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

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 Transport Network Access Link
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 Disadvantages
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
Interdisciplinary research, design, and development very challenging, but necessary to meet the requirements of future wireless applications
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. Joint S/C Coding with MIMO
Use antennas for multiplexing
Use antennas for diversity
High-Rate
Quantizer
ST Code
High Rate
Decoder
Error Prone
Low Pe
Low-Rate
Quantizer
ST Code
High
Diversity
Decoder

15. Diversity/Multiplexing Tradeoff 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=8 k=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 Multiplexing/Diversity/Delay Tradeoffs
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
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

26. Cross-layer protocol design for real-time media
Loss-resilient source coding and packetization
Application layer
Rate-distortion preamble
Congestion-distortion optimized
scheduling
Transport layer
Congestion-distortion optimized
routing
Traffic flows
Network layer
Capacity assignment for multiple service classes
Link capacities
MAC layer
Link state information
Adaptive
link layer techniques
Link layer

27. Video streaming performance
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.
-No backbone: nodes must self-configure into a network.
-In principle all nodes can communicate with all other nodes, but multihop routing can reduce the interference associated with direct transmission.
-Topology dynamic since nodes move around and link characteristics change.
-Applications: appliances and entertainment units in the home, community networks that bypass the Internet. Military networks for robust flexible easily-deployed network (every soldier is a node).

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.
All the sophisticated high-performance communication techniques developed since WW2 may need to be thrown out the window.
By cooperating, nodes can save energy

30. Cross-Layer Optimization Model
Min
s.t.
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
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 4 3 2 1
(0,0)
(5,0)
(10,0)
(15,0)
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 4 2
0.185 3
0.115 1
(0,0)
(5,0)
(10,0)
(15,0)
0.515
• Optimal routing uses single and multiple hops
• Link adaptation yields additional 70% energy savings

34. Cooperative MIMO 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.

35. Capacity Gain vs Network Topology
TX1 x1 x2
d=1
d=r<1
Cooperative DPC best
Cooperative
DPC worst
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
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
Why doesn’t power associated with the fundamental limit in digital decrease with D_0?

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. Optimal Controller under Packet Losses
Shared
Channel l1 l2 l3 x1 x2
Disturbance
System
Lossy
Channel
Lossy
Channel
Lossy
Channel
Control Command
Optimal State
Estimator
State Feedback
Controller
State Estimate
• 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, and l3
• These throughput parameters depend on the network design.
One of our main contributions is considering control packet losses.
Also, in time division the transmissions don’t interfere.
Centralized control.
Assume controller knows if a control command is received and the corresponding execution time.
Controller knows the delay distribution of control commands and sensor data sent over the network
With these assumptions, we prove that separation of the controller and state estimator is optimal.

44. Cross-Layer Design of Distributed Control
Application layer
Controller parameters: performance
index, sample period, controller design, etc.
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
Codebook
Modulation
Heuristic
Optimal
(7,7)
BPSK
7.03
6.77
(15,15)
5.77
(31,16)
5.84
(31,11)
5.91
QPSK
5.88
5.53
5.72
5.52   

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.