FlashLinQ: A Clean Slate Design for Ad Hoc Networks
Xinzhou Wu
May. 4th, 2010
QUALCOMM Proprietary & Confidential
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
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FlashlinQ – Direct Device-to-Device
Communication Technology
Over Licensed Spectrum
Without Infrastructure Support
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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
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Proximate
Internet
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Autonomous Advertisements…
School: Polling
Place
Mobile Notary
Public
Grocer ->
½ off Salami
Local
Seamstress
Taxi: for Hire ->
Heading to
NYC, need a
ride?
Courier: for
Hire
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Discovering what one
cares about nearby…
Good to
know
Johnny is
near home
The
“Neighborhood
Watch” Cmte
A Family
out for
the day
A School
Field Trip
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Communicating with it…
“Media Swap”
In-building Automation
Control
Mobile Social Network
“Profile Matching”
“Multi-player”
Neighborhood Gaming
“Proximate Contextaware Gaming”
“Vouch” – building 3rdparty Trust Nets
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“FlashPay” – eCash
between eWallets
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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
•…
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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
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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
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Technical Challenges in Peer Discovery Design
• 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)
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FlashLinQ Peer Discovery Solution – Operation
Peer Discovery
Resource
Peer Discovery
Resource
1second
16 ms
•
16 ms
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!
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Synchronicity is the key to improve energy
efficiency!
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FlashLinQ Peer Discovery Solution – PHY
Uframe
jt
it
0
1
0
1
Mframe
...
63
...
...
...
...
55
t=0
•
t=1
t=63
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!QUALCOMM Proprietary & Confidential
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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
color
: the minimum distance between any two nodes with the same
Available Colors:
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Minimum Distance with One Color
• Equivalent to the minimum distance between any two nodes out of n
randomly placed nodes:
– T. Richardson and E. Telatar
Dmin
1
 ( )
n
– 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.
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Main Result: K~log(n)/loglog(n)
• Question: How many colors are required to obtain Dmin
• Define  (n) to be the solution to
1. If
K   (n)
2. If
K   (n) , Pr( Dmin
,
1
 ( ) ?
n
 (n) ( n )  n . For any a>0,
a
)  1.
n
a

)  0.
n
Pr( Dmin 
log( n)
 ( n) 
log log( n)
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Main Result: K~log(n)
Dmin
• An upper bound is
• When
K   (n)
,
Dmin
K
2
n
1

n
K
• Is it possible to make Dmin 
n
– Yes, we need
K  log( n)
?
– Also a tight result
– Concentration effect
If K is large enough, distributed coloring can maintain a minimum distance
which 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
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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
B C
• 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?
A
D
– No central authority here to make decisions to everyone
– Exchanging information between nodes can be expensive
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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
A
– Exponentially backoff if collision detected
C
• 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
DIFS
Node A
Node B
Node C
Node D
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C
W
RTS
S
I
F
S
S
I
F
S
DATA
CTS
S
I
F
S
B
S
I
F
S
DIFS
S
I
F
S
NAV
S
I
F
S
NAV
ACK
NAV
NAV
D
C
W
DATA
RTS
CTS
ACK
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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
D
Carrier Sensing Range
C
– Unnecessary yielding enforced
between transmitters
• Exposed terminal
A
B
– Try to mimic wireline network
behavior by being heavily biased to
orthogonalization
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FlashLinQ Traffic Solution -- Operation
Connection
Scheduling
Rate
Scheduling
Pilot
Data segment
Ack
CQI
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
P1
P2
P3
P4
• 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?
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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
Tx (RTS)
Rx (CTS)
• 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
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SINR Based Yielding
Pt=P1
Tx1
Rx1
Rx1
Tx1
Tx2
Rx2
Pr=h11P1
Pt=1/h11P1
Tx2
Pr=h21/h11P1
• 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
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How to choose SINR threshold?
• Simulation shows a value between
[0,10]dB
• A simple analysis: assume SINR
threshold = x.
d 
– Translate into distance:
d
t

i
 x.
– Number of pairs scheduled: inversely
proportional to x^(2/alpha).
dt
– System capacity:
C ( x)  x
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
2

log 2 (1  x)
di
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System Throughput vs. SINR Threshold
• Optimal value between [0,10] dB
• Agree with the simulation results
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FlashLinQ vs. 802.11
• 802.11 is asynchronous
• FlashLinQ is synchronous
• 802.11’s scheduling is mainly
based on CSMA/CA and RTS/CTS
• FlashLinQ relies on RTS/CTS type of
mechanism only
• No exposed terminal
• No extended hidden terminal
• 802.11 signals are transmitted with
maximum power
• 802.11 does not have rate
scheduling
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• FlashLinQ has both transmitter and
receiver power scaling to maximize
system spectrum efficiency and
enable soft yielding decision
• FlashLinQ does explicit rate
scheduling
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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
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Throughput comparison
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System Sum Throughput (Mbps)
System spetrum efficiency (bit/s/Hz)
FlashLinQ
56
11.2
802.11g
31
1.55
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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
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System performance at different congestion levels
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Observations
• Synchronous PHY of FlashLinQ makes it possible to design a distributed lowoverhead, low-latency, spatial-efficient connection scheduling
• Easily extendable to support QoS, maximal matching and MIMO
• SIC?
R11
log( h11 )
log h21
log h11
1
1/2
1
40
2
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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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