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Node Cooperation and Cognition
in Dynamic Wireless Networks
Andrea GoldsmithStanford University
Joint with I. Maric, R. Dabora, N. Liu and D.C. Oneill
DAWN ARO MURI Program Review
U.C. Santa CruzSeptember 5, 2007
Wireless Multimedia Networks In Military Operations
• Command/Control• Data, Images, Video
How to optimize QoS and end-to-end performance?
Challenges to meeting network performance
requirements
Wireless channels are a difficult and capacity-limited broadcast communications medium
Interference severely degrades link performance
Network dynamics require adaptive and flexible protocols as well as distributed control
Wireless network protocols are generally ad-hoc and based on layering, but no single layer in the protocol stack can guarantee QoS
Interference in Wireless Networks
Radio is a broadcast medium
Radios in the same spectrum interfere
Network capacity in unknown for all canonical networks with interference (even when exploited)Z ChannelInterference ChannelRelay ChannelGeneral wireless ad-hoc networks
Interference: Friend or Foe?
If treated as noise: Foe
If decodable or precodable: Neutral Neither friend nor foe
IN
PSNR
Increases BER,
Reduces capacity
Multiuser detecion (MUD) and precoding
can completely remove interferenceCommon coding strategy to
approach capacity
If exploited via coding, cooperation, and
cognition
Friend
Interference: Friend or Foe?
Especially in a network setting
Cooperation in Wireless Networks
Many possible cooperation strategies:Cooperative coding, virtual MIMO,
interference forwarding, generalized relaying, and conferencing
“He that does good to another does good also to himself.” Lucius Annaeus Seneca
Cooperation through Coding
Capacity of Z channel unknown in general
Encoding strategy of X1 impacts
both receivers We obtain capacity for a class of Z
channelsSuperposition encoding and partial
decoding is capacity-achieving for these channels
Can show separation principle applies
The Z Channel
Codebook Design
Cooperation through Relaying
Relaying strategies: Relay can forward all or part of the
messages Much room for innovation
Relay can forward interference To help subtract it out
TX1
TX2
relay
RX2
RX1X1
X2
Y3=X1+X2+Z3
Y4=X1+X2+X3+Z4
Y5=X1+X2+X3+Z5
X3= f(Y3)
Achievable Rates withInterference Forwarding
)|;(
);,,(
);,,(
)|;,(
),|;(
3322
232121
132121
12322
32111
XYXIR
YXXXIRR
YXXXIRR
XYXXIR
XXYXIR
• 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
for any distribution p(p(x1)p(x2,x3)p(y1,y2|x1,x2,x3)
dest1
dest2
encoder 1
encoder 2
relay
Capacity Gains fromInterference Forwarding
Benefits of Cooperation
ScalabilityIncreased capacityReduced energy consumptionBetter end-to-end performance
We need more creative mechanisms fornode cooperation in wireless networks
Exploiting Interference through
Cognition
Cognitive radios can support new wireless users in existing crowded spectrumWithout degrading performance of existing
users
Utilize advanced communication and signal processing techniquesCoupled with novel spectrum allocation
policies
Technology could Revolutionize the way spectrum is
allocated worldwide Provide sufficient bandwidth to support
higher quality and higher data rate products and services
What is a Cognitive Radio?
Cognitive radios (CRs) intelligently exploit available side information about the
(a)Channel conditions(b)Activity(c)Codebooks(d) Messages
of other nodes with which they share the spectrum
Cognitive Radio Paradigms
UnderlayCognitive radios constrained to
cause minimal interference to noncognitive radios
InterweaveCognitive radios find and exploit
spectral holes to avoid interfering with noncognitive radios
OverlayCognitive radios overhear and
enhance noncognitive radio transmissions
Knowledge
andComplex
ity
Underlay Systems Cognitive radios determine the
interference their transmission causes to noncognitive nodesTransmit if interference below a given
threshold
The interference constraint may be metVia wideband signalling to maintain
interference below the noise floor (spread spectrum or UWB)
Via multiple antennas and beamforming
Challenges: measuring interference at RX and policy
NCR
IP
NCRCR CR
Interweave Systems Measurements indicate that even
crowded spectrum is not used across all time, space, and frequenciesOriginal motivation for “cognitive” radios
(Mitola’00)
These holes can be used for communicationDetecting and avoiding active users is
challengingHole location must be agreed upon between
TX and RXCommon holes between TX and RX may be
rare
Overlay Systems
Cognitive user has knowledge of other user’s message and/or encoding strategyUsed to help noncognitive
transmissionUsed to presubtract noncognitive
interferenceRX1
RX2NCR
CR
19
Proposed Transmission Strategy
Rate splitting
Precoding againstinterference
at CR TX
Cooperationat CR TXCooperation
atCR TX
Coop
era
tion
at
CR
TX P
reco
din
g a
gain
stin
terfe
ren
ceat C
R T
X
To allow each receiver to decode part of the other node’s message
reduces interference
Removes the NCR interference at the CR RX
To help in sending NCR’s
message to its RX
We optimally combine these approaches into
one strategy
More Precisely: Transmission for Achievable Rates
Rate split
(.)1cUP
NCR
)|(. 1| 11 cUU uPca
2W (.)2X
PNX2
1W cW
aW1
N
cU
1
NX2
NX2
NN
acUU
11,
NX2
NX1
2W
CR
The NCR uses single-user encoder
The CR uses - 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
RX1
RX2NCR
CR
Upper Bounds
How far are the achievable rates from the outer bound?
• 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
Performance Gains from Cognitive
Encoding
CRbroadcast
bound
outer boundour
schemeprior schemes
What about Dynamics?
Need new control mechanisms in addition to new
coding strategies
Introduction to Wireless Network Utility Maximization
Wireless networks operate over random time varying channels Fading distribution typically
unknown
Upper Layer performance is critical Dictates application quality Dictates user experience
Application performance depends on multiple performance metrics Rate Delay Outage
SNR
time
PhysicalLayer
UpperLayers
PhysicalLayer
UpperLayers
Rate
Delay
Outage
Utility=f(Rate,Delay,Outage)
(R*,D*,O*)
Wireless NUM Problem Statement
Find network policies (control functions) thatOptimize performance
At upper layers Through optimal cross layer
interaction Utilizing information-theoretic
coding strategies
Meet constraints Long term average: e.g. Power:
E[S(·)]≤S Instantaneous: e.g. Reliability:
BER≤(·)
Adapt gracefully to changing conditions
Network Utility Maximization (NUM)
Model end-to-end performance as a utility function (typically a function of rate
NUM often applied to wireline/wireless networksPerforms poorly in dynamic
environments
Dynamic NUM extends NUM to include dynamics in the links, interference, and network.
Best effort
Diminishing returnsContract with penalty
Interference and dynamics
easily incorporated
Utility functions U(r) Rate only Does not “select”
Rate-Reliability operating point
Explicit Rate-Reliability tradeoff by sources UB(rate, reliability)
B controls tradeoff
Sources select link code rate to meet reliability needs
Policies for Link power Sl(.) l=1,
…,L Link rates Rl(.) l=1,
…,L Code rates l=1,
…,L
Data
Data Data),( 111 rU
),( 222 rU
),( 333 rU
PhysicalLayer
Buffer
UpperLayers
PhysicalLayer
Buffer
UpperLayers
PhysicalLayer
Buffer
UpperLayers
PhysicalLayer
Buffer
UpperLayers
PhysicalLayer
Buffer
UpperLayers
Data
(.)l
Performance Improvement of Wireless NUM
Beta controls tradeoff in UB(rate, reliability)
BER (Reliability) Benefits
Rate Benefits
Summary
Interference can be exploited via cooperation and cognition to improve spectral utilization as well as end-to-end performance
Much room for innovation
WNUM can provide the bridge to incorporate novel coding methods into dynamic distributed networks.