IEEE 802.11 Wireless LAN: Capacity Analysis and Protocol Enhancement F. Cali, M. Conti, E. Gregori...

IEEE 802.11 Wireless LAN:IEEE 802.11 Wireless LAN:Capacity Analysis Capacity Analysis

and Protocol Enhancementand Protocol Enhancement

F. Cali, M. Conti, E. GregoriF. Cali, M. Conti, E. Gregori

IEEE 802.11 Wireless LAN:IEEE 802.11 Wireless LAN:Capacity Analysis Capacity Analysis

and Protocol Enhancementand Protocol Enhancement

F. Cali, M. Conti, E. GregoriF. Cali, M. Conti, E. Gregori

Vangelis AngelakisVangelis Angelakis23 / 3 / 200523 / 3 / 2005

CS-539: Mobile Networks & ComputingCS-539: Mobile Networks & ComputingCS-539: Mobile Networks & ComputingCS-539: Mobile Networks & Computing

IntroductionIntroduction

CS-539 Mobile Networks & Computing Vangelis Angelakis 23/3/2005

Federico Cali, Marco Conti and Enrico Gregori

"IEEE 802.11 Wireless LAN: Capacity Analysis and Protocol Enhancement"

Proceedings of the Conference on Computer CommunicationsIEEE Infocom’98San Francisco, California, USA. March/April 1998.available online at: http://www.ieee-infocom.org/1998/papers/02a_2.pdf

Institute for Informatics & Telematics (IIT), National Research Council (CNR), IT.

Federico Cali, Marco Conti and Enrico Gregori

"IEEE 802.11 Wireless LAN: Capacity Analysis and Protocol Enhancement"

Proceedings of the Conference on Computer CommunicationsIEEE Infocom’98San Francisco, California, USA. March/April 1998.available online at: http://www.ieee-infocom.org/1998/papers/02a_2.pdf

Institute for Informatics & Telematics (IIT), National Research Council (CNR), IT.

Communication in the 802.11 relies on a shared transmission medium.

The 802.11 MAC layer coordinates the transmissions of the n/w STAs

Transmission control information in the 802.11 MAC is both explicit and implicit.

Explicit: Control Messages (ACK, RTS, CTS…)

Implicit: Timing (SIFS, DIFS…) and Channel Condition (BUSY / IDLE –channel

sensing )

Communication in the 802.11 relies on a shared transmission medium.

The 802.11 MAC layer coordinates the transmissions of the n/w STAs

Transmission control information in the 802.11 MAC is both explicit and implicit.

Explicit: Control Messages (ACK, RTS, CTS…)

Implicit: Timing (SIFS, DIFS…) and Channel Condition (BUSY / IDLE –channel

sensing )

Control messages and message retransmissions reduce bandwidth available for successful message transmissions.

The bandwidth fraction used for successfully transmitted messages gives a good indication of the overhead the MAC layer requires to perform its coordination task.

This is called Utilization and is usually influenced by several parameters. The paper focuses on:

i) the number of active n/w STAs, andii) the way they contribute to the offered load

The maximum value of the Utilization is the MAC protocol Capacity

This is called Utilization and is usually influenced by several parameters. The paper focuses on:

i) the number of active n/w STAs, andii) the way they contribute to the offered load

The maximum value of the Utilization is the MAC protocol Capacity

The 802.11 Medium Access ControlThe 802.11 Medium Access Control

The basic access method in 802.11 MAC is the Distributed Coordination Function (DCF).

The 802.11 DCF is a Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) MAC protocol.

The basic access method in 802.11 MAC is the Distributed Coordination Function (DCF).

The 802.11 DCF is a Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) MAC protocol.

802.11 DCF802.11 DCF

I have a packet to send to B, must sense the

medium to avoid collissions

I have a packet to send to B, must sense the

medium to avoid collissions

802.11 DCF802.11 DCF

Time passes…

802.11 DCF802.11 DCF

Channel idle for DIFS

I am starting transmission to B.

Channel idle for DIFS

I am starting transmission to B.

802.11 DCF802.11 DCF

I have a packet to send…

Sensed medium to be BUSY

defering until IDLE

I have a packet to send…

Sensed medium to be BUSY

defering until IDLE

802.11 DCF802.11 DCF

deferring

Must Acknowledge

packet reception

Must Acknowledge

packet reception

802.11 DCF802.11 DCF

τMedium is IDLE, lets see if

it remains idle for DIFSMedium is IDLE, lets see if

it remains idle for DIFS

802.11 DCF802.11 DCF

Time passes…

802.11 DCF802.11 DCF

τ SIFS

deferring

802.11 DCF802.11 DCF

τ SIFS

Medium is IDLE, lets see if it remains idle for DIFS

802.11 DCF802.11 DCF

Time passes…

802.11 DCF802.11 DCF

Time passes…

Backing off

802.11 DCF802.11 DCF

τ SIFS

Contention

Window

802.11 DCF802.11 DCF

τ SIFS

Time slot = time required to sense the medium

Backoff handlingBackoff handling

The # of slots (backoff interval) is uniformly selected in the (0, CWi ) interval

With each idly passing slot the backoff timer is reduced

If during the backoff interval the channel becomes busy then the timer is paused and is reactivated next time the channel is idle for DIFS

If this leads to a collision, then the station will have to retransmit, this time selecting the backoff interval in the (0, 2 xCWi ) interval

The initial CW is CWmin and a CWmax is also defined.

The # of slots (backoff interval) is uniformly selected in the (0, CWi ) interval

With each idly passing slot the backoff timer is reduced

If during the backoff interval the channel becomes busy then the timer is paused and is reactivated next time the channel is idle for DIFS

If this leads to a collision, then the station will have to retransmit, this time selecting the backoff interval in the (0, 2 xCWi ) interval

The initial CW is CWmin and a CWmax is also defined.

RTS/CTSRTS/CTS

The Request-To-Send / Clear-To-Send message exchange is optional.

Used to avoid the hidden terminal problem.

After gaining channel control and before the actual data packet transmission the sender sends a unicast RTS frame.

This is answered by a broadcast CTS from the receiving STA.

-The RTS/CTS mechanism is accounted for in this work.

The Request-To-Send / Clear-To-Send message exchange is optional.

Used to avoid the hidden terminal problem.

After gaining channel control and before the actual data packet transmission the sender sends a unicast RTS frame.

This is answered by a broadcast CTS from the receiving STA.

-The RTS/CTS mechanism is accounted for in this work.

Capacity analysis: Definitions & Notations

ρmax the capacity in asymptotic conditions of M active

stations

ρsingle the capacity considering a single active station

asymptotic conditions (a.c.): All stations have a packet ready to transmit

ideally: ρsingle = ρ max = 1

The paper estimates capacity by evaluating in a.c. the ratio:_

Where: m average message length and

tv virtual transmission time : the average time

the channel is occpied in transmitting a message

ρmax the capacity in asymptotic conditions of M active

stations

ρsingle the capacity considering a single active station

asymptotic conditions (a.c.): All stations have a packet ready to transmit

ideally: ρsingle = ρ max = 1

The paper estimates capacity by evaluating in a.c. the ratio:_

Where: m average message length and

tv virtual transmission time : the average time

the channel is occpied in transmitting a message

Capacity analysis: One active stationCapacity analysis: One active station

Let S denote the time required for a successful transmission.

Then assuming that the average backoff time is E [ CW ] it follows that:

tv = E [ S ] + E [ CW ]

We consider a single active station, no collisions =>

CW always from the (0, CWmin) interval => E [ CW ] = CWmin/2

LEMMA #1:

Let S denote the time required for a successful transmission.

Then assuming that the average backoff time is E [ CW ] it follows that:

tv = E [ S ] + E [ CW ]

We consider a single active station, no collisions =>

CW always from the (0, CWmin) interval => E [ CW ] = CWmin/2

LEMMA #1:

DIFSACKSIFSmS 2 DIFSACKSIFSmS 2

A short proof that A short proof that

DIFSACKSIFSmS 2

Time: t0Time: t0

DIFSACKSIFSmS 2

Time: t0 + mTime: t0 + m

DIFSACKSIFSmS 2

Time: t0 + m + τab Time: t0 + m + τab

DIFSACKSIFSmS 2

Time: t0 + m + τab + SIFS Time: t0 + m + τab + SIFS

DIFSACKSIFSmS 2

Time: t0 + m + τab + SIFS + τbaTime: t0 + m + τab + SIFS + τba

DIFSACKSIFSmS 2

Time: t0 + m + τab + SIFS + τba + ACKTime: t0 + m + τab + SIFS + τba + ACK

DIFSACKSIFSmS 2

Time: t0 + m + τab + SIFS + τba + ACK + DIFSTime: t0 + m + τab + SIFS + τba + ACK + DIFS

Capacity analysis: One active stationCapacity analysis: One active station

yields that E[S] = m + 2τ + SIFS + ACK + DIFS

With (1), (2) & (3) it follows that

Assuming packet lengths to be: - integer multiples of the slot length tslot

- i.i.d. geometrically distributed with parameter q then

yields that E[S] = m + 2τ + SIFS + ACK + DIFS

With (1), (2) & (3) it follows that

Assuming packet lengths to be: - integer multiples of the slot length tslot

- i.i.d. geometrically distributed with parameter q then

DIFSACKSIFSmS 2 DIFSACKSIFSmS 2

( 2 ) (CWmin/2)Single

m SIFS ACK DIFS

( 2 ) (CWmin/2)Single

m SIFS ACK DIFS

tm slot

Capacity analysis: More active stationsCapacity analysis: More active stations

In this case the virtual transmission time tv must include, not

only the successful transmission, but also:-the collision intervals -the DIFS’ after each collision event-the propagation time τ-the idle periods due to the backoff algorithm

In this case the virtual transmission time tv must include, not

only the successful transmission, but also:-the collision intervals -the DIFS’ after each collision event-the propagation time τ-the idle periods due to the backoff algorithm

Coll1Coll1 DIFSDIFS ColliColli DIFSDIFS SS…

Idle_ pi Idle_ pi

Collision Collision Successfultransmission

Successfultransmission

i i 1 1 Ν+1Ν+1

From the previous slide we have:

In 802.11 duration of a collision equals the maximum length of the colliding packets.So, the collision duration (Colli ) depends on:

- the packet size distribution- the backoff algorithm (number of colliding

stations)The length of idle periods (Idle_pi ) depends on

- the backoff algorithm

From the previous slide we have:

In 802.11 duration of a collision equals the maximum length of the colliding packets.So, the collision duration (Colli ) depends on:

- the packet size distribution- the backoff algorithm (number of colliding

stations)The length of idle periods (Idle_pi ) depends on

- the backoff algorithm

SEpIdleEDIFSCollpIdleEt N

__ SEpIdleEDIFSCollpIdleEt N

To compute the two unknowns by taking the 802.11 backoff algorithm is impossible due to introduced interdependencies.

Instead, the authors denote I to be the number of attempts to successfully transmit a packet.

So each station experiences I backoff times {B1, B2, …, BI },

sampled uniformly in intervals of length {CW1, CW2,…,CWI}

To compute the two unknowns by taking the 802.11 backoff algorithm is impossible due to introduced interdependencies.

Instead, the authors denote I to be the number of attempts to successfully transmit a packet.

So each station experiences I backoff times {B1, B2, …, BI },

sampled uniformly in intervals of length {CW1, CW2,…,CWI}

To simplify the analysis authors assume that stations for each transmission attempt use a backoff interval sampled from a geometric distribution with parameter p, where p = 1 / ( E [B ] + 1 ), with E [B ] being the average value of {B1, B2, …, BI }, expressed in number

of slots.

LEMMA #2: E [B ] = (E [CW ] – 1) / 2Where E [CW ] is the average contention window in number of slots.

The above assumption on the backoff algorithm implies that future behaviour does not depend on the past.

To simplify the analysis authors assume that stations for each transmission attempt use a backoff interval sampled from a geometric distribution with parameter p, where p = 1 / ( E [B ] + 1 ), with E [B ] being the average value of {B1, B2, …, BI }, expressed in number

of slots.

LEMMA #2: E [B ] = (E [CW ] – 1) / 2Where E [CW ] is the average contention window in number of slots.

The above assumption on the backoff algorithm implies that future behaviour does not depend on the past.

So in a virtual transmission the following assumptions can be made

(especially under a large number of stations M ):

i) The idle periods duration Idle_pi are i.i.d. sampled from

a geometric distribution with average E [ Idle_p ]

ii) The collision duration Colli are i.i.d. with average E

[Coll ]

So equation:

Can become:

So in a virtual transmission the following assumptions can be made

(especially under a large number of stations M ):

i) The idle periods duration Idle_pi are i.i.d. sampled from

a geometric distribution with average E [ Idle_p ]

ii) The collision duration Colli are i.i.d. with average E

[Coll ]

So equation:

Can become:

SEpIdleEDIFSCollpIdleEt N

__ SEpIdleEDIFSCollpIdleEt N

SENEpIdleEDIFSCollENEtv 1_ SENEpIdleEDIFSCollENEtv 1_

Shortly we will see how E [CW ] can be calculated and p derived from it

Assuming it known, tv depends on E [N ], E [Idle_p ] and E

[Coll ]

LEMMA #3: If the backoff interval for each station is sampled for a geometric distribution with parameter p then:

Shortly we will see how E [CW ] can be calculated and p derived from it

Assuming it known, tv depends on E [N ], E [Idle_p ] and E

[Coll ]

LEMMA #3: If the backoff interval for each station is sampled for a geometric distribution with parameter p then:

pNE slotM

ppIdleE

)1()1()1(

])1()1[(1][

MMslot

pMppqpqh

tCollE

)1()1()1(

])1()1[(1][

MMslot

pMppqpqh

tCollE

Capacity analysis: Estimating E [CW ]Capacity analysis: Estimating E [CW ]

Lets focus on a tagged station and compute E [CW ] as the limiting value of the sequence {E [CW ( n ) ], n=1,2,…}.

The first value of the sequence E [CW ( 0 )] is the average minimum CW and

E [CW ( i+1 )] = Ψ (E [CW ( i )])

Specifically E [CW ( i+1 )] is the tagged station’s i-th average contention window computed under the assumption that all stations in the network transmit with probability p (i ) = 2 / (E [CW ( i )]+1)

Lets focus on a tagged station and compute E [CW ] as the limiting value of the sequence {E [CW ( n ) ], n=1,2,…}.

The first value of the sequence E [CW ( 0 )] is the average minimum CW and

E [CW ( i+1 )] = Ψ (E [CW ( i )])

Specifically E [CW ( i+1 )] is the tagged station’s i-th average contention window computed under the assumption that all stations in the network transmit with probability p (i ) = 2 / (E [CW ( i )]+1)

Capacity analysis: Estimating Ψ (E [CW ( i )])Capacity analysis: Estimating Ψ (E [CW ( i )])

When the tagged station transmits it will experience a collision if another station also attempts to transmit.

The probability of a collision at the (i+1)-th attempt is pcoll

(i+1) = 1 – (1-p (i ))M-1

It follows that the tagged station will experience h collisions before successfully transmitting a packet with probabilty:

P{Ncoll(i+1)=h } = ( pcoll

(i) )h . (1 - pcoll (i) )

Where Ncoll(i+1) is the number of collisions experienced by the

tagged station at the (i+1)-th iteration

When the tagged station transmits it will experience a collision if another station also attempts to transmit.

The probability of a collision at the (i+1)-th attempt is pcoll

(i+1) = 1 – (1-p (i ))M-1

It follows that the tagged station will experience h collisions before successfully transmitting a packet with probabilty:

P{Ncoll(i+1)=h } = ( pcoll

(i) )h . (1 - pcoll (i) )

Where Ncoll(i+1) is the number of collisions experienced by the

tagged station at the (i+1)-th iteration

Experiencing h collisions means that the station will use h+1 contention windows which will be selected according to the 802.11 backoff algorithm.

Lemma #4: Denoting Eh the set of contention windows used

by the tagged station when it experiences h collisions before a successful transmission then:

Where:

is given in the table which is constructed considering the behaviour of the backoff algorithm

Lemma #4: Denoting Eh the set of contention windows used

by the tagged station when it experiences h collisions before a successful transmission then:

Where:

is given in the table which is constructed considering the behaviour of the backoff algorithm

)1()1()1()1(

iii ECWPECWxCWPxCWP

)1()1()1()1(

iii ECWPECWxCWPxCWP

)1()1(

hNPhECWP

)1()1(

hNPhECWP

hii ECWxCWP )1()1( hii ECWxCWP )1()1(

Simulation verificationSimulation verification

The simulation confidence interval (confidence 90%) contains the analytically derived estimate

Packets were assumed to have a geometric distribution with q=0.99.

The simulation confidence interval (confidence 90%) contains the analytically derived estimate

Packets were assumed to have a geometric distribution with q=0.99.

BacktrackingBacktracking

We had the following:

We assume q, but needed p.

We had p (i ) = 2 / (E [CW ( i )]+1) And just showed the algorithm for E [CW ( i )] calculation.

We had the following:

We assume q, but needed p.

We had p (i ) = 2 / (E [CW ( i )]+1) And just showed the algorithm for E [CW ( i )] calculation.

pNE slotM

ppIdleE

)1()1()1(

])1()1[(1][

MMslot

pMppqpqh

tCollE

)1()1()1(

])1()1[(1][

MMslot

pMppqpqh

tCollE

The CapacityThe Capacity

Capacity BoundsCapacity Bounds

For a given packet length, capacity is maximized when the average virtual transmission time is minimized.As we have seen tv is a function of M, p and q.

Authors fix the values of q and M and try to derive the minimum of the tv( p ) function.

For a given packet length, capacity is maximized when the average virtual transmission time is minimized.As we have seen tv is a function of M, p and q.

Authors fix the values of q and M and try to derive the minimum of the tv( p ) function.

Low p values yield high tv

values due to high number of empty slots before a transmission(low probability of collision)

Low p values yield high tv

values due to high number of empty slots before a transmission(low probability of collision)High p values imply a high number of collisions before a successful transmission.

Bottom line:The minimum tv can be derived for each q and from that

the analytical upper bound of the MAC capacity.

High p values imply a high number of collisions before a successful transmission.

Bottom line:The minimum tv can be derived for each q and from that

the analytical upper bound of the MAC capacity.

Improving the IEEE 802.11 MAC capacityImproving the IEEE 802.11 MAC capacity

The critical point for the distance between the analytical capacity bound and the simulated results is the average backoff time which determines the probability of a collision: p (i ) = 2 / (E [CW ( i )]+1)

To demonstrate the figure compares the analytical bound to a simulation of an 802.11 network with a constant contention window sized equal to the optimal value 2/pmin-1

with the value of pmin taken

from the table in the previous slide.

To demonstrate the figure compares the analytical bound to a simulation of an 802.11 network with a constant contention window sized equal to the optimal value 2/pmin-1

with the value of pmin taken

from the table in the previous slide.

Improving the IEEE 802.11 MAC capacityImproving the IEEE 802.11 MAC capacity

Goal: to reach the analytically estimated capacity bound

Method: modify the 802.11 backoff algorithm,

providing it with an optimal constant CW.Bummer:

optimal CW is derived from the pmin and

the pmin value depends also on M and q.

i.e.: the optimal CW size depends on the network load…

To reach the maximum capacity the contention window must be computed in real-time estimating the M and q values…

Goal: to reach the analytically estimated capacity bound

Method: modify the 802.11 backoff algorithm,

providing it with an optimal constant CW.Bummer:

optimal CW is derived from the pmin and

the pmin value depends also on M and q.

i.e.: the optimal CW size depends on the network load…

To reach the maximum capacity the contention window must be computed in real-time estimating the M and q values…

Capacity with known M Capacity with known M

In theory, observing the channel status, a station can estimate the average collision length and the average number of collisions and with a minimization algorithm obtain pmin.

Authors argue that it is unsuitable for run-time and propose a heuristic for the estimation of pmin.

Remember:i) values of p lesser than pmin correspond to the case in which

E [Idle_p] dominates the tv.

ii) values of p greater than pmin correspond to the case in

which collisions dominate the tv.

In theory, observing the channel status, a station can estimate the average collision length and the average number of collisions and with a minimization algorithm obtain pmin.

Authors argue that it is unsuitable for run-time and propose a heuristic for the estimation of pmin.

Remember:i) values of p lesser than pmin correspond to the case in which

E [Idle_p] dominates the tv.

ii) values of p greater than pmin correspond to the case in

which collisions dominate the tv.

pmin approximationpmin approximation

With this in mind pmin is close enough to a p value that

satisfies:

E [Coll ].E [Nc ] = (E [Nc ] + 1).E [Idle_p ].tslot

Simplification: for p close to pmin the number of collisions is

stationary and so E [Coll ] can be taken to be constant.

With this in mind pmin is close enough to a p value that

satisfies:

E [Coll ].E [Nc ] = (E [Nc ] + 1).E [Idle_p ].tslot

Simplification: for p close to pmin the number of collisions is

stationary and so E [Coll ] can be taken to be constant.

),_(][ cNpIdleCollE ),_(][ cNpIdleCollE

][)1][(),_(

slotccc NE

tNENENpIdle

][)1][(),_(

slotccc NE

tNENENpIdle

pmin estimationpmin estimation

802.11+802.11+

Each station start with CW set to the standard CWmin.(authors use the legacy 802.11 draft specs. with CWmin=32)

CW is updated at the end of each tv which contains at least

one collision

Each station runs the algorithm to estimate pmin , and the

estimate of the contention window is now 2 / (pmin –1)

With this the current CW is updated as:CW = a.CW + (1-a).(2 / (pmin –1))

a having the role of a smoothing factor.

Each station start with CW set to the standard CWmin.(authors use the legacy 802.11 draft specs. with CWmin=32)

CW is updated at the end of each tv which contains at least

one collision

Each station runs the algorithm to estimate pmin , and the

estimate of the contention window is now 2 / (pmin –1)

With this the current CW is updated as:CW = a.CW + (1-a).(2 / (pmin –1))

a having the role of a smoothing factor.

802.11+ capacity802.11+ capacity

M unknownM unknown

Assume a wrong idea for the number active stations…Assume a wrong idea for the number active stations…

Run-time M estimationRun-time M estimation

From lemma 3, equations

denoting Total_Idle_p the average number of empty slots, we derive:

and so:

From lemma 3, equations

denoting Total_Idle_p the average number of empty slots, we derive:

and so:

pNE slotM

ppIdleE

ppIdleENEpIdleTotal

_1__ pM

ppIdleENEpIdleTotal

ppIdleTotal

1ppIdleTotal

Result of estimating MResult of estimating M

IEEE 802.11 Wireless LAN: Capacity Analysis and Protocol Enhancement F. Cali, M. Conti, E. Gregori...

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