TAPs: An Architecture and Protocols for a High-Performance Multi-hop Wireless Infrastructure

Ed Knightly

ECE/CS Departments

Rice University

http://www.ece.rice.edu/~knightly

Joint work with

V. Kanodia, A. Sabharwal, and B. Sadeghi

Ed Knightly

The Killer App is the Service

High bandwidth High availability

– Large-scale deployment– High reliability– Nomadicity

Economic viability

Why?– Broadband to the

home and public places

– Enable new applications

Ed Knightly

WiFi Hot Spots?

Why? poor economics– High costs of wired infrastructure ($10k + $500/month)– Pricing: U.S. $3 for 15 minutes; CH: 0.90 CHF/minute– Dismal coverage averaging 0.6 km2 per 50 metro areas

projected by 2005

11 Mb/sec, free spectrum, inexpensive APs/NICs

Carrier’s Backbone/Internet

T1

Medium bandwidth (wire), sparse, and expensive

Ed Knightly

Cellular?

Cellular towers are indeed ubiquitous– Coverage, mobility, …

High bandwidth is elusive– Aggregate bandwidths in Mb/sec range, per-user

bandwidths at dial-up speeds– Expensive: spectral fees and high infrastructure costs

High availability, but slow and expensive

Ed Knightly

Ad Hoc Networks?

Availability– Problems: intermediate nodes can move, power off,

fade, DoS attack routes break, packets are dropped, TCP collapses, …

Low bandwidth– Poor capacity scaling– Unlike cellular, users consume wireless resources at

remote locations

“Free” but low availability and low bandwidth

Ed Knightly

TAPs: Multihop Wireless Infrastructure

Transit Access Points (TAPs) are APs with – beam forming antennas – multiple air interfaces– enhanced MAC/scheduling/routing

protocols Form wireless backbone with limited

wired gateways

Ed Knightly

Multihop Wireless Infrastructure

Transit Access Points (TAPs) are APs with – beam forming antennas – multiple air interfaces– enhanced MAC/scheduling/routing

protocols Form wireless backbone with limited

wired gateways

High bandwidth – High spatial reuse and capacity scaling – Opportunistic protocols

High availability– Redundant paths and non-mobile infrastructure– Deployability

Good economics– Unlicensed spectrum, few wires, exploit WiFi components

Ed Knightly

Prototype and Testbed Deployment

FPGA implementation of enhanced opportunistic, beamforming, multi-channel, QoS MAC

Build prototypes and deploy on Rice campus and nearby neighborhoods

Measurement study from channel conditions to traffic patterns

Ed Knightly

Outline

TAP architecture

OAR: an opportunistic auto-rate MAC

MOAR: multi-channel OAR

Open problems

Ed Knightly

0

5

10

15

20

25

30

35

40

126 51 76

101

126

151

176

201

226

251

276

301

326

351

376

401

426

451

476

Time (sec)

SNR

Received signal: superposition of different reflections, with different delays and attenuations

Motivation

Wireless channel is variable

Coherence time chan

nel

gai

n

time

Ed Knightly

Opportunistic MAC Goal

Constraint: distributed random access protocol

Exploit the variations inherent in wireless channel to increase throughput Maintain fair temporal shares for different flows

chan

nel

gai

n

time

user 1

user 2

user 1

user 2

Ed Knightly

IEEE 802.11 Multi-rate

Support of higher transmission rates in better channel condition– 802.11b available rates: 2, 5.5, 11 Mbps

– 802.11a available rates: up to 54 Mbps

Auto Rate Fallback (ARF)– [Monteban et al. 97]– Use history of previous transmissions to

adaptively select future rates

Ed Knightly

Temporal vs. Throughput Fairness

Equivalent in single-rate networks Throughput fairness results in significant inefficiency

in multi-rate networks

Example

user 1

user 2

access point

user 3

Ed Knightly

Temporal vs. Throughput Fairness

Equivalent in single-rate networks Throughput fairness results in significant inefficiency

in multi-rate networks

Exampleuser 1

user 2access point

user 3

Throughput Fair

user 1

user 2 DATA

DATA

user 3 DATA

Even 1 user with low transmission rate results in

a very low network throughput

Ed Knightly

Temporal vs. Throughput Fairness

Equivalent in single-rate networks Throughput fairness results in significant inefficiency

in multi-rate networks

Exampleuser 1

user 2access point

user 3

Temporal Fair

user 1

user 2 DATA

DATA

user 3 DATA

DATA DATA DATA

DATA DATA DATA

DATA DATA

DATA DATA

Same time-shares of the channel for different flows, also higher throughput

Ed Knightly

Opportunistic MAC

Goal Exploit short-time-scale variations inherent in

wireless channel to increase throughput in wireless ad hoc networks

Issue Maintaining temporal share of each node

Challenge Channel info available only upon transmission

Ed Knightly

Opportunistic Auto Rate (OAR)

Observation: coherence time on order of multiple packet transmission times– measure channel quality on RTS/CTS handshake– hold good channels for multiple transmissions

Ensure fairness by scaling number of packets transmitted to channel quality– # packets = Current rate / Base rate– with random access, all flows equally likely to

access channel

OAR: High throughput, while maintaining temporal fairness properties of single rate IEEE 802.11

Ed Knightly

RTS

RBAR Protocol

source

destination

ACKCTS

Receiver Based AutoRate (RBAR)

DATA

Receiver controls the sender’s transmission rate

Control messages sent at Base Rate

Reservation Sub-Header

Ed Knightly

DATA DATARTSACKCTSACK

OAR Protocol

source

destination

Pkts Rate Pkts Rate Pkts Rate

802.11 1 2 1 2 1 2

802.11b 1 2 1 5.5 1 11

OAR 1 2 3 5.5 5 11

Protocol

Channel Condition

BAD MEDIUM GOOD

OAR

Once access granted, it is possible to send multiple packets if the channel is good

Reservation Sub-Header

Ed Knightly

Observation I Time spent in contention per packet is the same for

RBAR and single-rate IEEE802.11Transmitter

Receiver

OAR

Performance Comparison

IEEE 802.11

R

C A

D1Transmitter

Receiver

R

C A

D1 R

C A

D1Transmitter

Receiver

RBARObservation II OAR contends for the same total time as singe-rate

IEEE 802.11 but transmits more data

R

C A

D1

A

D2

A

D3

Ed Knightly

Transmitter

Receiver

OAR

Performance Comparison

IEEE 802.11

R

C A

D1Transmitter

Receiver

R

C A

D1 R

C A

D1Transmitter

Receiver

RBAR

Observation III OAR holds high-quality channels for multiple

transmissions

R

C A

D1

A

D2

A

D3

Ed Knightly

Analytical Model

Challenge: MAC and channel are random processes with memory

Model relates physical-layer characteristics to MAC throughput:– Time spent in contention

Markov model of modified IEEE 802.11– Average transmission rate

Due to channel distribution

Comparative model b/t multi-rate OAR and tractable systems– TIME: OAR contends as often as single-rate IEEE 802.11 with

increased data per contention– PACKETS: OAR reduces packet transmission time via per-

contention rate adaptation

Ed Knightly

Simulation Results Under Ricean Fading

OAR has 42% to 56% gain over RBAR Increase in gain as number of flows increases Model predicts OAR & RBAR throughput to within 7% accuracy

Nodes

ProtocolPackets Transmitted

with 11 Mbps (%)

OAR 80%

RBAR 65%

Ed Knightly

Outline

TAP architecture

OAR: an opportunistic auto-rate MAC

MOAR: multi-channel OAR

Open problems

Ed Knightly

– Example: at 2.4 GHz WiFi, 5 vs. 1-3 MHz

– Figure for Ricean, K=4

Multi-Channel Problem Formulation

Observe: for two MUs, quality of different channels can have low correlation if

channel separation >> coherence bandwidth

Ed Knightly

Challenge

Ideal protocol is simple: select the best channel at the instant of transmission

In practice, channel qualities are unknown a priori– Must first transmit and measure

Cost of measuring channels must be balanced with benefits of finding good ones

Ed Knightly

MOAR Protocol Sketch

Measure channel SNR at RTS/CTS handshake

If channel quality is high (above an SNR threshold), transmit via OAR

If channel quality is poor, skip to a new channel– next channel piggybacked in CTS

Design optimal stopping rule for skipping– stop when throughput gain of skipping to a better

channel is outweighed by overhead

Ensure fairness

Ed Knightly

Optimal Stopping Rule Formulation

Let Xn denote the SNR of the nth measured channel

Let c denote the cost (in time) of measuring the channel

After observing Xn transmit or measure again?– cannot go back to previous channel (coherence time)

The reward for the nth selection is Xn-nc – after scaling SNR to rate and then to time

Objective: maximize the expected reward

In a class of stopping rule problems (without recall)

Ed Knightly

Optimal Stopping Time

Let V* denote the expected return from the optimal stopping rule

Suppose pay c and observe X1= x1

If continue, x1 is lost and c is paid– continuing, can obtain return V*, but not more– start afresh

Optimal rule is threshold based– If xn < V*, continue; if xn > V* stop– N* = min{n 1: Xn V*}

Ed Knightly

Calculating the Stopping Threshold V*

V* = E max(X1,V*) – c

– F(x) represents the SNR distribution

Compute V* – channel model and parameters (ex. K, d)– system’s rate-SNR thresholds (ex. 1, 2, 5.5, 11)

cxdFVxV

*)(*)(

Ed Knightly

MOAR Throughput Gains

Ricean parameter K = 0 is no line-of-sight signal

Gains of 40%-60% increasing with K and SNR variance

Ed Knightly

Effect of Node Distance

Greatest help when far away Non-monotonic due to rate-SNR thresholds

Ed Knightly

Random Topologies

Nodes are uniform-randomly placed in a 250m circle

“Optimal Skipping” cheats: looks at all channels (with no cost) and jumps to the best

Observe– MOAR extracts most

available gain– close-by nodes detract

from average gain

Ed Knightly

Outline

TAP architecture

OAR: an opportunistic auto-rate MAC

MOAR: multi-channel OAR

Open problems

Ed Knightly

DoS Resilience and Security

Old methodology– Design a network protocol– Optimize for performance– Discover DoS/Security holes

Ex. Route query floods

– Patch one-by-one

Challenge– DoS-resilience and security as the foundation of

network protocols– Recognize these issues are as important as

performance

Ed Knightly

TAP Media Access and Scheduling

Challenge: distributed scheduling– Others’ channel states, priority, & backlog condition unknown

Ex. TAP A’s best recv’r may be transmitting elsewhere Ex. Traffic to be recv’d may be higher priority than that to be sent

– Traffic and system dynamics preclude scheduled cycles– Modulate aggressiveness according to overheard information

Ed Knightly

Multi-Destination Routing/Scheduling

Most data sources or sinks at a wire Routing protocols for any wire abstraction Scheduling

– At fast time scales, which path is best (channels, contention, …) now?

– Can delay/throughput gains be realized despite TCP?

Ed Knightly

Distributed Traffic Control

Distributed resource management: how to throttle flows to their system-wide fair rate?– Throttle traffic “near-the-wire” to ensure fairness

and high spatial reuse– TCP cannot achieve it (too slow and RTT biased)– Incorporate channel conditions as well as traffic

demands

Ed Knightly

Capacity Driven Protocol DesignProtocol Driven Capacity Analysis

Traditional view of network capacity assumes zero protocol overhead (no routing overhead, contention, etc.)

Protocols themselves require capacity A new holistic system view: “the network is the channel”

– Incorporate overhead in discovering/measuring the resource– Explore capacity limits under real-world protocols

Ed Knightly

Problem: Multiple APs/TAPs/…within Radio Range

PHY Interference has disproportionate throughput degradation at MAC layer

Interference can lead to severe scaling limitations and starvation (worse than zero-sum game)

Ed Knightly

Summary

Transit Access Points– WiFi “footprint” is dismal– Removing wires is the key for economic viability

Opportunistic Scheduling (OAR/MOAR)– Exploit time and frequency diversity

Challenges– Multi-hop wireless architectures– Distributed control– Scalable protocols