Cooperation and Crosslayer Design in Wireless Networks

1. Cooperation and Crosslayer Design in Wireless Networks Andrea Goldsmith Stanford University DAWN ARO MURI Program Review U.C. Santa Cruz September 12, 2006

2. Wireless Multimedia Networks In Military Operations • Command/Control • Data, Images, Video • Delay Constraints • Energy Constraints

3. 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

4. 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.

5. 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 RX1 RX2 TX2

6. Capacity Gain with Cooperation (2x2) x1 TX1 • TX cooperation needs large cooperative channel gain to approach broadcast channel bound • MIMO bound unapproachable G G x2 Joint work with N. Jindal and U. Mitra

7. Cooperative DPC best Cooperative DPC worst d=r<1 x1 x1 TX1 y2 x2 d=1 Capacity Gainvs Network Topology Joint work with C. Ng RX2 Optimal cooperation coupled with access and routing

8. 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

9. 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.

10. 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

11. 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

12. Iterative vs. One-shot Conferencing One-Shot Iterative • 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

13. 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

14. ST Code High Rate High-Rate Quantizer Decoder Error Prone ST Code High Diversity Low-Rate Quantizer Decoder Low Pe Joint Compression andChannel Coding with MIMO Joint with T. Holliday and H. V. Poor • Use antennas for multiplexing: • Use antennas for diversity Depends on end-to-end metric. How should antennas be used?

15. Increased rate here decreases source distortion But permits less diversity here And maybe higher total distortion Resulting in more errors End-to-End Tradeoffs s bits s bits Index Assignment Channel Encoder Source Encoder i p(i) MIMO Channel A joint design is needed s bits s bits Channel Decoder Inverse Index Assignment vj Source Decoder j p(j)

16. Antenna Assignment vs. SNR

17. 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 L=4 ARQ Window Size L=1 L=2 L=3

18. Minimum Distortion under Delay Constraints

19. Delay/Throughput/Robustness across Multiple Layers B • 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

20. Cross-layer protocol design for real-time media Loss-resilientsource codingand packetization Application layer Rate-distortion preamble Congestion-distortionoptimized scheduling Transport layer Congestion-distortionoptimized routing Traffic flows Network layer Capacity assignmentfor multiple service classes Link capacities MAC layer Link state information Adaptive link layertechniques Joint with T. Yoo, E. Setton, X. Zhu, and B. Girod Link layer

21. Video streaming performance s 5 dB 3-fold increase 100 1000 (logarithmic scale)

22. 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 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.

23. Cross-Layer Optimization Model Min • 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 s.t. Joint work with S. Cui

24. Minimum Energy Routing • Transmission and Circuit Energy Red: hub node Blue: relay only Green: relay/source 0.3 2 4 1 3 (15,0) (0,0) (5,0) (10,0) Multihop routing may not be optimal when circuit energy consumption is considered

25. Relay Nodes with Data to Send • Transmission energy only 0.1 Red: hub node Green: relay/source 0.085 2 4 1 3 0.115 0.185 (15,0) (0,0) (5,0) (10,0) 0.515 • Optimal routing uses single and multiple hops • Link adaptation yields additional 70% energy savings

26. Virtual MIMO with Routing

27. 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

28. Total Energy versus Delay

29. 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

30. 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.

31. 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)

32. Separate Design Optimal Separate Design Optimal? Joint Source/Channel/Network Coding Source Coding Information Theoretic Rate Regions Network Coding or Routing Convex Optimization (Minimum Distortion) D(·) S S