Cooperation and Crosslayer Design
in Wireless Networks
Andrea Goldsmith
Stanford University
DAWN ARO MURI Program Review
U.C. Santa Cruz
September 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), oneshot and iterative conferencing.
Capacity gain from cooperation depends on network topology, CSI,
number of cooperating nodes, and SNR.
Virtual MIMO
TX1
RX1
TX2
RX2
• 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
Capacity Gain with
Cooperation (2x2)
x
TX11
G
G
x2
Joint work with N. Jindal
and U. Mitra


TX cooperation needs large cooperative channel
gain to approach broadcast channel bound
MIMO bound unapproachable
Capacity Gain
vs Network Topology
x1
TX1
x2
d=r<1
x1
Cooperative DPC best
d=1
y2
Joint work with C. Ng
Cooperative
DPC worst
RX2
Optimal cooperation coupled with access and routing
Relative Benefits of
TX 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 CSI
Power 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 Gain
Joint 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
One-Shot
One-shot: DF vs. CF
Iterative
Iterative vs. One-shot

Weak relay channel: the iterative scheme is disadvantageous.

Strong relay channel: iterative outperforms one-shot
conferencing for large C.
Crosslayer Design in Ad-Hoc
Wireless Networks

Application

Network

Access

Link

Hardware
Substantial gains in throughput, efficiency, and
end-to-end performance from cross-layer design
Joint Compression and
Channel Coding with MIMO

Use antennas for multiplexing:
High-Rate
Quantizer
ST Code
High Rate
Joint with T. Holliday
and H. V. Poor
Decoder
Error Prone

Use antennas for diversity
Low-Rate
Quantizer
ST Code
High
Diversity
Decoder
Low Pe
How should antennas be used? Depends on end-to-end metric.
End-to-End Tradeoffs
uR
k
Source
Encoder
s bits
i
Increased rate here
decreases source distortion
Index
Assignment
s bits
p(i)
But permits less
diversity here
Channel
Encoder
MIMO
Channel
A joint design is needed
vj
Source
Decoder
s bits Inverse Index s bits
Assignment
j
p(j)
And maybe higher total distortion
Channel
Decoder
Resulting in more errors
Antenna Assignment vs. SNR
Diversity-Multiplexing-ARQ

Suppose we allow ARQ with incremental redundancy


ARQ is a form of diversity [Caire/El Gamal/Damen’05]
Comes at the cost of delay
d
16
14
12
L=4
10
8
6
ARQ Window
4
Size L=1
L=2
L=3
2
0
0
1
2
3
4
r
Minimum Distortion under
Delay Constraints
Delay/Throughput/Robustness
across Multiple Layers
B
A

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
Cross-layer protocol design
for real-time media
Loss-resilient
source coding
and packetization
Application layer
Rate-distortion preamble
Traffic flows
Congestion-distortion
optimized
scheduling
Transport layer
Congestion-distortion
optimized
routing
Network layer
Capacity
assignment
for multiple service
classes
Link capacities
MAC layer
Link state information
Joint with T. Yoo, E. Setton,
X. Zhu, and B. Girod
Adaptive
link layer
techniques
Link layer
Video streaming
performance
s
5 dB
3-fold increase
100
1000 (logarithmic scale)
Energy-Constrained Nodes

Each node can only send a finite number of bits.



Short-range networks must consider both transmit
and processing/circuit energy.




Energy minimized by sending each bit very slowly.
Introduces a delay versus energy tradeoff for each bit.
Sophisticated techniques not necessarily energy-efficient.
Long transmission times not necessarily optimal
Multihop 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
Min
s.t.
f 0 ( x1 , x2 ,...)
f i ( x1 , x2 ,...)  0, i  1,, M
g j ( x1 , x2 ,...)  0, j  1,, K

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
Joint work with S. Cui
Minimum Energy Routing

Transmission and Circuit Energy
Red: hub node
Blue: relay only
Green: relay/source
0.3
4
(0,0)
3
(5,0)
2
(10,0)
1
(15,0)
R1  60 pps
R2  R3  0
  100bits
Multihop routing may not be optimal when
circuit energy consumption is considered
Relay Nodes with
Data to Send

Transmission energy only
0.1
Red: hub node
Green: relay/source
0.085
4
(0,0)
3
0.185
(5,0)
0.515
2
(10,0)
0.115
1
(15,0)
R1  60 pps
R2  80 pps
R3  20 pps
• 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 scheme



At layer j, node i acts as ith antenna
Synchronization 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 coding

What 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 endto-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 Communications





Conferencing with multiple iterations
Layered broadcast coding approaches
Multiple relays with multiple antennas
Cooperation for cognitive radios
Cross-layer Design
Extend diversity/multiplexing/ARQ tradeoff
analysis to wireless networks
 Broader 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
Separate
Design
Optimal?
Source
Coding
S
Information
Theoretic
Rate
Regions
Separate
Design
Optimal
Network
Coding
or
Routing
Convex
Optimization
(Minimum
Distortion)
D(·)
S