PHY layer access misbehavior in WLAN networks

PHY layer access PHY layer access misbehavior in WLAN misbehavior in WLAN

networksnetworks

Master thesis presentationRadio Communication Systems, KTH

Probir KhaskelAdvisor: Olav Queseth & Examiner: Prof. Jens Zander

2

OutlineOutline

Problem definition System model Link adaptation Single cell system Multi-cell system Game theoretical analysis Conclusion Future work

Ideal system performance System response in CCA level modification

Airport environment Infrastructure based network Time driven system in MATLAB 1 time slot = 1 OFDM symbol (4μ sec )

Players attitude Dynamic strategies for stable system

3

ProblemProblem Definition Definition

Is there any incentive to modify CCA level defined by the standard from the user’s point of view ?

Greedy users in a single network Modification in the PHY layer Frequent channel access by

modifying CCA threshold level

????

Does this modification have impact on the overall system performance (in terms of throughput) ?

Greedy users in a single network Modification in the PHY layer Frequent channel access by

modifying CCA threshold level

4

Greedy channel access in the Greedy channel access in the PHY layerPHY layer

Th

resh

old

T

hre

shold

(d

Bm

)(d

Bm

)

TimeTime

-82

-72

~

Channel is busy

-62 Channel is Idle

RF signal RF signal strengthstrength

Problem background

↑↑

5

System modelSystem model

Propagation model Traffic model and

packet System deployment Transmit power and

noise Capture model Channel

interference

Traffic model and packet Asynchronous and identical traffic from the upper layer

Fixed packet size (MPDU-256 bytes)

System deployment Single cell system with various

number of nodes Multicell system with 16 cells, re-

use factor 4, 3 nodes in each cell Transmit power and noise

Fixed maximum power of +23 dBm Noise level -95 dBm

Capture model

i

ij jjrx

iirx

NdP

dP

)(

)(

,

,

Channel interference

Co-channel interference Adjacent channel

interference is ignored

Propagation model

L (d)freeSpace = 32:44+ 20 ¢log10(fc) + 10 ¢n ¢log10(d)

L (d)fr eeSpace= 32:44+ 20 ¢log10(fc) + 10 ¢n ¢log10(d)

Lmw = L (d)fr eeSpace+ L s ¢n[n s + 5n s + 3 ¡ b]s +¾x

A. . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . ......................................... ........................Lmw = L (d)freeSpace+ L s ¢n

[n s + 5n s + 3 ¡ b]s +¾x

L (d)fr eeSpace= 32:44+ 20 ¢log10(fc) + 10 ¢n ¢log10(d)

6

IEEE 802.11a PHY and link IEEE 802.11a PHY and link adaptationadaptation

8 PHY modes with data rates ranging from 6 to 54 Mbps

Link Adaptation for data transmission is realized as MPDU-based fast link adaptation, placed closer to the air-

interface based on the estimated C/I at the receiver

Link Adaptation for ACK transmission is realized as receiver adopts the same PHY mode as the

corresponding received data packet

8 PHY modes with data rates ranging from 6 to 54 Mbps

Link Adaptation for data transmission is realized as MPDU-based fast link adaptation, placed closer to the air-

interface based on the estimated C/I at the receiver

Link Adaptation for ACK transmission is realized as receiver adopts the same PHY mode as the

corresponding received data packet

24.56

24.05

18.80

17.04

10.79

9.03

7.78

6.02

C/I (dB)

54 Mbps

48 Mbps

36 Mbps

24 Mbps

18 Mbps

12 Mbps

9 Mbps

6 Mbps

Data Rate

3/464-QAM8

2/364-QAM7

3/416-QAM6

1/216-QAM5

3/4QPSK4

1/2QPSK3

3/4BPSK2

1/2BPSK1

Code RateModulationMode

7

Single cell systemSingle cell system

8

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

0.2

0.4

0.6

0.8

1

1.2

1.4

load vs. throughput

avg. offered load per station [Mbps]

avg.

thro

ughp

ut [M

bps]

6 STA

11 STA16 STA

21 STA

0 0.5 1 1.5 2 2.50

20

40

60

80

100

120

140

160

180

200load vs. delay


avg.

del

ay [m

s]

For higher number of users, system gets saturated earlier

Single cell: Ideal system performance

System with higher number of users is less capable to support delay bounded QoS with increasing offered load

More number of users in the system, more throughput drops down from system capacity to saturation level

Number of collision also gets saturated in system saturation

load vs. collision

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60

20

40

60

80

100

120

140

160

180

200


avg.

no.

of c

ollis

ion

9

-85 -80 -75 -70 -65 -60 -55 -50 -45 -400

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6cslevel vs. throughput for various no. of nodes

cslevel [dBm]

avg.

thro

ughp

ut p

er u

ser

[Mbp

s]

5 nodes

10 nodes15 nodes

20 nodes

(-52, 1.51)

(-54, 0.719)

(-58, 0.352)

(-58, 0.206)

The less number of users in the system, the higher reachable CCA level

Single cell: Saturation analysis

10

0 5 10 15 20 250

5

10

15

20

25

-85 -80 -75 -70 -65 -60 -55 -50 -45 -400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9cslevel vs. throughput; (D|S)

cslevel [dBm]

avg.

thro

ughp

ut p

er u

ser

[Mbp

s]

offered load per user:1.37 [Mbps]

G2: -82 dBm (timid)

G1: increasing (greedy)

G1: (-50, 0.857)

G2: (-50, 0.466)

-85 -80 -75 -70 -65 -60 -55 -50 -45 -400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8cslevel vs. throughput; (S|D)

cslevel [dBm]

avg.

thro

ughp

ut p

er u

ser

[Mbp

s]


G2: increasing (greedy)

G1: -82 dBm (timid)

G2: (-52, 0.761)

G1: (-52, 0.499)

Single cell: Greedy vs. timid users

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load vs. throughput

0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


avg.

thro

ughp

ut [M

bps]

G2: -52 dBmG1: -50 dBmIdeal systemavg. system, (D|D)

System throughput decreases than that of the ideal system

Number of collision increases around eight times, however, delay performance does not deteriorate compare to throughput and collision

0 0.5 1 1.5 2 2.50

20

40

60

80

100

120

140

160

180

200load vs. delay


avg.

del

ay [m

s]


0 0.5 1 1.50

100

200

300

400

500

600

700

800

900

1000load vs. collision


avg.

col

lisio

n


Single cell: Greedy vs. greedy users

12

Current Nash Equilibrium is Pareto inefficient

(S|S) would be Pareto efficient NE

System performance is better when users follow the standard protocol

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1bargain domain of SSG, single cell system

payoff of G1, v1

payo

ff o

f G

2, v

2

(S|S)(0.719,0.719)

(D1|D2)(0.612,0.521)

(S|D2)(0.499,0.761)

(D1|S)(0.857,0.466)

S: -82 dBm

D1: -50 dBm

D2: -52 dBm

single cell: 11 STAG1: 3 nodesG2: 7 nodes

current NE is Pareto inefficient,(S|S) could be Pareto efficient NE

defection by G2 defection by G1

NashEquilibrium

Current Nash Equilibrium is Pareto inefficient

(S|S) would be Pareto efficient NE

Single cell: single stage game

G1↓ G2→ S D

S (0.719, 0.719) (0.499, 0.761)

D (0.857, 0.466) (0.612, 0.521)

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Single cell: multi stage game

Users prefer to defect if δ<0.563, meaning that they are more likely to defect

Dynamic strategies: TFT, GRIM ensure a stable system

Discount factor, δ (delay bounded application dependent)

Anticipated payoff in stage t to player i

In an infinite game, payoff is computed as

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Multi-cell systemMulti-cell system

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System throughput out-perform the standard by cooperative modification of the CCA threshold

Collision reduces around three and a half times compare to the ideal system

-90 -80 -70 -60 -50 -40 -30 -200.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7cslevel vs. throughput; (C|C)

cslevel [dBm]

avg.

thro

ughp

ut p

er u

ser

[Mbp

s]


(-68, 0.665)

(-82, 0.376)

-90 -80 -70 -60 -50 -40 -30 -2050

100

150

200

250

300

350

400

450

500cslevel vs. collision; (C|C)

cslevel [dBm]

avg.

no.

of c

ollis

ion

offered load per user:1 [Mbps]

(-82, 483)

(-68, 139)

Multi-cell: saturation analysis

16

G1↓ G2→ C D

C (0.665, 0.665) (0.327, 0.438)

D (0.727, 0.398) (0.366, 0.366)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1bargain domain of SSG; multi cell system

payoff of 1, v1

payo

ff o

f 2,

v2 (C|C)

(0.665,0.665)

(D|D)(0.366,0.366)

(C|D)(0.327,0.438)

(D|C)(0.727,0.398)

C: -68 dBmD: -44 dBm

multi cell: 48 nodesG1: 16 nodesG2: 32 nodes

defection by G1defection by G2

current NE is Pareto efficient

NashEquilibrium

SSG: Current Nash Equilibrium is Pareto efficient

MSG: TFT, GRIM dynamic strategies ensure a stable system

Users prefer to defect if δ<0.171, meaning that they are more likely to cooperate

Multi-cell: game analysis

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ConclusionConclusion Single cell system

It’s possible to achieve higher throughput by modifying the CCA level

Any modification results in deterioration of the system performance

Multi-cell System

Adaptive modification of the CCA level gives a noticeable system improvement

A small group gains by further modification, the overall system performance deteriorates

Users are more likely to cooperate

Operators might be interested to have a control on the CCA level modification based on the network condition and update the users to adjust in a regular fashion

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Future workFuture work Part of the received data of a collided packet could

be recoverable by smart decoding algorithm, which in tern could increase the system throughput by avoiding to retransmit the whole packet

Transmit Power Control (TPC) could increase system capacity by minimizing co-channel interference

In general, any misbehaving activities can be detected by collision counter. However, pinpointing a misbehaving user is a crucial task

Players’ assessment of others’ strategy by observed throughput might be a pitfall for system stability

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Question Question

& &

Comments!Comments!

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0.5630.171

δ →0 1

Higher preference of future payoff,

e.g. voice telephony

Lower preference of future payoff,

e.g. best effort type application

Discount factor, δ

Extra slides

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Hidden Terminal ProblemHidden Terminal Problem

Hidden terminal

Station/Node

Access Point

Extra slides

22

Unlicensed Unlicensed frequencyfrequency bands bands

UPCS-Unlicensed Personal Communication Services [1.9GHz]

ISM-Industry, Science and Medicine [2.4-2.4835GHz]

UNII-Unlicensed National Information Infrastructure [5.15-5.825GHz] Why Why UnlicensedUnlicensed??

Promotes efficient spectrum sharing Further experimentation and innovation Mobility of wireless applications since no license

needed in new location

IntroductionIntroductionExtra slides

PHY layer access misbehavior in WLAN networks

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