Performance Evaluation of WLAN for Mutual Interaction between Unicast and

Multicast Communication Session

Author: Aamir Mahmood

Supervisor: Prof. Riku Jäntti

Wireless Local Area Networks

IEEE802.11 family of standards Maturity of standard, low cost infrastructure, operation

in unlicensed band Simple standalone infrastructure Extensions to existing networks (WiMax, TETRA) Providing access for high speed data and multimedia

services

Growing interest in outdoor operation of IEEE802.11

WLAN for Real-Time Services

Primary objective - asynchronous (data) services Case: A realistic network with multiple wireless

technologies complementing each other Requirement: Support for data and real-time services Challenges:

Uncontrolled and unreliable propagation environment Stringent Quality of Service (QoS) requirements Performance and scalability constraints of MAC algorithm

Thesis Contribution

WLAN evaluation by simulations under mutual interaction of unicast and multicast real-time sessions

Extending the isolating study of unicast and multicast sessions

Evaluation Steps: Designing a prototype for simulator verification (PHY and MAC

parameters, backoff time distribution, collision probability)

Mobility with a proposed model

Optimal size of WLAN cell, suitable for unicast and multicast sessions under TWO-RAY propagation model

Test bed and Simulator

Test environment concerns Modifications in system parameters Reliability and reproducibility of the results

Test bed Open Source Components: operating system, WLAN adaptor drivers,

real-time traffic emulation software, traffic monitoring and sniffing

Simulator Qualnet 4.0 – Reliable and comprehensive modeling / simulation Signal reception model IEEE 802.11 PHY layer Propagation model IEEE 802.11 MAC layer

Prototype Design for Simulator Verification - I

Monitoring Station

Node 1

Node 2

Var. Attenuator 40 dB

Access Point (AP)

30 dB

30 dB

RF Cable

Ethernet Cable

Maximum aggregate throughput

for two nodes with UDP flooding

Constant propagation environment

Measure the collision probability Two nodes sharing the equal

throughput

Testbed vs Simulation Throughput - II

100 300 500 700 900 1100 1300 14721

2

3

4

5

6

6.71

UDP Payload

Thr

ough

put (

Mbp

s)

simulatortestbed

100 300 500 700 900 1100 1300 14720.6

0.7

0.9

1.1

1.3

1.5

1.71

UDP Payload

Thr

ough

put (

Mbp

s)

simulatortestbed

@11Mbps @2Mbps

Collision Probability - III

Nodes Simulated collision probability

Simulated average backoff

Analytical average backoff

2 0.0318 16.0603 16.0423

3 0.0605 16.8147 16.5878

4 0.0867 17.1745 17.1213

6 0.1439 18.2225 18.2999

8 0.1818 19.1257 18.9628

1 (1 )(1 (2 ) 1 (2 1)( ).

1 2(1 2 ) 2 2

m m m m m

avg k

W p p p W p pW

p p

*H.L. Vu and T Sakurai, “Collision probability in saturated IEEE 802.11 networks”, ATNAC Australian Telecommunication Networks and Applications Conference 2006

*

Group Mobility

The proposed mutual interaction of unicast and multicast sessions is well-suited for simultaneous one-to-one and group communication

The performance for the proposed joint flows is evaluated under a proposed group mobility model

The model maps the mobility of public safety cooperative activities

Proposed Mobility Model

The deployment of users towards a randomly selected hotspot area belonging to the cell

Uniform initial distribution Speed of the user is proportional

to the distance from the destination in the hotspot

The destination location of a user in hotspot is also uniformly distributed

Statistical Analysis - I

0 0.5 1 1.5 20

0.005

0.01

0.015

Distance (units)

Vel

ocity

(uni

ts/s

)

0.005 0.006 0.007 0.008 0.009 0.01 0.011 0.012 0.013 0.014 0.0150

20

40

60

80

100

120

140

160

180

Velocity (units/s)

pdf

Velocity as a function of distance between the initial and final position

PDF of initial speed distribution

Rc = 1 units, Ro = 0.1 units, Vmin = 0.005 units/sec

Vmax = 0.015 units/sec

Statistical Analysis - II

20 40 60 80 100 1200

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

Simulation time (s)

Inst

an

tan

eo

us

ne

two

rk s

pe

ed

(u

nits

/s)

Ro=0.1

Ro=0.5

-1-0.5

00.5

1

-1

-0.5

0

0.5

10

0.2

0.4

0.6

0.8

xy

pd

f

Instantaneous network speed as a function of the simulation time

Spatial distribution of the nodes

Mutual Interaction of Unicast and Multicast Communication Session

Scenario Single multicast VoIP session in the downlink direction Increasing number of unicast VoIP session in the uplink direction Effect of adding one unicast video feed in addition to the uplink VoIPs

Performance measurement How does the performance of multicast session is degraded and vice

versa?

Metrics Packet Deliver Ratio PDR

PHY and MAC parameters, traffic emulation VoIP: CBR G.711 with 10ms payload size (92 bytes/packet) Video: CBR 30ms payload size (360Kbps)

Simulation Setup

Cell radiuses2Mbps:Target SNR = 6dB Cell Radius =300m

11Mbps:Target SNR = 10dB Cell Radius =240m

Probability of hidden nodes

Carrier sensing range (534m)

Hidden node probability 3%

0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SNR (dB)

PE

R

2Mbps11Mbps

0 20 40 60 80 100 120 1400

0.005

0.01

0.015

0.02

0.025

0.03

Packet Delivery Ratio (PDR)

2 3 4 5 60.84

0.86

0.88

0.9

0.92

0.94

No. of unicast voice sessions

Mul

ticas

t voi

ce P

DR

11Mbps11Mbps - 1 video

2 3 4 5 60.84

0.86

0.88

0.9

0.92

0.94

No. of unicast voice sessions

Mul

ticas

t voi

ce P

DR

2Mbps2Mbps - 1 video

Delay

2 3 4 5 60

2

4

6

8

10

12

No. of unicast voice sessions

Mul

ticas

t voi

ce d

elay

(mse

c)

11Mbps 2 Mbps11Mbps - 1 video 2 Mbps - 1 video

2 3 4 5 60

2

4

6

8

10

12

No. of unicast voice sessions

Uni

cast

voi

ce d

elay

(ms)

11Mbps 2 Mbps11Mbps - 1 video 2 Mbps - 1 video

Final remarks

The degradation in performance can be concluded as For multicast session it is the low PDR For unicast sessions it is the increasing average delay

Future Work It is expected that the degradation would be severe in the presence

of fading It will be more appropriate to model the traffic instead of considering

the CBR type of traffic DCF vs PCF

The End

Questions?

Thank You!