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Network Coding and Reliable Communications Group
Algebraic Network Coding Approach to Deterministic Wireless Relay Networks
MinJi Kim, Muriel Médard
Network Coding and Reliable Communications Group
Wireless Network• Open problem: capacity & code construction for wireless relay networks
– Channel noise– Interference
• High SNR– Noise → 0 & large gain– Large transmit power
• High SNR rate region:– TDM, Noise free additive channel [Ray et al. ‘03]– Note: This holds for higher field size (not just binary) [Ray et al. ‘03]
• [Avestimehr et al. ‘07]“Deterministic model” (ADT model)– Interference – Model noise deterministically– Use binary field– In essence, high SNR regime
Model as error free
links
R1
R2
Y(e1)
Y(e2)e1
e2
e3Y(e3)
Y(e3) = Y(e1) + Y(e2)
R1
R2
log(1+P2/N)
log(1+P1/N)
log(1+P2/(P1+N))
log(1+P1/(P2+N))
R1
R2
High SNR
Network Coding and Reliable Communications Group
ADT Network Background• Min-cut: minimal rank of an incidence matrix of a certain cut
between the source and destination [Avestimehr et al. ‘07]– Requires optimization over a large set of matrices– Min-cut Max-flow Theorem holds for unicast/multicast sessions
• Matroidal [Goemans et al. ’09]– Algebraic Network Coding is also Matroidal [Dougherty et al. ’07]
• Unicast code construction algorithms [Goemans et al. ‘09][Yazdi & Savari ‘09][Amaudruz & Fragouli ‘09]
• Multicast code construction algorithms [Erez et al. ‘10][Ebrahimi & Fragouli ‘10]
Network Coding and Reliable Communications Group
Our Contributions• Connection to Algebraic Network Coding [Koetter & Médard ‘03]:
– Use of higher field size [Ray et al. ’03]1.
2. Can’t achieve capacity for multicast with justbinary field [Feder et al. ’03][Rasala-Lehman & Lehman ’04][Fragouli et al. ‘04]
3. [Jaggi et al. ‘06] “permute-and-add”:Show that network codes in higher field size Fq can be converted to binary-vector code in (F2)n without loss in performance
R1
R2
log(1+P2/N)
log(1+P1/N)
log(1+P2/(P1+N))
log(1+P1/(P2+N))
R1
R2
High SNR
Network Coding and Reliable Communications Group
Our Contributions• Connection to Algebraic Network Coding [Koetter & Médard ‘03]:
– Use of higher field size [Ray et al. ’03]– Model broadcast constraint with hyper-edges– Capture ADT network problem with a single system matrix M
• Prove that min-cut of ADT networks = max rank(M)• Prove Min-cut Max-flow for unicast/multicast holds• Extend optimality of linear operations to certain non-multicast sessions• Incorporate failures and erasures [Lun et al. ‘04]• Incorporate cycles
– Show that random linear network coding achieves capacity [Ho et al. ‘03]
– Do not address ADT network model’s ability to approximate the wireless networks; but show that ADT network problems can be captured by the algebraic network coding framework
Network Coding and Reliable Communications Group
ADT Network Model• Original ADT model (Binary field)
– Broadcast: multiple edges (bit pipes) from the same node– Interference: additive MAC over binary field
Higher SNR: S-V1
Higher SNR: S-V2
broadcast
interference• Algebraic model:
Network Coding and Reliable Communications Group
Algebraic Framework
• Assume higher field size• X(S, i): source process i• Y(e): process at port e• Z(T, i): destination process i• Assume linear operations
– at the source S: α(i, ej)
– at the nodes V: β(ej, ej’)
– at the destination T: ε(ej, (T, i))
Network Coding and Reliable Communications Group
System Matrix M= A(I – F )-1BT
• Linear operations
– Encoding at the source S: α(i, ej)
– Decoding at the destination T: ε(ej, (T, i))
e1
e2
e3
e4
e5
e6
e7
e8
e9
e10
e11
e12
a
b
c
df
Network Coding and Reliable Communications Group
System Matrix M= A(I – F )-1BT
• Linear operations – Coding at the nodes V: β(ej, ej’)
– F represents physical structure of the ADT network– Fk: non-zero entry = path of length k between nodes exists– (I-F)-1 = I + F + F2 + F3 + … : connectivity of the network
(impulse response of the network)
e1
e2
e3
e4
e5
e6
e7
e8
e9
e10
e11
e12
a
b
c
df
F =
Broadcast constraint (hyperedge)
MAC constraint(addition)
Internal operations(network code)
Linear code with some coefficients fixed by the network!
Network Coding and Reliable Communications Group
System Matrix M = A(I – F )-1BT
Z = X(S) M
e1
e2
e3
e4
e5
e6
e7
e8
e9
e10
e11
e12
a
b
c
df
• Input-output relationship of the network
Captures rate
Captures network code, topology(Field size as well)
Network Coding and Reliable Communications Group
Theorem: Min-cut of ADT Networks
• From [Avestimehr et al. ‘07]– Requires optimizing over ALL
cuts between S and T– Not constructive: assumes infinite
block length, internal node operations not considered
• Show that the rank of M is equivalent– System matrix captures the structure of the network– Constructive: the assignment of variables gives a network code
e1
e2
e3
e4
e5
e6
e7
e8
e9
e10
e11
e12
a
b
c
df
Network Coding and Reliable Communications Group
Min-cut Max-flow Theorem• For a unicast/multicast connection from source S to destination T, the
following are equivalent:1. A unicast/multicast connection of rate R is feasible2. mincut(S,Ti) ≥ R for all destinations Ti
3. There exists an assignment of variables such that M is invertible• Proof idea:
1. & 2. equivalent by previous work3.→1. If M is invertible, then connection has been established1.→3. If connection established, M = I. Therefore, M is invertible
• Alternate proof of sufficiency of linear operations for multicast in ADT networks [Avestimehr et al. ‘07]
Network Coding and Reliable Communications Group
Corollaries• Extend Min-cut Max-flow theorem to other connections:
– [Multiple multicast]: Sources S1 S2 … Sk wants to transmit to all destinations T1 T2… TN
– [Disjoint multicast]:– [Two-level multicast]: Two sets of destinations, a set Tm for multicast connection, another set Td for
disjoint multicast connection.
S2
T1
TN
Network
S1
Sk
S
T1
T3
Network
a, b, c, d
T2
a
b, c
d
Destination wants
S
T1
T3
a, b, c, d
a, b, c, d
a, b, c, d
Destination wants
T4
T6
T5
a
b, c
d
Network
Network Coding and Reliable Communications Group
Corollaries• Extend Min-cut Max-flow theorem to other connections:
– [Multiple multicast]: Sources S1 S2 … Sk wants to transmit to all destinations T1 T2… TN
– [Disjoint multicast]:– [Two-level multicast]: Two sets of destinations, a set Tm for multicast connection, another set Td for disjoint multicast connection.
• Random linear network coding achieves capacity for unicast, multicast, and above connections.• Extend results to ADT networks with…
– Delay– Cycles– Erasures/Failures
Network Coding and Reliable Communications Group
Conclusions• ADT network can be expressed with Algebraic Network Coding Formulation
– Use of higher field size– Model broadcast constraint with hyper-edge– Capture ADT network problem with a single system matrix M
• Prove an algebraic definition of min-cut = max rank(M)• Prove Min-cut Max-flow for unicast/multicast holds • Show that random linear network coding achieves capacity• Extend optimality of linear operations to non-multicast sessions
– Disjoint multicast, Two-level multicast, multiple source multicast, generalized min-cut max-flow theorem
– Random linear network coding achieves capacity
• Incorporate delay and failures (allows cycles within the network)