ITMANET FLoWS Focus Talk
Interference in MANETs: Friend or Foe?
Andrea Goldsmith
Joint work with Dabora, Gunduz, Kramer, Liu, Maric, Poor, Shamai
MANET Characteristics





Peer-to-peer communications
All transmissions interfere due to broadcast nature of radio
Highly dynamic
Nodes can cooperate to forward data
Can introduce feedback to improve performance
Interference in MANETs

Radio is a broadcast medium

Radios in the same spectrum interfere

MANET capacity in unknown for all canonical
networks with interference (even when exploited)




Z Channel
Interference Channel
Relay Channel
General MANETs
Interference: Friend or Foe?

If treated as noise: Foe
P
Increases BER,
SNR 
Reduces capacity
NI
 If decodable or precodable: Neutral
 Neither
friend nor foe
Multiuser detecion (MUD) and precoding
can completely remove interference
Common coding strategy to approach capacity
Interference: Friend or Foe?
If exploited via coding,
cooperation, and cognition
Friend
Especially in a network setting
Exploiting Interference
through Coding
The Z Channel

Capacity of Z channel unknown in general

We obtain capacity for a class of Z channels
•
•
•

Korner/Marton technique applicable
Enough to consider superposition encoding
Han/Kobayashi achievable region is capacity region
Yields capacity for large class of Gaussian
interference channels
Exploiting Interference
through Cognition

Cognitive user has knowledge of other
user’s message and/or encoding strategy
 Used
to help noncognitive transmission
 Used to presubtract noncognitive interference
CR
NCR
RX1
RX2
Joint with Maric, Kramer, Shamai
Proposed Transmission Strategy
To allow each receiver to
decode part of the other
node’s message
 reduces interference
Removes the NCR
interference at the CR RX
Precoding against
interference
at CR TX
Rate splitting
Precoding against
interference
at CR TX
Cooperation
at CR TX
Cooperation
at CR TX
To help in sending NCR’s
message to its RX
8
We optimally combine
these approaches into
one strategy
More Precisely:
Transmission for Achievable Rates
The NCR uses single-user encoder
W2
PX 2 (.)
The CR uses
X 2N
CR
RX1
NCR
RX2
- Rate-splitting to allow receiver 2 to decode part of cognitive
user’s message and thus reduce interference at that receiver
- Precoding while treating the codebook for user 2 as
interference to improve rate to its own receiver
- Cooperation to increase rate to receiver 2
N
W
U
c
W1 Rate
1c
U 1Nc ,U 1Na
PU1c (.)
W1a
split
N
P
(.
|
u
)
X
1
U1a |U1c
1c
X 2N
N
CR
X2
W2
XN
NCR
2
Upper Bounds
• Follows from standard approach:
• Invoke Fano’s inequality
• Reduces to outer bound for full cooperation for R2=0
• Has to be evaluated for specific channels
How far are the achievable rates from the outer bound?
10
Performance Gains
from Cognitive Encoding
outer bound
our scheme
prior schemes
CR
broadcast
bound
Exploiting Interference
through Relaying
TX1
RX1
Y4=X1+X2+X3+Z4
X1
relay
Y3=X1+X2+Z3
TX2

X3= f(Y3)
X2
Y5=X1+X2+X3+Z5
RX2
Relaying strategies:
 Relay can forward all or part of the messages


Much room for innovation
Relay can forward interference

To help subtract it out
Joint with Maric, Dabora, Medard
Achievable Rates with
Interference Forwarding
R1  I ( X 1 ; Y1 | X 2 , X 3 )
encoder 1
dest1
R1  R2  I ( X 1 , X 2 , X 3 ; Y1 )
relay
encoder 2
R2  I ( X 2 , X 3 ; Y2 | X 1 )
dest2
R1  R2  I ( X 1 , X 2 , X 3 ; Y2 )
R2  I ( X 2 ; Y3 | X 3 )
for any distribution p(p(x1)p(x2,x3)p(y1,y2|x1,x2,x3)
• The strategy to achieve these rates is:
- Single-user encoding at the encoder 1 to send W1
- Decode/forward at encoder 2 and the relay to send message W2
• This region equals the capacity region when the interference is
strong and the channel is degraded
Capacity Gains from
Interference Forwarding
Diversity-Multiplexing Tradeoffs in
Multi-Antenna MANETs
• Focus on (M1, M2, M3)
• Quasi-static Rayleigh fading channel
Yi 
SNR
H i X i  Wi
Mi
•Channel state known only at the receivers
Joint with Gunduz, Poor
Diversity-Multiplexing Tradeoff in
Point-to-Point MIMO Channels
- Multiplexing gain r:
- Diversity gain d
d M1M 2 (k )  ( M 1  k )( M 2  k )
DMT for Full-duplex Relays

The relay can receive and transmit simultaneously

The DMT for (M1,M2,M3) full-duplex system is
d M1M 2 M 3 (r )  min{ d M1M 2 (r ), d M 2 M 3 (r )}

The hop with the minimum diversity gain is the
bottleneck

Achieved by decode-and-forward relaying with
block Markov structure

Follows easily since DF achieves capacity
Half-duplex Relay
Static Protocols:

The source transmits during the first aT channel uses, 0<a<1
The relay tries to decode the message and forwards over the
remaining (1-a)T channel uses:
decode-and-forward with fixed allocation (fDF)

The DMT for half-duplex (M1,M2,M3) system with fixed time
allocation a

r
 r 
d M1M 2M3 (r )  min d M1M 2  , d M 2 M 3 

a
1

a
 



fDF

Optimize a for each multiplexing gain:
decode-and-forward with variable allocation (vDF)
Dynamic Decode-and-Forward (DDF)
for Half-duplex Relay

Introduced by Azarian et al. (IT’05):

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Relay listens until decoding
Transmits only after decoding

Achieves the best known DMT for half-duplex relay
channels, yet short of the upper bound

We show: Achieves optimal DMT in multi-hop relay
channels

Not piece-wise linear, no general closed form expression

Can be cast into a convex optimization problem
DMT of (4,1,3) half-duplex relay channel
4
3.5
d
4,1
(r)
Diversity gain, d(r)
3
2.5
d
1,3
(r)
2
dDDF(r)
1.5
d
1
(r), a=0.5
fDF
d
(r)
vDF
0.5
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Multiplexing gain, (r)
0.7
0.8
0.9
1
DMT of (2,2,2) half-duplex relay channel
4
3.5
Diversity gain, d(r)
3
2.5
d2,2(r)
2
dvDF(r)
1.5
dDDF(r)
1
0.5
0
0
0.2
0.4
0.6
0.8
1
1.2
Multiplexing gain, (r)
1.4
1.6
1.8
2
Multiple Relay Networks
• Multiple full-duplex relays:
•DMT dominated by hop with minimum diversity gain.
• Multiple half-duplex relays:
•Odd and even numbered relays transmit in turn.
• DDF (with time limitation for successive hops) is DMT optimal.
•DMT dominated by 2 consecutive hops with min. diversity gain
End to End Distortion

Use antennas for multiplexing:
High-Rate
Quantizer
ST Code
High Rate
Decoder
DMT of (2,2,2) half-duplex relay channel
4
3.5
Use antennas for diversity
Diversity gain, d(r)

3
2.5
d2,2(r)
2
dvDF(r)
1.5
dDDF(r)
1
0.5
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Multiplexing gain, (r)
Low-Rate
Quantizer
ST Code
High
Diversity
Decoder
We optimize the point on the DMT tradeoff curve to minimize distortion
Exploiting Interference reduces
End-to-End Distortion

Interference exploitation at the physical
layer improves end-to-end distortion

We have proved a separation theorem for a
class of interference channels
 Separate

source and channel coding optimal
We found the operating point on the DMT
multihop region for minimal distortion
 Under
delay constraints, optimization needed
Summary
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Fundamental performance limits of MANETS
requires understanding and exploiting interference

Interference can be exploited via coding/relaying,
cooperation, or cognition

The right strategy depends on CSI, dynamics, network
topology, and node capabilities.

Exploiting interference leads to higher capacity,
more robustness, and better end-to-end performance

MIMO adds a new degree of freedom at each node
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Use antennas for multiplexing, diversity, or IC?
Final Comments: Startup
Lessons Learned

People in industry read our papers and implement our ideas
• Communication and network theory can be implemented in
a real system in 3-9 months
• Information Theory heavily influences current and nextgen. wireless systems (mainly at the PHY & MAC layers)
• Idealized assumptions have been liberating
• Wireless network design above PHY/MAC layer is ad-hoc
• The most effective way to do tech transfer is to start a
company