Cooperation and Crosslayer Design

in Wireless Networks

Andrea GoldsmithStanford University

DAWN ARO MURI Program Review

U.C. Santa CruzSeptember 12, 2006

Wireless Multimedia Networks In Military Operations

•Command/Control•Data, Images, Video

•Delay Constraints•Energy Constraints

Challenges to meeting network performance

requirements

Wireless channels are a difficult and capacity-limited broadcast communications medium

Fundamental capacity limits of wireless networks are unknown and, worse yet, poorly defined.

Wireless network protocols are generally ad-hoc and based on layering, which can be highly suboptimal

Energy and delay constraints change fundamental design principles

No single layer in the protocol stack can guarantee QoS: cross-layer design needed

Cooperation in Wireless Networks

Many possible cooperation strategies. Transmitter and receiver clusters can form virtual MIMO links. Cooperating nodes can be used as relays, possibly with

conferencing.

We investigate which forms of cooperation are effective. We consider dirty paper coding (DPC), relaying (DF and CF),

one-shot and iterative conferencing. Capacity gain from cooperation depends on network topology,

CSI, number of cooperating nodes, and SNR.

Virtual MIMO

• TX1 sends to RX1, TX2 sends to RX2• TX1 and TX2 cooperation leads to a MIMO BC• RX1 and RX2 cooperation leads to a MIMO MAC• TX and RX cooperation leads to a MIMO channel• Power and bandwidth spent for cooperation

TX1

TX2

RX1

RX2

Capacity Gain with Cooperation (2x2)

TX cooperation needs large cooperative channel gain to approach broadcast channel bound

MIMO bound unapproachable

TX1x1

x2

GG

Joint work with N. Jindal and U. Mitra

Capacity Gainvs Network Topology

Cooperative DPC best

Cooperative DPC worst

RX2

y2

TX1x1

x2

x1

d=1

d=r<1

Joint work with C. Ng

Optimal cooperation coupled with access and routing

Relative Benefits ofTX and RX Cooperation

Two possible CSI models: Each node has full CSI (synchronization between Tx and relay). Receiver phase CSI only (no TX-relay synchronization).

Two possible power allocation models: Optimal power allocation: Tx has power constraint aP, and

relay (1-a)P ; 0≤a≤1 needs to be optimized. Equal power allocation (a = ½).

Joint work with C. Ng

Transmitter vs. Receiver Cooperation

Capacity gain only realized with the right cooperation strategy

With full CSI, Tx co-op is superior.

With optimal power allocation and receiver phase CSI, Rx co-op is superior.

With equal power allocation and Rx phase CSI, cooperation offers no capacity gain.

Similar observations in Rayleigh fading channels.

Multiple-Antenna Relay Channel

Full CSIPower per transmit antenna: P/M.

Single-antenna source and relay

Two-antenna destination SNR > PU: No multiplexing gain;

can’t exceed SIMO channel capacity (Host-Madsen’05)

SNR < PL: MIMO GainJoint work with C. Ng and N. Laneman

Conferencing Relay Channel

Willems introduced conferencing for MAC (1983)Transmitters conference before sending message

We consider a relay channel with conferencing between the relay and destination

The conferencing link has total capacity C which can be allocated between the two directions

Joint work with C. Ng, I. Maric, S. Shamai, and R. Yates

Iterative vs. One-shot Conferencing

Weak relay channel: the iterative scheme is disadvantageous. Strong relay channel: iterative outperforms one-shot

conferencing for large C.

One-shot: DF vs. CF Iterative vs. One-shot

One-Shot Iterative

Crosslayer Design in Ad-Hoc Wireless

Networks

ApplicationNetworkAccessLink

Hardware

Substantial gains in throughput, efficiency, and end-to-end

performance from cross-layer design

Joint Compression andChannel Coding with

MIMOUse antennas for multiplexing:

Use antennas for diversity

High-RateQuantizer

ST CodeHigh Rate Decoder

Error Prone

Low Pe

Low-RateQuantizer

ST CodeHigh

DiversityDecoder

How should antennas be used?

Joint with T. Hollidayand H. V. Poor

Depends on end-to-end metric.

End-to-End Tradeoffs

kRu Index

Assignment

s bits

i)Channel Encoder

s bits

i

MIMO Channel

Channel Decoder

Inverse Index Assignment j)

s bits

j

s bits

Increased rate heredecreases source

distortion

But permits less diversity

here

Resulting in more errors

SourceEncoder

SourceDecoder

And maybe higher total distortion

A joint design is needed

vj

Antenna Assignment vs. SNR

Diversity-Multiplexing-ARQ

Suppose we allow ARQ with incremental redundancyARQ is a form of diversity [Caire/El

Gamal/Damen’05]Comes at the cost of delay

0

2

4

6

8

10

12

14

16

0 1 2 3 4

ARQ Window

Size L=1

L=2 L=3

L=4

d

r

Minimum Distortion under Delay Constraints

Delay/Throughput/Robustness across

Multiple Layers

Multiple routes through the network can be used for multiplexing or reduced delay/loss

Application can use single-description or multiple description codes

Can optimize optimal operating point for these tradeoffs to minimize distortion

A

B

Application layer

Network layer

MAC layer

Link layer

Cross-layer protocol design for real-time

media

Capacity assignment

for multiple service classes

Capacity assignment

for multiple service classes

Congestion-distortionoptimizedrouting

Congestion-distortionoptimizedrouting

Adaptivelink layer

techniques

Adaptivelink layer

techniques

Loss-resilientsource coding

and packetization

Loss-resilientsource coding

and packetization

Congestion-distortionoptimized

scheduling

Congestion-distortionoptimized

scheduling

Traffic flows

Link capacities

Link state information

Transport layer

Rate-distortion preamble

Joint with T. Yoo, E. Setton, X. Zhu, and B. Girod

Video streaming performance

3-fold increase

5 dB

100

s

(logarithmic scale)

1000

Energy-Constrained Nodes

Each node can only send a finite number of bits.Energy minimized by sending each bit very slowly. Introduces a delay versus energy tradeoff for each

bit.

Short-range networks must consider both transmit and processing/circuit energy.Sophisticated techniques not necessarily energy-

efficient. Long transmission times not necessarily optimalMultihop routing not necessarily optimal

Changes everything about the network design:Bit allocation must be optimized across all protocols.Delay vs. throughput vs. node/network lifetime

tradeoffs.Optimization of node cooperation.

Cross-Layer Optimization Model

The cost function f0(.) is energy consumption.

The design variables (x1,x2,…) are parameters that affect energy consumption, e.g. transmission time.

fi(x1,x2,…)0 and gj(x1,x2,…)=0 are system constraints, such as a delay or rate constraints.

If not convex, relaxation methods can be used. We focus on TD systems

Min ,...),( 210 xxf

s.t. ,0,...),( 21 xxfi Mi ,,1 Kj ,,1 ,0,...),( 21 xxg j

Joint work with S. Cui

Minimum Energy Routing

Transmission and Circuit Energy

4 3 2 1

0.3

(0,0)

(5,0)

(10,0)

(15,0)

Multihop routing may not be optimal when circuit energy consumption is considered

bits

RR

ppsR

100

0

60

32

1

Red: hub nodeBlue: relay onlyGreen: relay/source

Relay Nodes with Data to Send

Transmission energy only

4 3 2 10.115

0.515

0.185

0.085

0.1Red: hub nodeGreen: relay/source

ppsR

ppsR

ppsR

20

80

60

3

2

1

(0,0)

(5,0)

(10,0)

(15,0)

• Optimal routing uses single and multiple hops

• Link adaptation yields additional 70% energy savings

Virtual MIMO with Routing

Double String Topology with Alamouti Cooperation

Alamouti 2x1 diversity coding schemeAt layer j, node i acts as ith antennaSynchronization needed, but no cluster communication

Optimize link (constellation); MAC (transmission time), routing (which hops to use), scheduling

Goal is to optimize energy/delay tradeoff curve

Total Energy versus Delay

Cooperative Compression

Source data correlated in space and time

Nodes should cooperate in compression as well as communication and routing Joint source/channel/network codingWhat is optimal: virtual MIMO vs. relaying

Conclusions Cooperation in wireless networks is essential

Leads to significant capacity gains The appropriate form of cooperation depends on the

environment and CSI assumptions Many forms of cooperation are still unexplored

End-to-end performance requires a cross-layer design that exploits tradeoffs at each layer by higher layer protocols Cross-layer design leads to increased throughput, efficiency,

and end-to-end performance Cross-layer design requires new design and analysis tools Cross-layer design under energy constraints yields atypical

protocols Care must be used to avoid negative interactions and maintain

simplicity and scalability.

Plans for the Coming Year

Cooperative CommunicationsConferencing with multiple iterationsLayered broadcast coding approachesMultiple relays with multiple antennasCooperation for cognitive radios

Cross-layer DesignExtend diversity/multiplexing/ARQ

tradeoff analysis to wireless networksBroader the notion of source/channel

separation to include channel outage/error

Incorporate network coding into cross-layer design (w/ T. Ephremides and M. Medard)

Joint Source/Channel/Network Coding

SourceSourceCodingCodingSourceSourceCodingCoding

InformationInformationTheoreticTheoretic

RateRateRegionsRegions

InformationInformationTheoreticTheoretic

RateRateRegionsRegions

NetworkNetworkCodingCoding

ororRoutingRouting

NetworkNetworkCodingCoding

ororRoutingRouting

ConvexConvexOptimizationOptimization

(Minimum(MinimumDistortion)Distortion)

ConvexConvexOptimizationOptimization

(Minimum(MinimumDistortion)Distortion)

SSSSSSSS

D(·)D(·)D(·)D(·)

Separate Separate DesignDesign OptimalOptimal

Separate Separate DesignDesign OptimalOptimal

Separate Separate Design Design

Optimal?Optimal?

Separate Separate Design Design

Optimal?Optimal?