermediate network nodes. “
(Network Coding homepage)
But . . . what is it?
Point-to-point flows
)(maxmin)(
cutsizeCflowtscut →
=
C
1P
2P
CP
Min-cut Max-flow (Menger’s) Theorem [M27]
Ford-Fulkerson Algorithm [FF62]
s
t
Justifications revisited - I
s
t1 t2
b1 b2
b2
b2
b1
b1 ?b1
b1 b1
b1 (b1,b2)
b1+b2
b1+b2b1+b2
(b1,b2)[ACLY00]
Throughput
Gap Without Coding
. . .
. . .
h2
( )hh2
Coding capacity = h Routing capacity≤2
Example due to Sanders et al. (collaborators)
s
Multicasting
Upper bound for multicast capacity C,
C ≤ min{Ci}
s
t1
t2
t|T|
C|T|
C1
C2
Network
[ACLY00] - achievable!
[LYC02] - linear codes suffice!!
[KM01] - “finite field” linear codes suffice!!!
Multicasting
{ } )2(1,0)...( 21mm
m Fbbb ∈→∈ α
2α
kα
b1b2 bmα
1α
kkαβαβαβ +++ ...2211
β1
β2
βk
F(2m)-linear network[KM01]
Source:- Group together `m’ bits,
Every node:- Perform linear combinations over finite field F(2m)
Multicasting
Upper bound for multicast capacity C,
C ≤ min{Ci}
s
t1
t2
t|T|
C|T|
C1
C2
Network
[ACLY00] - achievable!
[LYC02] - linear codes suffice!!
[KM01] - “finite field” linear codes suffice!!!
[JCJ03],[SET03] - polynomial time code design!!!!
Thms: Deterministic Codes
For m ≥ log(|T|), exists an F(2m)-linear network which can be designed in O(|E||T|C(C+|T|)) time.
[JCJ03],[SET03]
Exist networks for which minimum m≈0.5(log(|T|))
[JCJ03],[LL03]
Justifications revisited - II
s
t1 t2
b1 b2
b2
b2
b1
b1
(b1,b2)
b1+b2
Robustness/Distributeddesign
(b1,b2)
b1+2b2
(Finite field arithmetic)b1+b2 b1+b2
b1+2b2
Thm: Random Robust Codes
s
t1
t2
t|T|
C|T|'
C1'
C2'
Faulty Network
C' = min{Ci'}
If value of C' known to s,same code can achieve C' rate!
(interior nodes oblivious)
Thm: Random Robust Codesm sufficiently large, rate R<C
Choose random [ß] at each node
Probability over [ß] thatcode works
>1-|E||T|2-m(C-R)+|V|
[JCJ03] [HKMKE03]
(different notions of linearity)
Decentralized design
b1b2 bm
b’1b’2 b’m
b’’1b’’2 b’’m
’
’’
Much “sparser” linear operations
(O(m) instead of O(m2)) [JCE06?]
Vs. prob of error - necessary evil?
Zero-error Decentralized CodesNo a priori network topological
information available - informationcan only be percolated down links
Desired - zero-error code design
One additional resource - eachnode vi has a unique ID number i(GPS coordinates/IP address/…)
Need to use yet other types of linear codes[JHE06?]
Inter-relationships between notions of linearity
C
B
M
M Multicast G General
Global Local I/O ≠ Local I/O =
a Acyclic
A AlgebraicB BlockC Convolutional
Does not exist
Є epsilon rate loss
G
a
GЄ
A Ma
Ma
Ma
G?
M
G
a
G
Ma G
G
[JEHM04]
Justifications revisited - III
s
t1 t2
Security
Evil adversary hiding in networkeavesdropping,
injecting false information[JLHE05]
Unicast
1. Code (X,Y,Z)2. Message (X,Z)3. Bad links (Z)4. Coin (X)5. Transmission (Y,Z)6. Decode correctly (Y)
Eureka
Xavier Yvonne
?
Zorba
??
|E| directed unit-capacity links
Zorba (hidden to Xavier/Yvonne) controls |Z| links Z. p = |Z|/|E|Xavier and Yvonne share no resources (private key, randomness)Zorba computationally unbounded; Xavier and Yvonne can only
perform “simple” computations.
Unicast
Zorba knows protocols and already knows almost all of Xavier’s message (except Xavier’s private coin tosses)
Goal: Transmit at “high” rate and w.h.p. decode correctly
Background
Noisy channel models (Shannon,…)Binary Symmetric Channel
p (“Noise parameter”)0
1
1
C
(C
apac
ity)
0 1
H(p)
0.5
Background
Noisy channel models (Shannon,…) Binary Symmetric Channel Binary Erasure Channel
p (“Noise parameter”)0
1
1
C
(C
apac
ity)
0 E
1-p
0.5
Background
Adversarial channel models “Limited-flip” adversary (Hamming,Gilbert-Varshanov,McEliece et al…) Shared randomness, private key, computationally
bounded adversary…
p (“Noise parameter”)0
1
1
C
(C
apac
ity)
0 1
0.5
p (“Noise parameter”)
0
1
1
C
(C
apac
ity)
Unicast - Results
0.5
0.5(Just for this talk, Zorba is causal)
p = |Z|/h
0
1
1
C
(N
orm
aliz
ed b
y h)
General Multicast Networks
0.5
0.5
h
ZS
R1
R|T|
Slightly more intricate proof
|E|-|Z| |E|
MDSCode
X|E|-|Z|
Block-length n over finite field Fq
|E|-
|Z|
n(1-ε)
x1…
n
Vandermonde matrix
T|E|
|E|
n(1-ε)
T1
. . .
n
Rate fudge-factor “Easy to use consistency information”
nεSymbol from Fq
Unicast - Encoding
… T|E|
… T1
. . .
r
r
nε
D1…D|E|
D1…D|E|
Di=Ti(1).1+Ti(2).r+…+Ti(n(1- ε)).rn(1- ε)
Ti
r Di
i
Unicast - Encoding
… T|E|
… T1
. . .
r
r
D1…D|E|
D1…D|E| … T|E|’
… T1’
. . .
r’
r’
D1’…D|E|’
D1’…D|E|’
Unicast - Transmission
Di=Ti(1)’.1+Ti(2)’.r+…+Ti(n(1- ε))’.rn(1- ε) ? If so, accept Ti, else reject Ti
Unicast - Quick Decoding
… T|E|’
… T1’
. . .
r
r’
D1…D|E|
D1’…D|E|’Choose majority (r,D1,…,D|E|)
∑k(Ti(k)-Ti(k)’).rk=0
Polynomial in r of degree n over Fq,value of r unknown to ZorbaProbability of error < n/q<<1
Use accepted Tis to decode
p = |Z|/h
0
1
1
C
(N
orm
aliz
ed b
y h)
0.5
0.5
General Multicast Networks
R1
R|T|
S
S’|Z|
S’2
S’1
Observation: Can treatadversaries as new sources
R1
R|T|
S
General Multicast Networksyi=Tix
x
y1’
S’|Z|
S’2
S’1 a1
yi’=Tix+Ti’ai
(x(1),x(2),…,x(n)) form a R-dimensionalsubspace X
w.h.p. over network code design,TX and TAi do not intersect (robust codes…).
(ai(1),ai(2),…,ai(n)) form a |Z|-dimensionalsubspace Ai
w.h.p. over x(i), (y(1),y(2),…y(R+|Z|)) forms a basis for TXTAi
But already know basis for TX,therefore can obtain basis for TAi
Variations – Omniscient but not Omnipresent
C
p
0
1
10.5
Achievability: Gilbert-Varshamov, Algebraic Geometry Codes
Converse: Generalized MRRW bound
p (“Noise parameter”)
0
1
1
C
(C
apac
ity)
Ignorant Zorba - Results
0.5
0.5
1
Xp+Xs
Xs
1-2p
a+b+c
a+2b+4c
a+3b+9c
MDS code
Overview of results
Centralized design Deterministic
Decentralized design Randomized Deterministic
Complexity Lower bounds Sparse codes
Types of linearity - interrelationships Adversaries