Node Cooperation and Cognition in Dynamic Wireless Networks

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

6. If exploited via coding, cooperation, and cognition Interference: Friend or Foe? 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.” LuciusAnnaeus 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. RX1 TX1 X1 Y4=X1+X2+X3+Z4 relay Y3=X1+X2+Z3 X3= f(Y3) Y5=X1+X2+X3+Z5 X2 TX2 RX2 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

10. encoder 1 dest1 relay encoder 2 dest2 Achievable Rates withInterference Forwarding 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 fromInterference Forwarding

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

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 Knowledge and Complexity • 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

16. Underlay Systems IP NCR CR CR NCR • 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

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

19. Proposed Transmission Strategy To allow each receiver to decode part of the other node’s message  reduces interference Cooperationat CR TX CooperationatCR TX Removes the NCR interference at the CR RX Cooperationat CR TX Precoding againstinterferenceat CR TX To help in sending NCR’smessage to its RX Precoding againstinterferenceat 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 The CRuses - 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 CR Rate split RX2 CR NCR NCR

21. 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

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

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

24. 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 Upper Layers Upper Layers Physical Layer Physical Layer SNR time 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

27. Interference and dynamicseasily 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 Data Upper Layers Upper Layers Upper Layers Upper Layers Upper Layers Buffer Buffer Buffer Buffer Buffer Physical Layer Physical Layer Physical Layer Physical Layer Physical Layer

28. Performance Improvement of Wireless NUM Rate Benefits BER (Reliability) Benefits 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.