1. Node Cooperation and Cognition in Dynamic Wireless Networks
Andrea Goldsmith
Stanford University
Joint with I. Maric, R. Dabora, N. Liu and D.C. Oneill
DAWN ARO MURI Program Review
U.C. Santa Cruz
September 5, 2007

2. Wireless Multimedia Networks In Military Operations
Command/Control
Data, Images, Video
How to optimize QoS and end-to-end performance?

3. 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

4. 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 Channel
Interference Channel
Relay Channel
General wireless ad-hoc networks

5. Interference: Friend or Foe?
If treated as noise: Foe
If decodable or precodable: Neutral
Neither friend nor foe
Increases BER,
Reduces capacity
Multiuser detecion (MUD) and precoding
can completely remove interference
Common coding strategy to approach capacity

6. If exploited via coding, cooperation, and cognition
Interference: Friend or Foe?
If exploited via coding, cooperation, and cognition
Friend
Especially in a network setting

7. 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

8. Cooperation through Coding
Codebook Design
The Z Channel
Capacity of Z channel unknown in general
Encoding strategy of X1 impacts both receivers
We obtain capacity for a class of Z channels
Superposition encoding and partial decoding is capacity-achieving for these channels
Can show separation principle applies

9. Cooperation through Relaying
TX1
TX2
relay
RX2
RX1 X1 X2
Y3=X1+X2+Z3
Y4=X1+X2+X3+Z4
Y5=X1+X2+X3+Z5
X3= f(Y3)
Relaying strategies:
Relay can forward all or part of the messages
Much room for innovation
Relay can forward interference
To help subtract it out

10. Achievable Rates with Interference Forwarding
dest1
dest2
encoder 1
encoder 2
relay
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

11. Capacity Gains from Interference Forwarding

12. Benefits of Cooperation
Scalability
Increased capacity
Reduced energy consumption
Better end-to-end performance
We need more creative mechanisms for
node cooperation in wireless networks

13. Exploiting Interference through Cognition
Cognitive radios can support new wireless users in existing crowded spectrum
Without degrading performance of existing users
Utilize advanced communication and signal processing techniques
Coupled 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

14. What is a Cognitive Radio?
Cognitive radios (CRs) intelligently exploit
available side information about the
Channel conditions
Activity
Codebooks
Messages
of other nodes with which they share the spectrum

15. Cognitive Radio Paradigms
Underlay
Cognitive radios constrained to cause minimal interference to noncognitive radios
Interweave
Cognitive radios find and exploit spectral holes to avoid interfering with noncognitive radios
Overlay
Cognitive radios overhear and enhance noncognitive radio transmissions
Knowledge
and
Complexity

16. Underlay Systems
Cognitive radios determine the interference their transmission causes to noncognitive nodes
Transmit if interference below a given threshold
The interference constraint may be met
Via 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 IP CR
NCR
NCR

17. Interweave Systems
Measurements indicate that even crowded spectrum is not used across all time, space, and frequencies
Original motivation for “cognitive” radios (Mitola’00)
These holes can be used for communication
Detecting and avoiding active users is challenging
Hole location must be agreed upon between TX and RX
Common holes between TX and RX may be rare

18. Overlay Systems
Cognitive user has knowledge of other user’s message and/or encoding strategy
Used to help noncognitive transmission
Used to presubtract noncognitive interference
RX1 CR
RX2
NCR

19. Proposed Transmission Strategy
To allow each receiver to decode part of the other node’s message  reduces interference
Cooperation at CR TX
Cooperation atCR TX
Removes the NCR
interference at the CR RX
Cooperation at CR TX
Precoding against interference at CR TX
To help in sending NCR’s message to its RX
It is CLEAR that it needs
Precoding against interference at CR TX
Rate splitting
We optimally combine
these approaches into
one strategy

20. More Precisely: Transmission for Achievable Rates
The NCR uses single-user encoder
RX1
RX2
NCR CR
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
Rate
split CR
NCR

21. 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?

22. Performance Gains from Cognitive Encoding
outer bound
our scheme
prior schemes CR
broadcast bound

23. Need new control mechanisms in addition to new coding strategies
What about Dynamics?
Need new control mechanisms in addition to new coding strategies

24. Introduction to Wireless Network Utility Maximization
SNR
time
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
Physical
Layer
Upper
Layers
Physical
Layer
Upper
Layers
Rate
(R*,D*,O*)
Delay
Utility=f(Rate,Delay,Outage)
Outage

25. Wireless NUM Problem Statement
Find network policies (control functions) that
Optimize 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

26. Network Utility Maximization (NUM)
Model end-to-end performance as a utility function (typically a function of rate
NUM often applied to wireline/wireless networks
Performs poorly in dynamic environments
Dynamic NUM extends NUM to include dynamics in the links, interference, and network.
Best effort
Diminishing returns
Contract with penalty
1 NUM has been extensively studied in wireline networks
2 Models
2.1 Upper layer protocols as concave strictly increasing functions. Examples: Stephen Low’s work on TCP
2.2 Network as a collection of fixed capacity non interfering deterministic links.
3 In this talk we will look at utility functions of the form, where r is the flow of information from the upper layer protocol.

27. 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
Physical
Layer
Buffer
Upper
Layers
Key point is that the utility function is determining the optimal operating point of the network. Beta is a parameter that changes the utility functions’ preference for rate vs. reliability. The network uses convolutional codes that the upper layers can select among as necessary.

28. Performance Improvement of Wireless NUM
Rate Benefits
BER (Reliability) Benefits
WNUM offers greater performance: Greater throughput and lower error rates than NUM or any fixed system. I think of TCP’s reaction to errors (lost packets) over wireless links. Perhaps we should get involved in the “NEW TCP” projects?
Beta controls tradeoff in
UB(rate, reliability)

29. 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.