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Vehicular Technology Society

Connecting the Mobile World

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


Conferences – Vehicular

Technology Conference

“VTC”, our biannual flagship conference with

500 to 700 attendees


free to members

society news

and tutorial

papers

Publications –Vehicular

Technology Magazine


40+ active chapters throughout

the world


62 Distinguished Lecturers


Member

connection

“VTS Mobile World”

Monthly e-newsletter

– With industry news

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events


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

2

1/24/17ShiwenMao,AuburnUniversity,Auburn,AL

AuburnUniversity

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�  Takenfromapoem“TheDesertedVillage”byOliverGoldsmith:�  “SweetAuburn!Loveliestvillageoftheplain...”

�  Chartered1856�  27,287students�  5,501graduatestudents�  140degreesand13schools/colleges

�  36thamongpublicuniversi-esna-onwide(USNewsandWorldReport)


AuburnUniversity

4

1/24/17ShiwenMao,AuburnUniversity,Auburn,AL ISMBApril20134©ShiwenMao

2010nationalchampionship

SamfordHall

Toomer’scorneroaktrees

rollingthecorner

WarEagle!

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WearetheTigerswhosay'WarEagle'

HargisHall

LangdonHall

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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),…


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

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VehicularNetworkingfor5G:TechniquesandChallenges

1

Speaker:ShiwenMao

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

CompaniesforVehicularNetworking

4Source: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

ApplicaOons

6Source:A.T.Kearneyanalysis

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!

SensorsofSmartVehicle

11

Technologiesforautomateddriving

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

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•  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

V2Xfor5G

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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

C-V2X

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C-V2XTechnicalAdvantagesOverIEEE802.11p(ITS-G5orDSRC)

Source：5GAA-whitepaper

DSRC–DedicatedShortRangeCommunicaOonsprotocol

19IllustraOonoftheWAVEprotocolstackoftheDSRCnetwork.

•  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

MAClayer

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ComparisonamongMACprotocols

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.

Networklayer

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DifferentcommunicaOonscenarios

(a)Unicast (b)MulOcast/Geocast

(c)Broadcast

DifferentProtocols

24HUAWEI-WHITEPAPER-5G-CONNECTED-CARS

DifferentProtocols

25

DSRCSafetyApplicaOonScenario.

26

DSRCSafetyApplicaOonScenarios.

ChallengesandFuturePerspecOves

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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

2

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


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


• 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.

Channel State Information (CSI)

5

Y=H X+N


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


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


• 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


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


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


12

PhaseBeat Design

Fig. 3. PhaseBeat system flow


13

Environment Detection

Fig. 4. Patient status detection1/24/2017 Shiwen Mao, Auburn University

14

Data Calibration

Fig. 5. Data calibration


• Hampel Filter• Remove DC

and high frequency noises

• Downsampling

15

Subcarrier Selection

Fig. 6. CSI phase difference series patterns after data calibration

Fig. 7. Absolute deviation of each subcarrier


16

Discrete Wavelet Transform

Fig. 8. Discrete wavelet transform


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


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


19

Fig. 11.(a) Experimental setup Fig. 11.(b) Experimental test

Experimental Setup and Test


Three scenarios: Computer laboratory, through‐wall, and long corridor

Results: CDF of Breathing Rate Estimation

20

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


Results: Various Environment Factors

22

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


23

Realtime PhaseBeat Demo


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


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

http://www.eng.auburn.edu/~szm0001

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)


3

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


4

“Moore’sLaw”forWireless

ì  AccordingtoMar$nCooper(oneofthepioneersofcellulartelephony)ì  Thewirelessthroughputhasdoubledevery30monthsover

aperiodof104yearsì  Amillion-foldincreasesince1957

ì  Abreakdownofthegain


5

Qualcomm’s 1000x Mobile Data Challenge

The industry is preparing for the astounding 1000x increase


6

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


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


9

Frame-based Scheduling

Scheduling phase:


10

Frame-based Scheduling (cont’d)


11

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

Frame-based Scheduling (cont’d)


12

How to Compute the Schedule?

Mixed Integer Nonlinear Programming (MINLP) problem

Who talks to whom:

… for how long:


13

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


14

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


15

Example

Demand matrix

Solution Algorithm 1: [410 s, 34 time slots, 57 packets]

Greedy Coloring Algorithm: [4.3 μs, 36 time slots, 57 packets]


16

An Enhancement

Allow transmitting packets that arrive during the current frame, whenever possible

Helpful when congestion occurs


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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


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Comparison with State-of-the-art


19

• 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)


20

Average delay under non-uniform traffic pattern:

Three neighbors receive 40% of offered load



21

Throughput under non-uniform traffic pattern:



22

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

23

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


25

Thank You!

Questions & comments?

http://www.eng.auburn.edu/~szm0001

1/24/2017

26

Shiwen Mao, Auburn University, Auburn, AL

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