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Ve h i c u l a r Te c h n o l o g y S o c i e t y
Scope and Focus
Mobile Radio
Land Transportation
Motor Vehicles
Ve h i c u l a r Te c h n o l o g y S o c i e t y
Conferences – Vehicular
Technology Conference
“VTC”, our biannual flagship conference with
500 to 700 attendees
Ve h i c u l a r Te c h n o l o g y S o c i e t y
free to members
society news
and tutorial
papers
Publications –Vehicular
Technology Magazine
Ve h i c u l a r Te c h n o l o g y S o c i e t y
Member
connection
“VTS Mobile World”
Monthly e-newsletter
– With industry news
– Society news and
events
Ve h i c u l a r Te c h n o l o g y S o c i e t y
Educational Activities
Video Lectures on technical topics of interest to members
– Distributed on DVD with the VT magazine
Example topics: – Grounding of Hybrid Vehicles
– Thermal Stress Failures in Electronic and Photonic Systems
– In Vehicle Networking
– Hybrid and Plug-In Electric Vehicle Systems
– Hybrid Powertrain Design
ì
APLATFORMCOMMUNICATIONSTECHNOLOGYFORVEHICLE-VEHICLE
&VEHICLE-CONTROLOFFICEWIRELESSCOMMUNICATIONS
ShiwenMaoSamuelGinnDis-nguishedProfessor
Director:WirelessEngineeringResearchandEduca-onCenter(WEREC)AuburnUniversity,Auburn,AL
hEp://www.eng.auburn.edu/~szm0001
IEEEVTSSantaClaraChapter,Jan.25,2017
AuburnUniversity
3
� Takenfromapoem“TheDesertedVillage”byOliverGoldsmith:� “SweetAuburn!Loveliestvillageoftheplain...”
� Chartered1856� 27,287students� 5,501graduatestudents� 140degreesand13schools/colleges
� 36thamongpublicuniversi-esna-onwide(USNewsandWorldReport)
1/24/17ShiwenMao,AuburnUniversity,Auburn,AL
AuburnUniversity
4
1/24/17ShiwenMao,AuburnUniversity,Auburn,AL ISMBApril20134©ShiwenMao
2010nationalchampionship
SamfordHall
Toomer’scorneroaktrees
rollingthecorner
WarEagle!
AuburnTigers
WearetheTigerswhosay'WarEagle'
HargisHall
LangdonHall
ElectricalandComputerEngineering
6
� Establishedin1891� 26facultymembers
� 14FellowsofIEEE� 6fellowsofotherprofessional
socie-es� 10presidenciesoftechnical
socie-es� 3ABETevaluators� 11editorsoftechnicaljournals
� 196graduatestudents� 567undergraduatestudents
� EE,CE,andBWE
� Over7,000alumni:VincentPoor,EdKnightly,GeoffreyYeLi,TimCook(ISE),…
1/24/17ShiwenMao,AuburnUniversity,Auburn,AL
WEREC
ì Ini-atedbya$25milliongikbyDr.SamuelL.Ginnì $3millionfromVodafone-USFounda-onforscholarshipandfacility
ì Mainlyinvolvesfacul-esfromECEandCSSE
ì DevelopedtheABET-accreditedBachelorofWirelessEngineeringprogram(BWE),star-ngFall2002
7
ResearchCapabilityì RFICandlow-power
ICdesignforbroadbandaccess&applica-ons
ì Wirelesscommunica-onsandnetworks
ì Wirelessandcybersecurity
ì Wirelessapplica-ons
ì Machinelearningforwireless
8
2
VehicularNetworking:why?
• Combattheawfulside-effectsofroadtraffic– IntheEU,around40’000peopledieyearlyontheroads;more
than1.5millionsareinjured– TrafficjamsgenerateatremendouswasteofOmeandoffuel
• MostoftheseproblemscanbesolvedbyprovidingappropriateinformaOontothedriverortothevehicle
Valueoftheconnectedmobilitymarket
3
Self-drivingwillfreeup1.9trillionminutesofidleOmein2030
Globalmarketforautomatedandautonomousdriving,includingrelatedservices($billion)
Source:A.T.Kearneyanalysis
SAEDefiniOonofAutonomousDrive
Nocontrol Fullcontrol
SAELevel0NoAutomaOon
SAELevel1Driver
AssistanceEx:ACC
SAELevel2ParOal
AutomaOonEx:ACC
SAELevel3CondiOonalAutomaOon
(Driverintheloop)
SAELevel4High
AutomaOonEx:Normal
DynamicDriving
SAELevel5Full
AutomaOonEx:AllWeatherDynamicDriving
Levels4&5arechallenging
Theevolu3ontowardsautonomousvehiclesHumanlike
ArOficialIntelligence
AutonomousVehiclesTimeline
OverviewofSmartVehicle
Forward radar
Computing platform
Event data recorder (EDR)Positioning system
Rear radar
Communication facility
Display
A modern vehicle is a network of sensors/actuators on wheels !
Source:gerla.ppt
Terms
q EDR• usedinvehiclestoregisterallimportantparameterssuchasvelocity,
acceleraOon,etc.especiallyduringabnormalsituaOons,suchasaccidents• ThisdataisusedforreconstrucOon.
q Forwardradar• Usedtodetectanyforwardobstaclesasfaras200meters
q PosiOoningSystem• Usedtolocatevehicles• Accuracycanbeimprovedbyknowledgeofroadtopology
q CompuOngplaform• InputsfromvariouscomponentsisusedtogenerateusefulinformaOon
In-VehicleNetworkTopology
9W.Zeng,M.A.S.Khalid,andS.Chowdhury,“In-vehiclenetworksoutlook:AchievementsandChallenges,”IEEECommun.SurveysTutorials,vol.18,no.3,ThirdQuarter,2016.
In-VehicleNetworkTopology(cont’d)
10W.Zeng,M.A.S.Khalid,andS.Chowdhury,“In-vehiclenetworksoutlook:AchievementsandChallenges,”IEEECommun.SurveysTutorials,vol.18,no.3,ThirdQuarter,2016.
q Wireharnesssystemisthe3rdmostexpensiveandheavysystem,aRertheengineandchassis
q RouTngofwireharnessischallenging
q Higherbandwidthdemands
q Upgradingandreplacement
q Gowireless!
V2Xin3GPPTR22.885
12
V2V
V2P
V2I
Pedestrian
Vehicle
Vehicle
Network
• Vehicle-to-Vehicle(V2V)CommunicaOons • Vehicle-to-Infrastructure(V2I)CommunicaOons • Vehicle-to-Pedestrian(V2P)CommunicaOons
ThevehicularcommunicaOoninthisTR,referredtoasVehicle-to-Everything(V2X),containsthefollowingthreedifferenttypes:
RoadsafetyandtrafficefficiencyservicesforV2X
13
• IntersecOonCollisionRiskWarning• Roadhazardwarnings(roadworks,carbreakdown,weathercondiOons,etc.)• Approachingemergencyvehiclewarning• Pre-/Post-Crash• ElectronicEmergencyBrakeWarning• GLOSA–GreenLightOpOmalSpeedAdvisory• Energy-efficientintersecOon• MotorcycleapproachinginformaOon• In-vehiclesignage• RedlightviolaOonwarning• Trafficjamaheadwarning
ScenariosinV2X
14
RSU
(a)RoadsafetyservicesviaanRSU.
Traffic-SafetyServer
Car accident Ahead
Pedestrian
(b)V2XServiceviatheTrafficsafetyserver.
Pedestrian
Vehicle
(c)PedestrianCollisionWarningevenwhenoutofthelineofsight.
(d)Vulnerableroaduserwarningusecasescenario.
ParametersforV2X
15
Effective distance* Absolute speed of a UE supporting V2X Services
Relative speed between 2 UEs supporting V2X Services
Maximum tolerable latency
Minimum radio layer message reception reliability (probability that the recipient gets it within 100ms) at effective distance
Example Cumulative transmission reliability***
#1 (suburban/major road) 200m 50km/h 100km/h 100ms 90% 99% #2 (freeway/motorway) 320m 160km/h 280km/h 100ms 80% 96% #3 (autobahn) 320m 280km/h 280km/h 100ms 80% 96% #4 (NLOS / urban) 150m 50km/h 100km/h 100ms 90% 99% #5 (urban intersection**) 50m 50km/h 100km/h 100ms 95% - #6 (campus/ shopping area) 50m 30km/h 30km/h 100ms 90% 99% #7 Imminent crash 20m 80km/h 160km/h 20ms**** 95% -
• ThesystemparametersforV2Xincluderange,speed,latency,reliabilityandsoon • TheseparametersbasedonV2Xscenariosandrequirements
CellularV2X(C-V2X)
l Scalability for different bandwidths including 10 MHz bandwidth. ConfiguraOons use adedicatedcarrierforV2VcommunicaOons,meaningthetargetbandisonlyusedforPC5basedV2VcommunicaOons.GNSSisusedforOmesynchronizaOon.
ConfiguraOon1:schedulingandinterferencemanagementofV2Vtrafficissupportedbasedondistributedalgorithmsimplementedbetweenthevehicles.ConfiguraOon2:schedulingandinterferencemanagementofV2Vtrafficisassistedviacontrolsignaling.TheeNodeBwillassigntheresourcesbeingusedforV2Vsignalinginadynamicmanner
Sources:3GPPRP-161788
• Ch. 178: • Control Channel • WAVE Service Advertisements are broadcast here, indicating how to
access services on other “Service Channels”
Ch. 172: Collision Avoidance Safety
Ch. 184: Public Safety
Reserved5M
Hz
CH172
Service
10MHz
CH174
Service
10MHz
CH176
Service
10MHz
CH178
Control
10MHz
CH180
Service
10MHz
CH182
Service
10MHz
CH184
Service
10MHz
CH17520MHz
CH18120MHz
5.850GHz 5.925GHz
PHYlayer
PacketScheduling
23
LTE. EnhancedDistributedChannelAccess(EDAS)inDSRC
K.Zeng,Q.Zheng,P.Chatzimisios,W.Xiang,andY.Zhou,“Heterogeneousvehicularnetworking:Asurveyonarchitecture,challengesandsoluOons,”IEEECommun.SurveysTutorials,vol.17,no.4,FourthQuarter,2015.
ChallengesandFuturePerspecOves
27
l Highlyheterogeneousvehicularnetworksl Datamanagementandstoragel LocalizaOonsystemsl Securityandprivacyl DisrupOvetolerantcommunicaOonsl Geographicaladdressingl Trackingatargetl StandardizaOonofprotocolsl CooperaOonwithothernetworksl Variablenetworkdensityl NetworkfragmentaOon
PhaseBeat:Exploi0ngCSIPhaseDataforVitalSignMonitoringwith
CommodityWiFiDevicesXuyuWang,ChaoYang,ShiwenMao
AuburnUniversity,Auburn,AL
Monitoring Breathing and Heart RatesPersonal health Baby monitoring Elderly healthcare
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Adapt lighting and music to mood (affective computing)
1/24/2017 Shiwen Mao, Auburn University
Existing Techniques for Monitoring Vital Signs – Not Contact‐free
Breathing monitoring Heart rate monitoring
Not proper for elderly & babies
31/24/2017 Shiwen Mao, Auburn University
https://www.vernier.com/products/sensors/rmb/
RF Based Vital Signs Monitoring
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Scheme Technique Bandwidths Breathing orHeart rates
Stability
Vital‐Radio FMCW Radar Large Both High
UbiBreathe 2.4 GHz WiFiRSS
small Breathing Low
mmVital 60 GHz WiFi Large Both High
CSI amplitudeBased method
2.4 GHz WiFi small Both Low
PhaseBeat 5 GHz WiFi small Both High
1/24/2017 Shiwen Mao, Auburn University
• F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. Miller, “Smart homes that monitor breathing and heart rate,” in Proc. ACM CHI’15, Seoul, South Korea, Apr. 2015, pp. 837–846.
• Z. Yang, P. Pathak, Y. Zeng, X. Liran, and P. Mohapatra, “Monitoring vital signs using millimeter wave,” in Proc. IEEE MobiHoc’16, Paderborn, Germany, July 2016, pp. 211–220.
• H. Abdelnasser, K. A. Harras, and M. Youssef, “Ubibreathe: A ubiquitous non‐invasive WiFi‐based breathing estimator,” in Proc. IEEE MobiHoc’15, Hangzhou, China, June 2015, pp. 277–286.
• J. Liu, Y. Wang, Y. Chen, J. Yang, X. Chen, and J. Cheng, “Tracking vital signs during sleep leveraging off‐the‐shelf WiFi,” in Proc. ACM Mobihoc’15, Hangzhou, China, June 2015, pp. 267–276.
6
packet boundary detection (PBD)sampling frequency offset (SFO)central frequency offset (CFO)
• Antennas on the same NIC, RF chains are frequency‐locked– p, s, c are the same for all the antennas
• Phase difference:
CSI Measured Phase Information
1/24/2017 Shiwen Mao, Auburn University
ChannelEstimation
Rx
DownConverting
Sampling/ADC
Packet Detection
Phase LockedLoop
Theorem 1. The measured phase difference on subcarrier ibetween two antennas is stable, and its mean and variation are expressed by
CSI Phase Difference Information
Fig. 1. Comparison of single antenna phases (marked as blue crosses) and phase differences (marked as red dots) of the 5th subcarrier in the polar coordinate system for 600 back‐to‐back received packets
71/24/2017 Shiwen Mao, Auburn University
• Lemma 1. When wireless signal is reflected from the chestwith a breathing frequency fb, the true phase of the reflectedsignal at any of the antennas at the receiver is also a periodicsignal with the frequency fr , and we have fr=fb .
CSI True Phase Information
D+A
D-A81/24/2017 Shiwen Mao, Auburn University
CSI True Phase Information (cont’d)
• The reflected signal is the dynamic component, while LOS and other multipath signals are the static component.
• The phase of the total component CSIi is given by
91/24/2017 Shiwen Mao, Auburn University
CSI True Phase Difference Information
Fact: the phases of the total component CSIi for any two antennas havedifferent phase difference while the same frequency. Thus, we canobtain the true phase difference between any two antennas, which isalso a periodic signal with the same frequency fb.
• Theorem 1. For indoor environments with mutipaths, whenthe wireless signal is reflected from the chest of a person withbreathing frequency fb, the true phase at any antenna of thereceiver is also a periodic signal with the frequency fd as thefollowing
101/24/2017 Shiwen Mao, Auburn University
CSI True Phase Difference Information
In PhaseBeat, we use a directional antenna at the transmitter forheart rate estimation, to strengthen the reflected weak heart signal
111/24/2017 Shiwen Mao, Auburn University
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Data Calibration
Fig. 5. Data calibration
1/24/2017 Shiwen Mao, Auburn University
• Hampel Filter• Remove DC
and high frequency noises
• Downsampling
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Subcarrier Selection
Fig. 6. CSI phase difference series patterns after data calibration
Fig. 7. Absolute deviation of each subcarrier
1/24/2017 Shiwen Mao, Auburn University
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Discrete Wavelet Transform
Fig. 8. Discrete wavelet transform
1/24/2017 Shiwen Mao, Auburn University
L=4, considering the frequency range of breathing and heartbeat signals
Breathing/Heart Rate Estimation• Peak Detection for Single Person• FFT for Multiple Persons• FFT for Heart Rate Estimation
Fig. 10. Heart rate estimation based on FFTFig. 9. Breathing rate estimation for two persons
171/24/2017 Shiwen Mao, Auburn University
Implementation with Commodity WiFi
• Transmitter• One Lenovo laptop with one external antenna• Set to the injection mode on 5GHz band• Transmitting rate: 400 packets per second
• Receiver• One desktop with three external antennas• The distance between two adjacent antennas is 2.68cm• Set to the monitor mode
• Benchmark• The NEULOG Respiration Monitor Belt Sensor: breathing rate• The fingertip pulse oximeter: heart rate
181/24/2017 Shiwen Mao, Auburn University
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Fig. 11.(a) Experimental setup Fig. 11.(b) Experimental test
Experimental Setup and Test
1/24/2017 Shiwen Mao, Auburn University
Three scenarios: Computer laboratory, through‐wall, and long corridor
Results: CDF of Breathing Rate Estimation
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J. Liu, Y. Wang, Y. Chen, J. Yang, X. Chen, and J. Cheng, “Tracking vital signs during sleep leveraging off‐the‐shelf wifi,” in Proc. ACM Mobihoc’15, Hangzhou, China, June 2015, pp. 267–276.1/24/2017 Shiwen Mao, Auburn University
Fig. 12. Performance of breathing rate estimation
Results: Heartbeat Rate Estimation
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Fig. 13. Performance of heart rate estimation Fig. 14. Accuracy of breathing and heart rates estimation for different sampling frequency
1/24/2017 Shiwen Mao, Auburn University
Results: Various Environment Factors
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2) Impact of user receiver distance1) Impact of transmitter receiver distance
3) Impact of User Orientation Relative to the Receiver 4) Impact of different user poses
1/24/2017 Shiwen Mao, Auburn University
Summary• PhaseBeat: use CSI phase difference data to monitor
breathing and heart rates with commodity WiFi– Validate the feasibility of CSI phase difference data for vital sign
monitoring– Contact‐free, suitable for long‐term monitoring, low cost, and easy to
deploy– Commodity WiFi: easy to use and widely available
• Performance validated with extensive experiments– Medium error: breathing rate—0.25 bpm, heart rate—1.0 bpm– Robust to distances, wall, orientation, poses, multi‐persons
241/24/2017 Shiwen Mao, Auburn University
Frame-Based Medium Access Control
for mmWave Wireless Networks
Shiwen Mao Samuel Ginn Distinguished Professor
Auburn University, Auburn, AL http://www.eng.auburn.edu/~szm0001
IEEE VTS Santa Clara Chapter, Jan. 25, 2017
The Smartphone Revolution
The iPhone 5s is 15,625 times more powerful than the computer used for the first moon landing
iPhone CPU: 625 times more transistors than a 1995 Pentium CPU
Every single item in this 1990 Circuit City ad are now replaced by the smartphone
Apple: 75 Billion app downloads
12 apps on a smartphone on average
The average user check their phone 110 times a day
12% used it in the shower
1/24/2017 Shiwen Mao, Auburn University, Auburn, AL
2
http://www.slideshare.net/GoCanvas/15-facts-37654260
More Mobile Devices and Apps …
More mobile device on earth than people
They are data hungry …
Year 2000: 1 Exabyte (the entire Internet) Year 2013: 18 Exabyte (mobile data) Year 2018: 15 Exabyte (per month)
1/24/2017 Shiwen Mao, Auburn University, Auburn, AL
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http://www.businessinsider.com/
… and the Cellular Data Crisis
A 1000-fold mobile data traffic growth since 2010
By 2018, there will be nearly five billion global mobile users, up from more than four billion in 2013
Internet video
40% of consumer Internet traffic
62% in 2015
Mobile video is already half of the overall mobile data traffic, and …
… will be 69% of the mobile data traffic by 2018
1/24/2017 Shiwen Mao, Auburn University, Auburn, AL
4
“Moore’sLaw”forWireless
ì AccordingtoMar$nCooper(oneofthepioneersofcellulartelephony)ì Thewirelessthroughputhasdoubledevery30monthsover
aperiodof104yearsì Amillion-foldincreasesince1957
ì Abreakdownofthegain
1/25/17ShiwenMao,AuburnUniversity,Auburn,AL
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Qualcomm’s 1000x Mobile Data Challenge
The industry is preparing for the astounding 1000x increase
1/24/2017 Shiwen Mao, Auburn University, Auburn, AL
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http://www.qualcomm.com/solutions/wireless-networks/technologies/1000x-data
Towards the 5G Wireless
Spectrum expansion TV whitespace: 572—698 MHz: IEEE 802.22Wireless RAN 28~78 GHz mmWave communications for cellular Terahertz communications Free space optical communications
Spectrum efficiency enhancement Cognitive radio
Interference alignment and cancellation
Massive MIMO
Device-to-device communications
Full duplex transmissions
Network densification Small cells (HetNet)
Macro, micro, pico, metro, relays
Femtocells
1/24/2017 Shiwen Mao, Auburn University, Auburn, AL
The Argos testbed developed at Rice
mmWave Communications
Up to 7 GHz unlicensed bandwidth in the 60 GHz band (57~64 GHz)
Unique oxygen absorption properties Attenuation 22 dB higher than that in the 5 GHz
band
Limited range
Beamforming to overcome attenuation Small wavelength, many small antennas can be
assembled
Narrow beams: a few degrees
Airlinx: 1.4o/40dBi, 0.7o/46dBi
“Pseudo-wired” suitable for concurrent transmissions in the outdoor environment
Database synchronization, storage, data centers
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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http://www.rfglobalnet.com/article.mvc/Fixed-Wireless-Communications-at-60GHz-Unique-0001 http://www.engadget.com/2009/01/23/researchers-tout-new-60ghz-rf-chip-for-high-speed-wireless-trans/
Frame Based Directional MAC
An mmWave wireless personal area network (WPAN)
One piconet coordinator (PNC)
Multiple devices (DEVs)
Highly directional transmissions: “pseudo-wired”
A neighborhood discovery protocol
Bootstrapping
Discover new nodes
When idle, all nodes point their beams to the PNC
Time divided into non-overlapping frames
Consists of a scheduling phase and a transmission phase
One schedule computed for each frame
The transmission phase consists of multiple concurrent transmissions
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Frame-based Scheduling
Scheduling phase:
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Frame-based Scheduling (cont’d)
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Transmission phase:
It takes 11 time slots to transmit 17 packets, a big saving from 24 time slots if every packet goes through the PNC
How to Compute the Schedule?
Mixed Integer Nonlinear Programming (MINLP) problem
Who talks to whom:
… for how long:
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Solution Algorithm 1
Reformulation and linearization
Define substitution variables to replace quadratic terms
Derive RLT bound-factor product constraints for the substitution variables
Obtain a Mixed Integer Linear Programming (MILP) problem
Solved with an MILP solver
Need a local search method to find a near-by feasible solution
Time consuming; cannot be used in practice, but serves as a benchmark
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Solution Algorithm 2
S : K-edge-colorable graph
Maximal weight matching
Greedy coloring algorithm
Fast computation of schedules: O(|E|2)
Bound on the number of colors K
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Example
Demand matrix
Solution Algorithm 1: [410 s, 34 time slots, 57 packets]
Greedy Coloring Algorithm: [4.3 μs, 36 time slots, 57 packets]
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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An Enhancement
Allow transmitting packets that arrive during the current frame, whenever possible
Helpful when congestion occurs
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Simulation Setting
Same protocol level parameters as in prior work
Traffic models:
i.i.d. Bernoulli traffic
On-off bursty traffic
Traffic patterns:
Uniform traffic
Destination of a packet is uniformly distributed among all neighbors
Non-uniform traffic
Some neighbors receive higher data rate
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Comparison with State-of-the-art
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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• S. Singh, F. Ziliotto, U. Madhow, E. M. Belding, and M. Rodwell, “Blockage and directivity in 60 GHz wireless personal area networks: From cross-layer model to multihop MAC design,” IEEE J. Sel. Areas Commun., vol. 27, no. 8, pp. 1400–1413, Oct. 2009.
• S. Singh, R. Mudumbai, and U. Madhow, “Distributed coordination with deaf neighbors: Efficient medium access for 60 GHz mesh networks,” in Proc. IEEE INFOCOM, San Diego, CA, Mar. 2010, pp. 1–9.
Average delay under uniform traffic pattern:
Comparison with State-of-the-art (cont’d)
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Average delay under non-uniform traffic pattern:
Three neighbors receive 40% of offered load
Comparison with State-of-the-art (cont’d)
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Throughput under non-uniform traffic pattern:
Comparison with State-of-the-art (cont’d)
1/24/2017 Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
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Enhancement performance: Fairness performance:
• R. Jain, A. Durresi, and G. Babic, “Throughput fairness index: An explanation,” Feb. 1999, ATM Forum/99-0045.
Related Work
Directional MAC: [Ko99, Takai02, Korakis08]
TDMA mmWave MAC: [An08, Pyo09]
Centralized schemes: [Gong10, Singh09]
Distributed schemes: [Singh10, Shihab09]
Interference analysis: “pseudowired” model [Singh11]
Link scheduling for static channel conditions: exclusive regions [Cai10]
This work:
Frame-based scheduling: gated service in polling systems, amortize control overhead
Leveraging spatial reuse
General link model considering both interference and blockage
1/24/2017
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Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
Conclusions
Frame based scheduling in 60 GHz mmWave WPANs
Exploit concurrent transmissions to improve network capacity
Adopt frame-based scheduling to amortize control overhead
Proposed FDMAC with a Greedy Coloring algorithm core
For more details, please visit: http://www.eng.auburn.edu/~szm0001/
1/24/2017
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Shiwen Mao | http://www.eng.auburn.edu/~szm0001/
Acknowledgments
ì Mr.DhavalJ.Brahmbha.andIEEEVTSSFBayArea
ì Researchsponsorsì NSFì CERDEC,USArmy,USNRLì Ciscoì TranSwitch
ì This work is supported in part by the NSF under Grants CNS-1320664, and Auburn University Wireless Engineering Research and Education Center. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundation
1/24/17ShiwenMao,AuburnUniversity,Auburn,AL
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