low complexity algebraic multicast network codes sidharth “sid” jaggi philip chou kamal jain
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Low Complexity Algebraic Multicast Network Codes
Sidharth “Sid” Jaggi
Philip Chou
Kamal Jain
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The Multicast Problem Model
Network with directed edges, nodes.
Single source, rate R.|T| sinks, desired rate R, identical
information.
Examples –1. Online news broadcasts.2. Online gaming.
s
t1
t2
t|T|
Network
.
.
.
R
R
R
R
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History-I AssumptionsAcyclic graph.Each link has unit capacity.Links have zero delay.Arithmetic operations allowed
at all nodes.
Upper bound for multicast capacity C,
C ≤ min{Ci}
s
t1
t2
t|T|
C|T|
C1
C2
Network
||,...,2,1,)(maxmin)( ||
TiCcutsize iflowtscut T
.
.
.
Multicast capacity C achievable! (Random coding argument, [ACLY 2000])
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Example
s
t1 t2
b1 b2
b2
b2
b1
b1
(b1,b2)
b1+b2
b1+b2b1+b2
(b1,b2)Example due to [ACLY2000]
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History-II
)2(1,0)...( 21mm
m Fbbb
2
k
b1b2 bm
1
kk ...2211
β1
β2
βk
F(2m)-linear network can achieve multicast capacity
C!
F(2m)-linear network[KM2001]
Source:- Group together `m’ bits,
Any node:- Perform linear combinations over finite field F(2m)
Local Coding Vector: [β1 β2... βk]
2m>|T|C, Computational complexity high.
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Results (Ours and Others’)
Importance Linear encoding/decoding. [SET2003] Capacity gap without coding arbitrarily large. Lower bound on field size 2m ..
[LL(preprint)] Lower bound on alphabet size ; finding smallest alphabet NP-hard. Multicast the only “easy and interesting” case.
Can be implemented as binary block-linear codes. Randomized construction (Also [SET2003],[HMKKE2003])
Faster design, More robust (Single code, zero error, small rate loss, arbitrary failure
pattern).
||T
Main Result (Also [SET2003]): For 2m ≥ |T|, exists an F(2m)-linear network which can be designed in polynomial time.
||T
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Example: Local Coding Vectors
s
b1
b2b1
b1b1+b2
Local Coding Vector: [1]
Local Coding Vector: [1 1]
length = number of incoming edges(variable)
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Idea: Global Coding Vectors
s
b1
b1+b2
Global Coding Vector: [1 0]Global Coding Vector: [0 1]
Information carried by edge↔(g.c.v.)edge.[s1s2...sC]T
Global Coding Vector: [1 1]
length = capacity(fixed)
b2
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Idea: Linear IndependenceTASK: Find local coding vectors so that each receiver can decode.
METHOD: Find local coding vectors sequentially so that global coding vectors on every cut-set to every receiver are linearly independent.
s
t1 t2
PREPROCESSING:Find a set of C paths froms to each ti.
b2b1
b1+b2b1+b2 T2 = [1 1] [0 1]
T1 = [1 0] [1 1]
OUTPUT: Local coding vectors,Final global coding vectors.
Decoding: [x1...xk]=[Ti]-1[y1...yk]T
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One edge at a time…
e1 e2
e4
e5
e3M1({1,4}) = [1 0]
[0 1]
b2b1
[1 0] [0 1]
[1 1]
M2({3,2}) = [1 0][0 1]
M2({2,5}) = [0 1][1 1]
M1({1,5}) = [1 0][1 1]
Design g.c.v. for edge 5
b1+b2
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Choosing local coding vectors appropriately
Let v1,…,vk be global coding vectors feeding into e.Let Mi(n) be matrices of global coding vectorson nth cutset to ti. Inductive hypothesis – each Mi(n) has rank C
e
v1
v2
vkLet L = span {v1,…,vk},
L
Sj = span{rows(Mj(n))- vj}
Then we wish to find v in L such that
v not in Sj for all Sj (and therefore rank of each Mi is still C)
v1
vk
CCCC
jCjj
C
vvv
vvv
vvv
...
:
...
:
...
21
21
11211
V
Sk
S1
L
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Cool Lemma
Lemma:
)(||1
j
k
jSLqT
)),((1
j
k
jSLL
,|| )(LrankqL
)(1)(
1
1)(
1)1(||||||
,||
LrankLrankj
k
j
Lrankj
qqTSLqT
qSL
0|))((|1
j
k
jSLL
VS1
L
Proof: Consider
V
L
1SLkSL
Hence Proved.(Quick deterministic algorithm,Faster randomized algorithms.)
Sk
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Practical Optimal Network Codes.
Multicast the only “easy and interesting” case for network coding problems.
Capacity achieving codes. Linear encoding/decoding. Small field sizes.
At most quadratic gap. Smallest field-size determination hard.
Polynomial design complexity. (Random code design) Robustness. (Random code design)
Joint paper being prepared for submission to IT Trans.: Jaggi, Sanders, Chou, Effros, Egner, Jain, Tolhuizen
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