Cross-Layer Design of Wireless Networks

1. Cross-Layer Design of Wireless Networks Andrea Goldsmith Stanford University wsl.stanford.edu Clean Slate Seminar Dec. 5, 2005

2. Future Network Applications Internet (for the Z generation) “Cellular” Entertainment Commerce Smart Homes/Spaces/Structures Sensor Networks Automated Highways/Factories … Applications have hard delay constraints, rate requirements, energy constraints, and/or security constraints that must be met These requirements are collectively called QoS

3. Challenges to meeting QoS • Underlying channels, networks, and end-devices are heterogenous • Traffic patterns, user locations, and network conditions are constantly changing • Hard constraints cannot be guaranteed, and average constraints can be poor metrics. • No single layer in the protocol stack can support QoS: cross-layer design needed

4. Application Application Presentation Presentation Session Session Transport Transport Network Network Network Datalink Datalink Datalink Physical Physical Physical Physical medium Example: OSI Reference Model A Brief Introductionto Protocol Layers Premise: Break network tasks into logically distinct entities, each built on top of the service provided by the lower layer entities.

5. OSI vs. TCP/IP • OSI: conceptually define services, interfaces, protocols • Internet: provides a successful implementation Application Application Telnet FTP DNS Presentation Session TCP UDP Transport Transport IP Network Internet Datalink Host-to- network Packet radio LAN Physical OSI TCP/IP

6. Layer Functionality • Application • Compression, error concealment, packetization, scheduling, … • Transport • End-to-end error recovery, retransmissions, flow control, … • Network • Neighbor discovery and routing • Access • Channel sharing, error recovery/retransmission, packetization, … • Link • Bit transmission (modulation, coding, …)

7. Layering Pros and Cons • Advantages • Simplification - Breaking the complex task of end-to-end networking into disjoint parts simplifies design • Modularity – Protocols easier to optimize, manage, and maintain. More insight into layer operation. • Abstract functionality –Lower layers can be changed without affecting the upper layers • Reuse – Upper layers can reuse the functionality provided by lower layers • Disadvantages • Suboptimal: Layering introduces inefficiencies and/or redundancy (same function performed at multiple layers) • Information hiding: information about operation at one layer cannot be used by higher or lower layers • Performance: Layering can lead to poor performance, especially for applications with hard QoS constraints

8. Key layering questions • How should the complex task of end-to-end networking be decomposed into layers • What functions should be placed at each level? • Can a function be placed at multiple levels? • What should the layer interfaces be? • Should networks be decomposed into layers? • Design of each protocol layer entails tradeoffs, which should be optimized relative to other protocol layers • What is the alternative to layered design? • Cross-layer design • No-layer design

9. Crosslayer Design:Information Exchange Across Layers • Application • Transport • Network • Access • Link End-to-End Metrics Substantial gains in throughput, efficiency, and QoS can be achieved with cross-layer design

10. Information Exchange • Applications have information about the data characteristics and requirements • Lower layers have information about network/channel conditions

11. Crosslayer Techniques • Adaptive techniques • Link, MAC, network, and application adaptation • Resource management and allocation • Diversity techniques • Link diversity (antennas, channels, etc.) • Access diversity • Route diversity • Application diversity • Content location/server diversity • Scheduling • Application scheduling/data prioritization • Resource reservation • Access scheduling

12. Key Questions • What is the right framework for crosslayer design? • What are the key crosslayer design synergies? • How to manage crosslayer complexity? • What information should be exchanged across layers, and how should this information be used? • How to balance the needs of all users/applications?

13. Cross-Layer Design Examples • Joint source/channel coding in MIMO channels and networks • Energy-constrained networks • Distributed control over wireless networks

14. ST Code High Rate High-Rate Quantizer Decoder Error Prone ST Code High Diversity Low-Rate Quantizer Decoder Low Pe Joint S/C Coding with MIMO • Use antennas for multiplexing • Use antennas for diversity

15. Diversity/MultiplexingTradeoff at High SNR‡ • Define a family of block codes {C(SNR)}of length T with rate R(SNR)~r log SNR • Given {C(SNR)},define diversity and multiplexing gains asymptotically ‡Zheng/Tse 2002

16. Diversity/Multiplexing Tradeoffs Insert picture • Where do we operate on this curve? • Depends on higher-layer metrics

17. Bound on Total Distortion • We use the high SNR bound (finite blocks) • Equating source and channel rates and assuming high SNR (order terms negligible) • As SNR, equate exponents to minimize distortion

18. Asymptotic Distortion • With matching exponents, • The distortion is a simple function of SNR and the optimal diversity/multiplexing point • Asymptotically, leads to a familiar form Same form as Zheng/Tse Pe and rate formulas

19. Finite SNR • Convex problem • Solve using standard techniques • Solution shows how to best use antennas

20. Distortion vs. Multiplexing N=M=8 k>>T

21. Distortion vs. Multiplexing N=M=8k=O(T)

22. Joint Source/Channel Coding for Video • The same optimization framework applies to a broad set of source/channel codes • Progressive video encoding [Girod 2000], yields separable distortion DT=DS+DC • Linear space-time codes [Kuhn 2003] that can tradeoff diversity and multiplexing • Optimize # of antennas NU for multiplexing • Optimization is an integer program

23. Antenna Assignment vs. SNR Can extend framework to include delay

24. Extensions • Can also optimize throughput • Throughput approaches capacity at high SNR • Discontinuity at d=0 • Tradeoffs under retransmissions • Retransmissions add a third dimension of delay • Region obtained by Caire, Damen, ElGamal (IT’05) • Tradeoff optimization requires an end-to-end distortion measure that incorporates delay, rate, and Pe. • Multiplexing versus diversity tradeoffs in networks Multiplexing/Diversity/Delay Tradeoffs

25. 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

26. 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 Link layer

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

28. Ad-Hoc Networks • Peer-to-peer communications. • Fully connected with different link SINRs • No centralized control • Routing can be multihop. • Topology is dynamic.

29. Energy-Constrained Nodes • Each node can only send a finite number of bits. • TX 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. • Sleep modes can save energy but complicate networking. • 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.

30. 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 time division systems s.t. Joint work with S. Cui

31. Modulation Optimization

32. Minimum Energy Routing • Transmission and Circuit Energy Red: hub node Blue: relay only Green: 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

33. 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

34. Nodes close together can cooperatively transmit Form a multiple-antenna transmitter Nodes close together can cooperatively receive Form a multiple-antenna receiver Node cooperation can increase capacity, save energy, and reduce delay. Cooperative MIMO

35. Cooperative DPC best Cooperative DPC worst d=r<1 x1 x1 TX1 y2 x2 d=1 Capacity Gainvs Network Topology RX2 Joint work with C. Ng

36. Cross-Layer Design with Cooperation Multihop Routing among Clusters

37. Total Energy versus Delay(with rate adaptation)

38. 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

39. Energy-efficient estimation s21 Sensor 1 g1 s22 g2 Sensor 2 Fusion Center gK s2K Sensor K Different channel gain (known) Different observation quality (known) Minimize the Pk’s for a given distortion constraint

40. Digital v.s. Analog

41. Key Message Wireless ad hoc networks impose tradeoffs between rate, power/energy, and loss/delay The tradeoff implications for distributed control is poorly understood

42. Distributed Control over Wireless Links Joint work With X. Liu • Packet loss and/or delays impacts controller performance • There is little methodology to characterize this impact • Controller sampling determines data rate requirements • Network design and resulting tradeoffs of rate vs. loss and delay should be optimized for the controller performance

43. Shared Channel l3 l2 l1 Optimal Controller underPacket Losses x1 Disturbance x2 System • This structure is optimal for LQG control with no losses. • Under lossy observations, prove that the optimal controller is a modified Kalman filter and state feedback controller. • The controller adapts to packet delay and loss, and its error covariance is stochastic • System stability depends on l1, l2, andl3 • These throughput parameters depend on the network design. Lossy Channel Lossy Channel Lossy Channel Control Command Optimal State Estimator State Feedback Controller State Estimate

44. Cross-Layer Designof Distributed Control Controller parameters: performance index, sample period, controller design, etc. Application layer Network layer Routing, flow control, etc. MAC layer Bandwidth sharing through Medium Access Physical layer Modulation, coding, etc. • Network design tradeoffs (throughput, delay, loss) • implicit in the control performance index

45. Multiple System Example • Inverted Pendulum on a cart. • Two identical systems share the network. • Different weight matrices in the objective function. q q Actuator Actuator disturbance disturbance Cart Cart u Control force x (position) x (position) Discrete Time Controller Discrete Time Controller

46. Link Layer Design Tradeoffs(modulation, coding) Uncoded BPSK is optimal!

47. Iterative Cross Layer Design Example

48. Optimal vs. Heuristic Controller   

49. To Cross or not to Cross? • With cross-layering there is higher complexity and less insight. • Can we get simple solutions or theorems? • What asymptotics make sense in this setting? • Is separation optimal across some layers? • If not, can we consummate the marriage across them? • Burning the candle at both ends • We have little insight into cross-layer design. • Insight lies in theorems, analysis (elegant and dirty), simulations, and real designs.