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

INPSNR

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

XYXIRYXXXIRRYXXXIRR

XYXXIRXXYXIR

• 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

KnowledgeandComplexity

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 SystemsCognitive 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

Coo

pera

tion

at C

R T

X Precoding againstinterference

at CR

TX

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

Nc

U1

NX2

NX2

NNac

UU11

,

NX2

NX12W

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

RateDelay

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 effortDiminishing 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.