rearchitecting wireless networks with phy layer components

Rearchitecting Wireless Networkswith PHY Layer Components

Romit Roy ChoudhuryAssistant Professor

1

A little bit about ourselves

2

Webpage http://synrg.ee.duke.edu

3

Our Research

PHY

MAC / Link

Network

Transport

Security

ApplicationLocalization

Software Radios

Home networksMobility

EnergyLocation Privacy

Interfaces

Collaborative Sensing

Interference Mgmt.

Wireless Networking(bottom up)

Mobile Computing(top down)

Rate Control

Smart Antennas

Mobile Computing

Location SensingPhysical and Logical Localization

Information TelescopeMobile Phones for Collaborative Sensing

MobiSys 08 MobiCom 09Infocom 09, 10MobiCom 10

Smart ContentContext-aware content and compression

MobiSys 10Hotmobile 11

Micro-mobilityGesture and activity recognition

MobiHeld 09

5

Wireless Networking

Wired + WirelessInfrastructure Assisted Wireless

MobiCom 09Hotnets 08

Cross-LayerPHY Informed Protocol Design

MobiCom 09, 10Hotnets 09, 10

NSDI 10

SleepWellWiFi Energy Management

In Submission

Out of BandSensor Assisted Wireless Networking

LANMAN 10

6

Today’s Talk

1. Time to Frequency2. AccuRate3. CSMA/CN

Context

Cross-Layer Systems

Mobile Computing

Closing Thoughts

1. Virtual Telescope2. Location3. PhonePen



NSDI 10

7

Context

8

Wireless Everywhere

9

Wireless usage increased by 25x in last 5 years

Cisco predicts 40x increase by 2013

Network outages a reality Major carriers forcing

customers to pay-per-byte

Skyrocketing Demands

FCC looking for 500 MHz spectrum by 2020 …But also calling for much better use of available spectrum

10

Problem is not of spectrum alone Under utilization of available spectrum a major problem

Significant leaps in achievable PHY capacity MIMO, OFDM, Coding, Beamforming …

Yet, this PHY capacity not visible to higher layers Inefficiencies in network design …

protocols … architecture

Capacity vs. Goodput

PHY Bitrate

LinkThroughput

11

The capacity-throughput gap is not new Researchers recognized need to share information across layers Cross layer approaches became popular

Cross layer optimization Several creative ideas … many analyzed and simulated

Layering too Restrictive?

Capacity

Throughput

12

The capacity-throughput gap is not new Researchers recognized need to share information across layers Cross layer approaches became popular

Cross layer optimization Several creative ideas … many analyzed and simulated

However, 2 deficiencies

Layering too Restrictive?

1. Lack of experimentation platform difficult to build practical working systems

2. Protocol designers untrained in communications cross layer ideas variants of originals uses some PHY layer info. Capacity

Throughput

13

Software Radios Software defined radios

Changing landscape of wireless systems

Protocol designers understanding PHY concepts, using them PHY community receiving feedback from practical systems

Full view of PHY layer enabling experimentation with holistic, unconventional ideas …

We intend to contribute here14

Our Goal:

Rearchitect wireless networks with full access to PHY layer capabilities

15

We instantiate our ideas through WiFi

However, the core ideas not specific to WiFi …choice of WiFi mainly from platform considerations

16

WiFiProtocol Structure

17

WiFi Structure

AP1 AP2

R1 R2

Packet for R1 Packet for R2

18

WiFi Structure

AP1 AP2

R1 R2

Random Backoff = 10

Random Backoff = 18

19

WiFi Structure

Time

AP1 AP2

R1 R2

Random Backoff = 10

Random Backoff = 18

AP1 = 10

AP2 = 1820

WiFi Structure

Time

AP1 AP2

R1 R2

RemainingBackoff = 0


AP1 = 0

AP2 = 821

WiFi Structure

Time

AP1 AP2

R1 R2

Transmit @ rate = r1

Channel Busy

AP1 = 0

AP2 = 8

Data

AP2 Waits

ACK

22

WiFi Structure

AP1 AP2

R1 R2

AP1 = 15

AP2 = 8

Data

AP2 Waits

ACK

NewBackoff = 15


23

WiFi Structure

AP1 AP2

R1 R2

AP1 = 7

AP2 = 0

Data

AP2 Waits

ACK



24

WiFi Structure

AP1 AP2

R1 R2

AP1 = 7

AP2 = 0

Data

AP2 Waits

ACK

Channel Busy Transmit @ rate r2

AP1 Waits

Data ACK

25

WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK

Transmit Channel Busy

AP1 Waits

Data ACK

Data

26

WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK

Channel Busy ACK NotReceived

AP1 Waits

Data✘Interference

27

WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK

Channel Busy Adjust Rate & Retransmit

AP1 Waits

DataInterference

✘

Data

28

WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK


AP1 Waits

DataInterference

✘

Data

29

WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK


AP1 Waits

DataInterference

✘

Data

Channel Wastage

30

WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK


AP1 Waits

DataInterference

✘

Data

Channel Wastage

Collision or Fading 31

WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK


AP1 Waits

DataInterference

✘

Data

Heuristic Rate SelectionChannel Wastage


WiFi Structure

AP1 AP2

R1 R2

Data

AP2 Waits

ACK


AP1 Waits

DataInterference

✘

Data

RedundancyHeuristic Rate SelectionChannel Wastage



34

PHY Layer Information


(OFDM, Constellation, Interference Cancellation, Correlation …)

35




Software Radios (USRP, WARP, SoRa)

36




Software Radios (USRP, WARP, SoRa)

Cross-Layered Network Systems37

1. Channel Wastage due to Randomized Backing off

38

Backoff

Data

AP2 Waits

ACK AP1 Waits

Data ACK

Data

Per packet backoff 35% of channel wastage at 54 Mbps.Worse at higher data rates.

39

Fundamentally,backoff is not a time domain operation …

its implementation has been in the time domain

40

Fundamentally,backoff is not a time domain operation …

its implementation has been in the time domain

We intend to break away,and implement backoff on the frequency domain

41

Frequency Domain 802.11a/g PHY adopts OFDM

Wideband channel divided into 48 narrow sub-carriers Copes better with fast, frequency selective fading Purely a PHY layer motivation

MAC Opportunity Pretend OFDM subcarriers are integers Emulate randomized backoff

Frequency

Subcarriers: 1 2 3 4 … 48

42

T2F: Main Idea Pick random backoff, say 6 Submit signal on 6th subcarrier

0 47

6

0 47

18

AP1 Backoff = 6 AP2 Backoff = 18

43

0 47

6

0 47

18

Listen Antenna

Listen Antenna

6 18 6 18

Pick random backoff, say 6 Submit signal on 6th subcarrier

T2F: Main Idea

AP1 Backoff = 6 AP2 Backoff = 18

AP2 learns some other AP is winner.AP1 learns AP1 is the winner … hence, AP1 transmits

44

Subcarrier0 1 2 3 4 5

Second Round

What if Collision?

Introduce a second round of contention Winners of first go to second


First RoundWinner

45

Why beneficial?

Avg. temporal backoff = 16 slots = 144 micro sec.

Frequency backoff = 1 OFDM symbol = 4 micro sec

2 rounds of backoff = 8 micro sec.

Possible to do better …

46


Second Round

Creating a Queue


First Round

Winner Rank 2

47


Second Round

Creating a Queue


First Round

0 2 40 2 4 0 2 4Rank 1 Rank 2 Rank 3 EnablingTDMA

48

Improved Channel Utilization

Data DataData Data

Data DataData Data

WiFi: Contention per packet

T2F: OFDM contention per TDMA schedule

TDMA

49

Multiple Collision Domains What happens with real-world scatterred APs

50


B waits for O, but O waits for G

G 1

O 2 W 5B 3

51



Protocol: Each node continuously carrier senses If node sees channel idle for DIFS, redo backoff

• New backoff = Old backoff – Backoff of past winner Else, if channel idle for PIFS duration

• Count ++• If Count = Rank, transmit

G 1

O 2 W 5B 3

52



T2F precisely emulates 802.11 Only every backoff is nearly instantaneous

G 1

O 2 W 5B 3

G 1 W 5 4 O 2

B 3 1

53

Performance 10 USRP Testbed

Deployed in Duke

Quantify Reliable subcarrier detection Collision probability Net throughput gain over WiFi

54

Subcarrier DetectionSN

R in

dB

FFT NumberReliable subcarrier detection at 14dB

55

Collision Probability

Small collision probability in dense networks

Benefit of second round

56

Throughput Gain

Throughput Gain of 27% at 36Mbps with 15 APs57

Closing Thoughts

Contention is not fundamentally a time domain operation T2F shows feasibility in frequency domain Long standing overheads of backoff alleviated

T2F is complimentary to MIMO Additional antenna amenable to T2F

T2F not specific to WiFi Powerline Ethernet runs OFDM and performs backoff T2F applicable in such scenarios too …

58

2. Sub-Optimal Rate Selection

59

Data @ rate R ACK

What is the optimal rate R?

After backing off, AP transmits packet at rate R

60

Estimate from the optimal rate of the previous packet


Data @ rate R ACK


61



Data @ rate R ACK

What is the optimal rate of the previous packet?


62


Data @ rate R ACK

What is the optimal rate of the previous packet?


More generallyGiven any transmission at some rate R, what would have been the max rate R*,

at which that transmission would have been successful


63

✦ Recently PHY-based:✦ SoftRate [SIGCOMM ’09]

• Uses a BER heuristic to estimate bit rate• BER accurately identifies when to increase/decrease rate• However, may not be able to jump to optimal rate

Current Wireless Rate Selection

Data

ACK

HistoryInfo. Data

SNR

Frame Based SNR Based

SampleRate, RRAA RBAR, CHARM

We dive deeper into PHY … jump to the optimal rate64

Quick Background:Symbols, Modulation, Bit-rate

65

2 bits together

01111001 ....

11

00 10

01

Tx 4QAM Symbol

Data =

PHY Layer Symbols

66

2 bits together

01111001 ....

Dispersion11

00 10

01

Tx 4QAM Symbol

Data =

11

00 10

01

Rx 4QAM Symbol

11

01

11

01

Channel

Decoding Symbols

67

Dispersion 11

00 10

01

Rx 4QAM Symbol

2 bits together

01111001 ....Data =

11

00 10

01

Tx 4QAM Symbol

Channel

Decoding Symbols

68

Tx 16QAM Symbol

4 bits together

01111001 ....Data =

0111

Channel

Rx 16QAM Symbol

Dispersion

2 bits together

01111001 ....Data =

11

00 10

01

Tx 4QAM Symbol

Channel

Dispersion 11

00 10

01

Rx 4QAM Symbol

Different Modulations in 802.11

69

6 bits together

01111001 ....

Tx 64QAM Symbol

011110

Data =

Rx 64QAM Symbol

Channel

Dispersion

2 bits together

01111001 ....Data =

11

00 10

01

Tx 4QAM Symbol

Channel

Dispersion 11

00 10

01

Rx 4QAM Symbol

Different Modulations in 802.11

70

High Dispersion

Data 01111001 ....=

Tx 16QAM Symbol

WeakChannel

Wrongly demodulated symbol

Weak Channel Induces Error

Picking the right modulation is a function of channel quality Excess dispersion induces decoding error

71

01

0

StrongChannel

ModerateChannel

WeakChannel

0111

In General

72

01

0

StrongChannel

ModerateChannel

WeakChannel

0111

6 Mbps

24 Mbps

36 Mbps Smaller dispersion permits higher rate

In General

73

AccuRate Hypothesis: Symbol dispersion is independent of modulation

Observation: Dispersion indicates the optimal rate that should have been used for that packet

74

Hypothesis Verification

11

00 10

01

Tx 4QAM

Tx 16QAM

Channel

11

00 10

01

Rx QPSK

Rx16QAM 75

McKinley et. al., 2004, “EVM calculation for broadband modulated signals”

Hypothesis Verification

76

Observe symbol dispersion and select optimal modulation

Hypothesis: Symbol dispersion is independent of modulation

77

DataBPSK

4QAM

16QAM

78

DataBPSK

4QAM

16QAM

79

BPSK

4QAM

16QAM

Data

We call this Virtual Channel Replay

80

Channel Replay Vector

d1 Vector V = {d1, d2, ...., dn}d2

81

Receiver

Demodulator

Packet

Best Rate

BPSK Channel Replay

Demodulator CRCCheck

4QAM Channel Replay


16QAM Channel Replay


82

Optimal modulation ≠ Optimal rateBit-rate is a function of

both modulation and coding

Need to find the optimal <modulation,coding> for a received packet?

83

Receiver

Demodulator

Data BPSK Channel Replay1/2 Demodulator

CRC CheckDecoder

BPSK Channel Replay3/4 Demodulator

CRC CheckDecoder

QAM4 Channel Replay1/2 Demodulator

CRC CheckDecoder


CRC CheckDecoder

18 Mbps

Decoder

6 Mbps

9 Mbps

12 Mbps


CRC CheckDecoder

Best Rate

54 Mbps

84

Packet Failure

Packet failure implies incorrect dispersion vector

All packets have globally known preamble/postamble Used for detection and synchronization to new signals

Compute the replay vector for the preamble alone Precise dispersions known for this vector Replicate this vector to model the complete dispersion vector

Vector V’ = {d1, d2, ...., dn}d1

d2

e2 e2

Cannot replay this vector, V’

85

AccuRate needs to discriminate interference from collision

Rate selection needs to be independent of interference

86

How to Detect Interference?

Interference causes substantial symbol dispersion Reliable indicator

With InterferenceWithout Interference

87

How to Detect Interference?

Interference starts first: Preamble with high dispersion

Interference starts second: Postamble with high dispersion

Compare preamble with postamble dispersion

Data

DataInterference

Interference

88

Performance Evaluation

Used 802.11 like Tx and Rx design on USRP/GnuRadio Modulation: BPSK, QPSK, 16QAM, 64QAM Coding: Convolution coding with puncturing with rate 1/2, 3/4 Compare with Softrate, SNR-based

Testbed 10 traces at walking speed Trace based evaluation

Simulation Characterize AccuRate’s performance under high mobility Raleigh fading channel simulator ported to GnuRadio

RealChannel

Simulator

89

What is the True Optimal Rate?

Testbed Using train of packets (virtual packet) Virtual packet composed of train of small packets

• Each short packet at increasing rate

Virtual Packet 6Mbps 9Mbps 12Mbps 18Mbps 24Mbps 36Mbps 54Mbps

OptimalOptimal -1 Optimal+1

90

Rate Estimation Accuracy

AccuRate

Incorrect rate selection ~ 4%

For correctly received packets,

100% in Simulation,95% in Testbed

91

Interference Detection Accuracy

Detection Accuracy better at higher rates (91%)

Testbed

92

Overall Rate Selection Accuracy

93% accuracy in optimal rate selection

Correct

Overselect

Underselect

Testbed

93

AccuRate achieves 87% of the optimal throughput

Testbed

Throughput at Walking Speeds

94

3. Collision Detection in Wireless Networks

95

Data ✘

Transmitter infers a collision from the absence of an ACK.Prepares to retransmit the packet.

Interference Data ACK

96

Data Data✘

Bits transmitted unnecessarily

ACK

Interference

Bits transmitted redundantly

Undesirable Channel Wastage

97

Ethernet is Efficient

Called Collision Detection (CSMA/CD)

Collision

Transmitter detects a collision,and immediately aborts transmission.

Unfortunately, CSMA/CD not feasible in wireless networks …98

We ask: Can we emulate CSMA/CD in wireless networks

i.e., abort collisions right when they occur

99

MAC

PHY

MAC

PHY

Data Transmission (S1)

S=S1

Tx

Rx

Cros

s La

yer

Cros

s La

yer

CSMA/CN: Basic Idea

100

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

CollisionDetected

Cros

s La

yer

Cros

s La

yer

CSMA/CN: Basic Idea

101

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

Abort signaldetected

Abort Signal (S2)S=S1+S2

Cros

s La

yer

Cros

s La

yer

Collision.Send Abort

CSMA/CN: Basic Idea

102

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

ABORT


Cros

s La

yer

Cros

s La

yer


CSMA/CN: Basic Idea

103

3 Challenges

1. Detect collision in real time at the receiver2. Detect notification at the transmitter3. Design protocol to ensure correct transmissions aborted

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

ABORT


Cros

s La

yer

Cros

s La

yer


104

Wired

Wireless

Interference Cancellation Need to detect very weak notification signals

Opportunity Pass the Tx signal over wire Listen antenna has 2 copies

of the Tx signal

Both copies have same filter and frequency offset effects

Align the two signals using sampling offset information Subtract the wired signal from wireless

Correlate residue with collision notification

105

Collision Detection at Rx Receiver detects collision within 20 bytes Total turnaround time for CN signature 18us

Quicker turnaround Faster Tx abortion

Throughput gain over PPR

MAC

PHY

Median gain = 25%

106

In Closing …

Its not adequate to only use PHY layer information for higher layer protocols

Need to rethink protocol design holistically(with PHY layer capabilities folded into it)

We are trying to design such holistic systems, while respecting the lessons learnt from layering

107

Our Research

PHY

MAC / Link

Network

Transport

Security

ApplicationLocalization

Software Radios

Home networksMobility

EnergyLocation Privacy

Interfaces

Collaborative Sensing

Interference Mgmt.

Wireless Networking(bottom up)

Mobile Computing(top down)

Rate Control

Smart Antennas

Mobile Computing







MobiHeld 09

109

Mobile Computing


MobiCom 09Infocom 09, 10MobiCom 10




MobiHeld 09


MobiSys 08

110

Next generation mobile phones will havelarge number of sensors

Each phone may be viewed as a micro lensExposing a micro view of the physical world to the Internet

111

With 4.1 billion active phones in the world today

(the fastest growing computing platform …)

Our Vision is …

112

Internet

A Virtual Information Telescope

113

Free WiFi?

114

People

Virtual Information Telescope

Location Energy Privacy Interfaces . . .. . .

Apps

Research

115

Mobile Computing


MobiSys 08




MobiHeld 09


MobiCom 09Infocom 09, 10MobiCom 10

116

RadioShackStarbucks

Physical LocationError

The dividing-wall problem117

Its possible to localize phones by sensing the ambience

such as sound, light, color, movement, orientation…

Hypothesis

118

Mobile Computing





MobiHeld 09



119

Can phones automatically create video highlights of a social gathering?

120

Goal

Envisioning the end product Imagine a social party of the future Assume phones are wearable

GOAL: Create automatic video highlights of the occasion

The Idea: Mobile phones sense ambience Collaboratively infer an “interesting event” Select phone with good view of the event Stitch the recorded clips to form the highlights

Nokia Morph

MicrosoftSenseCam

AppleIPod Nano

121

Mobile Computing







MobiHeld 09

122

PhonePoint Pen Using phone accelerometers

To write short messages in the air

123

PhonePen words

124

See PhonePoint Pen YouTube Video at

http://synrg.ee.duke.edu/media.htm

125

Thank You

Visit our Systems Networking Research Group (SyNRG)

Google “SyNRG”

126

Ordered Transmissions Each node has global view of backoff values

Computes its rank among contending APs Enables TDMA

470 12

Rank in TDMA: 3AP1

Self Backoff

Other’s Backoff

128

App Store 2020 OmniSearch

Is there live music at the plaza now? Do people around 9th street feel safe?

LiveLearn Learn about Mt. Everest via videos from base camp, interact with Sherpas Zoom into a location, and replay the history from any time in the past

VirtualWindShield Content overlayed on vehicle windshield … show street view … speed limit

BlindSight Sensors “see” the surrounding and speaks into the ears of blind people

MyLife’sImportantBits Automatic video highlights of your everyday life

AirWrite, MindWrite Write SMS in the air … send SMS by thinking

129

Beyond Location, Energy, Privacy …

Humans part of the end device 3 eyes (2 human and 1 camera) … 2 CPUs (1 Intel and 1 brain)

Activity, emotion, and intent recognition Feasible from the ability to sense and mine multi-dimensional data

streams Extend to vision / cognition

Information distillation Pulling out the “signal” from the noise

Agile networking Sensor assisted wireless communications

User interface Intuitive, implantable, cognitive

130

Caveat:

We are not innovating in communications.

We are adopting well known techniques to rearchitect wireless networks

131

We Ask:

If network protocols were designed with joint PHY and higher layer information,

would they be the same as what we have today?

The answer is: No

132

We Ask:

If network protocols were designed with joint PHY and higher layer information,

would they be the same as what we have today?

The answer is: No

This talk explains why not, and carves out opportunities of improvement.

133

134

Problem is not of spectrum alone Significant leaps in achievable PHY capacity MIMO, OFDM, Coding, Beamforming …

This PHY capacity not visible to higher layers Inefficiencies in network design … architecture

Capacity vs. Goodput

WiFi Structure

Time

AP1 AP2

R1 R2

Random Backoff = 10

Random Backoff = 18

AP1 = 10

AP2 = 18135

Today’s Talk


Context

Cross-Layer Systems

Mobile Computing

Closing Thoughts

1. Time to Frequency2. AccuRate3. CSMA/CN

1. Virtual Telescope2. Location3. PhonePen


NSDI 10

136

Backoff

Data

AP2 Waits

ACK AP1 Waits

Data ACK

Data

137

138

Overwhelming Adjacent Subcarrier

256 Point FFT

139

Solution: Use a Higher Point FFT

Subcarrier Number

SN

R (d

B)

SN

R (d

B)

-10 -5 0 5 10 Subcarrier Number

70

60

50

40

30

20

10

0

512 point FFT

1024 point FFT

Reliable subcarrier detection at 15dB

Data

140

Subcarrier Detection Accuracy

141

Dete

ctio

n Ac

cura

cy

Distance in Subcarriers

Reliable subcarrier detection at 14dB

Simulation Testbed

Rate Estimation Accuracy

For correctly received packets,

100% in Simulation,95% in Testbed 142

143

Practical Requirements?

Collision

Transmitter cannot detect collision Receiver needs to detect it

Receiver needs to convey Collision notification to the transmitter

Transmitter needs an additional antenna To receive the notification

Collision Notification

144

CSMA/CN Summary CSMA/CN imitates CSMA/CD in wireless

Aborts collision upfront, instead of recovering from it (PPR)

Correlation is the key Enough for 1 bit feedback

2 important future directions How can protocols be redesigned with such few bit feedbacks Can we move from correlation to decoding (full duplex)

Prevention Better than Cure

145

Practical Challenge: High Self Signal

AccuRate performs well even under high mobility

Under Varying Mobility

146

AccuRate estimates the optimal rate for an already received packet

What is the performance if the next transmission uses this rate?

147

Distance

Signalpower

Collision

Thus, wireless networks must perform less efficient CSMA/CA148

MAC

PHY

MAC

PHY

Tx

Rx

Cros

s La

yer

Cros

s La

yer

We propose CSMA/CN

149

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

Check forCollision

Cros

s La

yer

Cros

s La

yer

CSMA/CN: Basic Idea

150

MAC

PHY

MAC

PHY Cros

s La

yer


S=S1

Tx

Rx

Check forCollision

Search forAbort

Cros

s La

yer

CSMA/CN: Basic Idea

151

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

Search forAbort

Cros

s La

yer

Cros

s La

yer

Check forCollision

CSMA/CN: Basic Idea

152

MAC

PHY

MAC

PHY


S=S1+S2

Tx

Rx


Search forAbort

Abort Signal (S2)

Cros

s La

yer

Cros

s La

yer

CSMA/CN: Basic Idea

153

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

Abort signaldetected


Cros

s La

yer

Cros

s La

yer


CSMA/CN: Basic Idea

154

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

ABORT


Cros

s La

yer

Cros

s La

yer


CSMA/CN: Basic Idea

155

156

3 Challenges1. Detect collision in real time at the receiver2. Detect notification at the transmitter3. Design protocol to ensure correct transmissions aborted

MAC

PHY

MAC

PHY


S=S1

Tx

Rx

ABORT


Cros

s La

yer

Cros

s La

yer


Challenge 1Data Data

Correlate for Preamble + SoftPHY hints

Detect collision in real time

Main Idea Receiver correlates for preamble If preamble present

Continuously monitor symbol level confidence (SoftPHY)

Achieves 96% collision detection accuracy at the receiver157

Challenge 1Data Data

Correlate for Preamble + SoftPHY hints

Detect collision in real time

Main Idea Receiver correlates for preamble

If preamble present Continuously monitor symbol level confidence (SoftPHY)

Confidence (symbol) = Log likelihood ratio (symbol) BER = Avg. (Confidence (symbol) If BER_i > 3*BER_(i-1) Announce Collision

Achieves 96% collision detection accuracy at the receiver158

Challenge 2 Detect collision notification

In the same frequency channel

Main Idea No need to decode the abort signal correlate for signature

Data

Abort

159

Challenge 2 Detect collision notification

In the same frequency channel

Main Idea No need to decode the abort signal correlate for signature

Data

Abort

Cor

rela

tion

Sample Number

Works when notification is no weaker than 18dB of self-signalCorrelation spikes whenever notification arrives

160

Wired

Wireless

Challenge 2 Need to detect very weak notification signals

Opportunity Pass the Tx signal over wire Listen antenna has 2 copies

of the Tx signal

Both copies have same filter and frequency offset effects

Align the two signals using sampling offset information Subtract the wired signal from wireless

Correlate residue with collision notification

161

Reliable notification detection until 34dB below

(Self Signal) - (Notification Signature)(dB)

Fr

actio

n of

False

+/-

False positivesFalse negatives

Challenge 2 Performance with cancellation

162

163

Challenge 3: Protocol Design

Data Data

T1

R

Collision

T2

R2R1

Signal starts before interference

164

Data Data

R

Correlate (Sign(R1))

Sign(R1) Sign(R2)

Collision

T1T2

R2R1

Signal starts after interferenceChallenge 3: Protocol Design

Data Data

R

Sign(R1)

Corr (Sign(R1))

Notification!Stop Tx

Collision

T1T2

R2R1Correlate (Sign(R1))

Sign(R1) Sign(R2)

Signal starts after interference Notification aborts the correct transmission

Challenge 3: Protocol Design

165

166

Performance Evaluation 10 node USRP testbed BPSK, QPSK modulation Signature size: 20 bytes Topologies with three links doing CSMA/CN Compare with 802.11 and PPR

PPR retransmits only suspected bits of the packet

167

Collision Detection at Rx Receiver detects collision within 20 bytes Total turnaround time for CN signature 18us

Quicker turnaround Faster Tx abortion

Throughput gain over PPR

MAC

PHY

Median gain = 25%

168

CSMA/CN Summary

CSMA/CN imitates CSMA/CD in wirelessAborts collision upfront, instead of recovering from it (PPR)

Prevention Better than Cure

169

In Closing …

People

Virtual Information TelescopeFrontend

Backend

170

Location Sensing

171

Smart Content

172

User Interfaces

173

Some Other Projects

174

Wired + WirelessInfrastructure Assisted Wireless

MobiCom 09Hotnets 08

SleepWellWiFi Energy Management

In Submission

WiFi as sensorsSleepWellHome networksSAWCSmart antennas

Wide agreement on the need for cross layer systems Exploit information from other layers Optimize protocols/networks

Not a new idea Discussed for several decades Variety of creative thought experiments Several ideas developed and simulated …

Layering Too Restrictive?

175

Yet, cross-layer systems have not made itto the main streamThe PHY and MAC layer gap remains …

even widens as each area advances independently

The MAC layer operates at a coarser granularity than PHY PHY: Signals, bits MAC: packets, frames

Wireless networks have been designed on this principle Data and Control … both use packets

• Even 1 bit feedback, takes up a discrete resource unit

Cross-layer systems allow flexible granularity Control information can be at the granularity of bits/signals Data communication at the granularity of packets/bandwidth

Efficient, streamlined wireless systems

In Closing …

176

rearchitecting wireless networks with phy layer components

Documents

wireless networkinglanman

layerscross layer approaches

achievable phy capacitymimo

notion of cross

network outages

network designers

recognized need

capacitythroughput gap