QUALCOMM Proprietary & Confidential FlashLinQ: A Clean Slate Design for Ad Hoc Networks Xinzhou Wu May. 4 th, 2010

QUALCOMM Proprietary & Confidential

FlashLinQ: A Clean Slate Design for Ad Hoc Networks

Xinzhou Wu

May. 4th, 2010


Qualcomm CR&D at Bridgewater

• Flarion Technologies Inc founded in 2000 as a Bell Labs spin-off by Dr. Rajiv Laroia

• Acquired by Qualcomm on January 18, 2006

• Systems team lead by Dr. Tom Richardson, VP Eng.

• Innovative workforce - enhancing Qualcomm patent portfolio

– 53 patents awarded for OFDMA innovation

– 315 additional patents filed and pending

• 11 years of innovative OFDMA products and technologies

• FLASH-OFDM® - first fully mobile commercial OFDMA system

• Current major projects include:

– Femtocell Station Modem (FSM) SoCs

– FLASH-OFDM ®

– FlashLinQ

2


FlashlinQ – Direct Device-to-Device Communication Technology

Over Licensed SpectrumWithout Infrastructure Support

3


Where We are Today

• Wireless– WAN

• 1G – Analog voice• 2G – Digital voice• 3G/4G – Broadband data/voice• No notion of physical location or proximity

– LAN• WiFi• Bluetooth• Ad hoc networks (WiFi P2P mode)

• Wired– Ethernet – local– Internet

• Global• No notion of physical location or proximity

We Are Social Beings That Interact With The Physical World Around Us

4


QUALCOMM Proprietary and Confidential

5


Proximate Internet


6


Autonomous Advertisements…

Mobile Notary Public

Courier: for Hire

Local Seamstress

School: Polling Place

Taxi: for Hire -> Heading to NYC, need a ride?

Grocer -> ½ off Salami


7


Good to know Johnny is near home

A Family out for the day

A School Field Trip

The “Neighborhood Watch” Cmte

Discovering what one cares about nearby…


8


Communicating with it…

“Multi-player” Neighborhood Gaming

“Media Swap”

“Proximate Context-aware Gaming”

Mobile Social Network “Profile Matching”

In-building Automation Control

“Vouch” – building 3rd-party Trust Nets

“FlashPay” – eCash between eWallets


9


Applications of Proximate Internet

• Social networking– Discover friends in the vicinity

– Find people that share common interests

• Mobile advertizing– Neighborhood stores – products & services

– People offering services

• Remotely control devices around you

• …


Need for Proximate Internet

• Proximate Internet complements the Internet, does not replace it

• Mobile/fixed ‘devices’ communicate with nearby mobile/fixed ‘devices’ – Think of devices as ‘higher layer entities’ such as applications or services

• Location based services over 3G networks– Mobile-to-fixed (could also be mobile-to-mobile)

• Bluetooth based proximate services– File/content sharing – mobile-to-mobile– Local advertising – mobile-to-fixed

• WiFi based in home services– Apple devices using Bonjour – mobile-to-fixed or fixed-to-fixed

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Requirement for Proximate Internet

• Peer Discovery -- establishing need to communicate– Devices (application) discover all other devices within range (upto ~ mile)

• Capable of discovering thousands of devices

• Identify only authorized devices (privacy maintained)

– Automatic power efficient discovery without human intervention

• Paging – initiating communication– Link established through paging

• Communication– Once link established, devices can securely communicate

– All pairs that can coexist communicate simultaneously• Orthogonalization/reuse tradeoff - high system capacity

12


Outline

• Motivation: proximate internet – internet aware of the physical proximity

• FlashLinQ peer discovery solution – PHY: 5x range improvement and 40x energy efficiency improvement over WiFi

– MAC: greedy MAC protocol achieves close-to-optimal performance in dense deployment

• FlashLinQ traffic scheduling slultion– Fully distributed SINR based scheduling protocol

– 10x spectrum efficiency improvement over WiFi

13


Technical Challenges in Peer Discovery Design

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• Autonomous and continuous: peer discovery should happen without manual intervention

• Energy-efficient: low processing power to achieve decent stand-by time

• Standby time for 802.11 is around 7 hours

• Long range: each device need to discover peers far away

• 802.11 transmissions can only reach 200m

• Scalable: each device to monitor a large number of entities of interest in a dense network; graceful performance degradation as density increases

• Spectrally-efficient: minimum signaling overhead to allow simultaneous advertisements by large number of devices

• Many others: Secure, open and flexible, intelligent (application-defined timing and semantics) and dynamic (variation due to device mobility or user/application interactions)


FlashLinQ Peer Discovery Solution – Operation

• Synchronized peer discovery operation– All devices synchronize to an external time source (e.g., CDMA 2000,

MediaFLO, GPS)

– Periodically, every device transmits its peer discovery signal and also listens to peer discovery signals of others to detect entities of interest in the proximity

– Peer discovery occupies roughly 16 ms every one second• The system overhead is 1.6%

• Standby time is 8.3 days!

Synchronicity is the key to improve energy efficiency!

15

Peer DiscoveryResource

16 ms 16 ms

1second

Peer DiscoveryResource


FlashLinQ Peer Discovery Solution – PHY

• PHY signaling: single-tone OFDM signaling– Concentrating the transmit energy in small degrees of freedom (+17dB)

– Taking advantage of good PAPR property of sinusoid signals (+6dB)

• Caveats of single-tone signaling: half-duplexing and desensing– Miss other transmissions when transmitting due to half-duplexing

– May not be able to hear all simultaneous transmissions due to desensing

– Solution: hopping (Latin square)• Two Peer Discovery Resource IDs overlap in time at most once in 512 seconds

Single tone signaling is the key to increase range and be able to discover many at a time!

16

Uframe

it0

55

1...

...

jt Mframe

0 1 63... ...

...

t=0 t=1 t=63


FlashLinQ Peer Discovery Solution – MAC

• Peer discovery resource is divided into 5600 logical channels that repeats every 8 seconds

• Question: How to pick peer discovery resource (PDRID) in a distributed way?

• Listen first and pick one which is not being used

• What if all of them are used?– Happen when the number of users exceeds the number of PDRIDs in the system

– Stadium scenario

• Pick the one which is least congested – Measure the power at each PDRID and pick one with least power

• A greedy distributed online protocol

• How is the performance in the dense deployment?– Can be analyzed using a simple mathematical model

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Spatial coloring problem

• n nodes uniformly distributed in a 2D space of unit area

• K colors (PDRIDs) are available

• Greedy coloring: pick a random coloring sequence and let each node picks color which maximizes the min distance

• Study : the minimum distance between any two nodes with the same color

Available Colors:


Minimum Distance with One Color

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• Equivalent to the minimum distance between any two nodes out of n randomly placed nodes:

– T. Richardson and E. Telatar

– Much worse than the average

• Can be proved using a balls-into-bins argument– Similar to the birthday problem

• Our result: (Sigmetrics 2010, Ni-Srikant-Wu)– As number of colors increase, the minimum distance behaves more and more

like mean.

)1(min n

D


Main Result: K~log(n)/loglog(n)

• Question: How many colors are required to obtain ?

• Define to be the solution to . For any a>0,

1. If ,

2. If ,

)(nK .1)Pr( min n

aD

nn n )()( )(n

)(nK .0)Pr( min n

aD

)1

(minn

D

)log(log

)log()(

n

nn

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Main Result: K~log(n)

• An upper bound is

• When ,

• Is it possible to make ?

– Yes, we need

– Also a tight result

– Concentration effect

n

KD

2min

)(nK n

D1

min

n

KD min

)log(nK

If K is large enough, distributed coloring can maintain a minimum distancewhich is a constant factor away from the optimal coloring scheme

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Observations

• Online distributed PDRID selection (greedy coloring) protocol is near-optimal in dense scenario, if

– PDRID space is sufficiently large (~log(n) << n)

• Distances between nodes sharing the same PDRID concentrate around the mean values

– Tight hexagonal packing; WAN similar behavior

• Performance for peer discovery in high density deployment is predictable

• New system level ideas can be introduced to improve the performance– WAN interference management schemes like FFR can be introduced to peer

discovery

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Outline

• Motivation: proximate internet – internet aware of the physical proximity

• FlashLinQ peer discovery solution – PHY: 5x range improvement and 40x energy efficiency improvement over WiFi

– MAC: greedy MAC protocol achieves close-to-optimal performance in dense deployment

• FlashLinQ traffic scheduling slultion– Fully distributed SINR based scheduling protocol

– 10x spectrum efficiency improvement over WiFi

23


Main Challenges in FlashLinQ Scheduling

• When to listen and when to transmit?– All mobiles are half-duplex: while device is transmitting, it cannot monitor signals

from other devices in the same band

– Traditional TDD has a predetermined FL/RL partition in cellular networks

– In FlashLinQ, TX and RX partition may not be fixed or determined a priori by a centralized controller

• Which connections to schedule and what rates to use?– In WAN, scheduling units are the connections between a set of devices and

their serving base station (intra-AR scheduling) • Scheduling is not a must, but a way to improve QoS and system capacity

• Problems well formulated and studied in both academic and industry

– In FlashLinQ, scheduling units are the connections between an arbitrary set of device pairs

• Scheduling is a must to avoid deadlock

• Not as many guidance from literature

• How to make efficient scheduling decisions in a distributed fashion?– No central authority here to make decisions to everyone

– Exchanging information between nodes can be expensive

A

B C

D

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Carrier sensing: extend the wireline network to wireless

• Wireless is also a shared medium for communications– Carrier sensing + collision avoidance to make sure the mobiles orthogonalize the

channel use

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A caveat: hidden terminal problem

• Wireless signal loses power much faster when it propagates in space, as compared to the wireline counterpart

– Propagation loss

• Hidden terminal: a corner case that carrier sense breaks down– A patch is needed: RTS/CTS

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802.11 approach: Carrier sense and RTS/CTS

• Carrier sensing and collision avoidance:– Senders (transmitters) are required to listen for DIFS

– Exponentially backoff if collision detected

• Optional RTS/CTS (virtual carrier sensing)– Include the information of the timed required to complete the

data transmission

– All nodes which decoded RTS or CTS not intended for them keep silent during the time interval specified in RTS/CTS

RTS

CTS

DATA

ACK

DIFS

NAV

NAV

SIFS

SIFS

SIFSC

W

DIFS

RTS

CTS

DATA

ACK

NAV

NAV

SIFS

SIFS

SIFS

CW

Node A

Node B

Node C

Node D

A C

B D

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Behavior of 802.11 scheduling: Hard spatial reuse

• SNR based (hard) spatial reuse:– Orthogonalization enforced within

the carrier sensing range, independent of the actual transmission distance

– Unnecessary yielding enforced between transmitters

• Exposed terminal

– Try to mimic wireline network behavior by being heavily biased to orthogonalization

BA

Carrier Sensing Range

D

C

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FlashLinQ Traffic Solution -- Operation

Connection Scheduling Ack

Pilot CQI

Data segmentRate

Scheduling

Logical Traffic slot

• Synchronous system

• Connection scheduling happens every data slot

• Rate scheduling gives SINR estimate of the surviving connections– No rate scheduling in 802.11

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Connection scheduling in FlashLinQ

• Transmitters send out transmit requests (RTS)

• Receivers hearing RTS from a higher priority connection should refrain from sending the CTS back.

– Receiver yielding

• Receivers send out receiver responses (CTS)

• Transmitters hearing CTS from a higher priority pair should refrain from sending data in the current data segments

– Transmitter yielding

• Q: How to choose priority and how to make yielding decision?

P1P2

P3 P4

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Connection Scheduling Signaling

• RTS/CTS signaling: All signals are single tone signals

– Better range due to PAPR gain

– More connections can compete the resource in a few symbols; small system overhead (224 CIDs, 18% system overhead)

– A connection picks a connection ID which is locally unique when the connection is setup

– Symbol/tone choice for RTS/CTS at a given time slot is pseudo random based on the CID

• Priority is embedded in the position of the symbol/tone choice of a signal

– “Fair” sharing of the channel use

• Both channel information and priority information are embedded by the position and power of the signals

Tx (RTS) Rx (CTS)

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SINR Based Yielding

• Receiver yielding: compare the signal strength from the intended transmitter to the signal strength from the interferer

– Yield if the SINR (interference from higher priority connections) is below a certain threshold

• Transmitter yielding: Receiver nodes do inverse power control to help SINR estimation at the transmitters

– Yield if the SINR (interference from the initiator) of a higher priority connection is below a certain threshold

Inverse power scaling enables accurate SINR estimation

Pt=P1 Pr=h11P1

Pt=1/h11P1

Pr=h21/h11P1

Tx1Rx1

Tx2

Tx1

Tx2

Rx1

Rx2

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How to choose SINR threshold?

• Simulation shows a value between [0,10]dB

• A simple analysis: assume SINR threshold = x.

– Translate into distance:

– Number of pairs scheduled: inversely proportional to x^(2/alpha).

– System capacity:

.xd

d

i

t

)1(log)( 2

2

xxxC

dt

di

33


System Throughput vs. SINR Threshold

• Optimal value between [0,10] dB

• Agree with the simulation results

34


FlashLinQ vs. 802.11

• 802.11 is asynchronous

• 802.11’s scheduling is mainly based on CSMA/CA and RTS/CTS

• 802.11 signals are transmitted with maximum power

• 802.11 does not have rate scheduling

• FlashLinQ is synchronous

• FlashLinQ relies on RTS/CTS type of mechanism only

• No exposed terminal

• No extended hidden terminal

• FlashLinQ has both transmitter and receiver power scaling to maximize system spectrum efficiency and enable soft yielding decision

• FlashLinQ does explicit rate scheduling

35


Simulation scenario

• 200m x 200m with wraparound

• n bi-directional links dropped uniformly

• Maximum communcation range 20m

• Keenan Motley model used to model indoor environment

• Compare performance between 802.11g and FlashLinQ

36


Throughput comparison

System Sum Throughput (Mbps) System spetrum efficiency (bit/s/Hz)

FlashLinQ 56 11.2

802.11g 31 1.55

37


Delay comparison

• WiFi makes hard reuse decision– each user is scheduled much less often, but gets high SINR when scheduled

• FlashLinQ makes soft reuse decision

38


System performance at different congestion levels

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Observations

11

21

log

log

h

h

• Synchronous PHY of FlashLinQ makes it possible to design a distributed low-overhead, low-latency, spatial-efficient connection scheduling

• Easily extendable to support QoS, maximal matching and MIMO

• SIC?

)log( 11

11

h

R

1

1

1/2

2

40


Conclusions

• Proximate internet combines the physical network and the internet

• Current technology does not meet the requirements of proximate internet– Range, energy consumption, spectrum efficiency, etc.

• FlashLinQ is a clean slate design for ad hoc networks which can enable proximate internet

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Documents

QUALCOMM Proprietary & Confidential FlashLinQ: A Clean Slate Design for Ad Hoc Networks Xinzhou Wu May. 4 th, 2010