Analysis of IEEE 802.11e and Analysis of IEEE 802.11e and
Application of Game Models for Application of Game Models for
Support of Quality-of-Service in Support of Quality-of-Service in
Coexisting Wireless NetworksCoexisting Wireless Networks
Stefan MangoldComNets Aachen University
30-June-2003
Stefan Mangold - ComNets Aachen University 2
OutlineOutlineOutlineOutline
IEEE 802.11 wireless LANBrief introduction: Distributed Coordination Function (DCF)
IEEE 802.11e QoS extensionOverview: Enhanced DCF (EDCF)
Achievable throughput with the EDCF
Model for achievable throughput per priority
Result evaluation with WARP2
Overlapping radio networks in unlicensed bandsGame model of competition
Result evaluation with YouShi
Analysis of competition scenario: stability, expected outcomes
Cooperation in repeated games
Conclusions
Stefan Mangold - ComNets Aachen University 3
Motivation of this ThesisMotivation of this ThesisMotivation of this ThesisMotivation of this Thesis
IEEE 802.11 is the dominant radio system for wireless Local Area Networks (LANs):
Today’s Wireless LANs cannot support Quality of Service (QoS)
However, the demand is growing for new applications with QoS requirements
Wireless LANs operate in unlicensed frequency bands, where they have to share radio resources
Problems/Questions:How to support QoS in wireless LANs?
If wireless LANs can support QoS, what level of QoS can be maintained in unlicensed frequency bands?
New methods to support QoS in wireless LANs are developed and evaluated in this thesis.
Stefan Mangold - ComNets Aachen University 4
IEEE 802.11 Wireless LANIEEE 802.11 Wireless LANIEEE 802.11 Wireless LANIEEE 802.11 Wireless LAN
Radio standard for data transport system that covers ISO/OSI layer 1 and 2:
Multiple Physical (PHY) layers: .11/.11a/.11b/.11g
One common Medium Access Control (MAC) layer
Here: IEEE 802.11a PHYOFDM multi-carrier transmission
Up to 54Mbit/s (@PHY)
5 GHz unlicensed bandShared resources
Main Service:MSDU Delivery
Reference model
MLMEmedium accesscontrol sublayer
PLMEPLCP sublayer
SME
PMD sublayer
IEEE 802.11
user
plan
e
man
agem
ent
plane
cont
rol p
lane
logical link controlsublayer
MAC-SAPMLME-
SAP
physical layer
transport layer
network layer
data link controllayer
OSI reference model
1
3
4
2
MLMEmedium accesscontrol sublayer
PLMEPLCP sublayer
SME
PMD sublayer
IEEE 802.11
user
plan
e
man
agem
ent
plane
cont
rol p
lane
logical link controlsublayer
MAC-SAPMLME-
SAP
physical layer
transport layer
network layer
data link controllayer
OSI reference model
1
3
4
2
Stefan Mangold - ComNets Aachen University 5
Distributed Coordination Function (DCF)Listen before talk: CSMA/CA
Binary exponential backoffContention window increases with each retransmission
Received MPDUs (data frames) are acknowledged
Variable frame body sizes (up to 2312 byte)One queue per stationCollisions occur if many stations operate in parallel
Medium AccessMedium AccessMedium AccessMedium Access
CTS
RTS
time
ACKSIFS
DIFS
PIFS
SIFS
Contention Window(counted in slots,
9us per slot, 15 slots in 802.11a)
SIFS
defer access count down as long as medium is idle,backoff when medium gets busy
with 802.11a: slot: 9us SIFS: 16us PIFS: 25us DIFS: 34us
SIFS
DATA
busychannel
CTS
RTS
time
ACKSIFS
DIFS
PIFS
SIFS
Contention Window(counted in slots,
9us per slot, 15 slots in 802.11a)
SIFS
defer access count down as long as medium is idle,backoff when medium gets busy
with 802.11a: slot: 9us SIFS: 16us PIFS: 25us DIFS: 34us
SIFS
DATA
busychannel
Stefan Mangold - ComNets Aachen University 6
IEEE 802.11 Wireless LAN BasicsIEEE 802.11 Wireless LAN BasicsIEEE 802.11 Wireless LAN BasicsIEEE 802.11 Wireless LAN Basics
MAC protocol is distributed (simple and successful)One queue per station (station = backoff entity)
MSDU can be fragmented into multiple MPDUs
RTS/CTS helps to improve efficiency
QoS involves achieving a minimum MSDU Delivery throughput and MSDU Delivery delays not exceeding a maximum limit
Delay variation and loss rate are often considered
IEEE 802.11 Task Group E (TGe) defines QoS mechanisms to be integrated into the legacy 802.11 MAC
This supplement standard is referred to as IEEE 802.11e (here: draft 4.0)
QoS Support in legacy 802.11? no!
Stefan Mangold - ComNets Aachen University 7
Contention-based medium access: EDCFDifferent EDCF parameters per Access Category (AC)
DIFSAIFS[AC]
CWminCWmin[AC]
*) not in current draft standard
802.11e Medium Access: HCF802.11e Medium Access: HCF802.11e Medium Access: HCF802.11e Medium Access: HCF
CTS
RTS
time
ACKSIFS
AIFS[AC=med.]
AIFS[AC=high](=PIFS)
PIFS
AIFS[AC=low]
SIFS
CW[AC=high]
with 802.11a: aSlotTime: 9us SIFS: 16us PIFS: 25us DIFS: 34us AIFSN: 1…10[slots] AIFS: >=PIFS
SIFS
highpriority AC
lowpriority AC
mediumpriority AC
backoff
backoff
busychannel
AIFS[AC] =SIFS + aSlotTime * AIFSN[AC]
aSlotTime
DCF: Random backoff counter isselected from interval 0...CW.Minimum interframe space is DIFS.Earliest channel access is DIFS.
EDCF: Random backoff counter isselected from interval 1...CW+1.Minimum interframe space is PIFS.Earliest channel access is DIFS.
earliest channel accessfor high priority AC
CW[AC=low]
CTS
RTS
time
ACKSIFS
AIFS[AC=med.]
AIFS[AC=high](=PIFS)
PIFS
AIFS[AC=low]
SIFS
CW[AC=high]
with 802.11a: aSlotTime: 9us SIFS: 16us PIFS: 25us DIFS: 34us AIFSN: 1…10[slots] AIFS: >=PIFS
SIFS
highpriority AC
lowpriority AC
mediumpriority AC
backoff
backoff
busychannel
AIFS[AC] =SIFS + aSlotTime * AIFSN[AC]
aSlotTime
DCF: Random backoff counter isselected from interval 0...CW.Minimum interframe space is DIFS.Earliest channel access is DIFS.
EDCF: Random backoff counter isselected from interval 1...CW+1.Minimum interframe space is PIFS.Earliest channel access is DIFS.
earliest channel accessfor high priority AC
CW[AC=low]
CWmaxCWmax[AC]
(PF=2PF[AC]*)
Stefan Mangold - ComNets Aachen University 8
Achievable ThroughputAchievable ThroughputAchievable ThroughputAchievable Throughput
Three different EDCF parameter setsAC (priority):higher medium(=legacy) lower
AIFSN[AC]:2 2 9
CWmin[AC]:7 15 31
CWmax[AC]:10231023 1023
PF[AC]:24/16 32/16 40/16
Question: achievable throughput per backoff entity in an isolated scenario? "saturation throughput"
Isolated scenario means: the same EDCF parameters are use by all backoff entities
Results depend on: frame body length, number of contending backoff entities, RTS/CTS, fragmentation
Approach: WARP2 stochastic simulation and analytical model (modifications of Bianchi’s legacy 802.11 model)
Stefan Mangold - ComNets Aachen University 9
512 byte frame body: 512 byte frame body, RTS/CTS:
2304 byte frame body: 2304 byte frame body, RTS/CTS:
Legacy (Medium) PriorityLegacy (Medium) PriorityLegacy (Medium) PriorityLegacy (Medium) Priority
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 80 100 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
Stefan Mangold - ComNets Aachen University 10
512 byte frame body: 512 byte frame body, RTS/CTS:
2304 byte frame body: 2304 byte frame body, RTS/CTS:
Low Priority (larger CWmin[AC])Low Priority (larger CWmin[AC])Low Priority (larger CWmin[AC])Low Priority (larger CWmin[AC])
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
thrp. increases with increasingnumber of backoff entities
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
thrp. increases with increasingnumber of backoff entities
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 0
0.2
0.4
0.6
0.8
1with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 0
0.2
0.4
0.6
0.8
1with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
Stefan Mangold - ComNets Aachen University 11
512 byte frame body: 512 byte frame body, RTS/CTS:
2304 byte frame body: 2304 byte frame body, RTS/CTS:
High Priority (smaller CWmin[AC])High Priority (smaller CWmin[AC])High Priority (smaller CWmin[AC])High Priority (smaller CWmin[AC])
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
deviation with highercollision probability
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
deviation with highercollision probability
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
deviation with highercollision probability
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
deviation with highercollision probability
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
deviation with highercollision probability
10 20 40 60 0
0.2
0.4
0.6
0.8
1
with address 4, w/o WEP encrypt.
number of backoff entities
satu
ratio
n th
rp. (
no
rm.)
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
deviation with highercollision probability
Stefan Mangold - ComNets Aachen University 12
Modified Bianchi ModelModified Bianchi ModelModified Bianchi ModelModified Bianchi Model
(1-p)/ W0
p/ Wi
1
1
11
11 1
11
p/ W1
p/ Wi+1
p/ Wm
p/ Wmp/ Wm
p/ Wi
k: slot index i: stage index m: maximum backoff stage p: collision probability W0: CWmin+1 Wm: CWmax+1
(1-p)/ W0“fireand
new“
“coll.“
“coll.“
“coll.“
m depends on Persistent Factor (PF)in the EDCF (proposed), CWmax, andthe retry counter.
0,20,0 0,1 0,W0-2 0,W0-1
i-1,0
m,k=2m,0 m,k=1
i, k=2i,0 i, k=1 i,Wi-1i,Wi-2
-2mWm, -1mWm,
“coll.“
slot index k
stageindex i
W0 W0[AC] in EDCF
Wm Wm[AC] in EDCF m m[AC] in EDCF
(1-p)/ W0
p/ Wi
1
1
11
11 1
11
p/ W1
p/ Wi+1
p/ Wm
p/ Wmp/ Wm
p/ Wi
k: slot index i: stage index m: maximum backoff stage p: collision probability W0: CWmin+1 Wm: CWmax+1
(1-p)/ W0“fireand
new“
“coll.“
“coll.“
“coll.“
m depends on Persistent Factor (PF)in the EDCF (proposed), CWmax, andthe retry counter.
0,20,0 0,1 0,W0-2 0,W0-1
i-1,0
m,k=2m,0 m,k=1
i, k=2i,0 i, k=1 i,Wi-1i,Wi-2
-2mWm, -1mWm,
“coll.“
slot index k
stageindex i
W0 W0[AC] in EDCF
Wm Wm[AC] in EDCF m m[AC] in EDCF
Stefan Mangold - ComNets Aachen University 13
Share of CapacityShare of CapacityShare of CapacityShare of Capacity
Saturation throughput shown so far is only valid for isolated scenariosNice to have, but useless for QoS support:
For QoS support, a backoff entity needs to know the expected throughput in mixed scenarios
Achievable throughput per backoff entity is referred to as "share of capacity"
Question: what is the share of capacity a backoff entity can achieve in a mixed scenario?
This is *THE* important question for EDCF QoS support
Bianchi model does not provide the answer
There is no solution available until today
Stefan Mangold - ComNets Aachen University 14
Access Probability per SlotAccess Probability per SlotAccess Probability per SlotAccess Probability per Slot
25 34 43 52 61 70 79 88 97 106 115 124 133 142 151 160 1690
0.1
0.2
0.3
0.4
0.5
0.6
pro
b(a
cce
ss)
slot [s]
AIFSN[higher pr.]=2
AIFSN[medium pr.]=2
AIFSN[lower pr.]=9
8 backoff entities per AC[higher]
8 backoff entities per AC[medium]
8 backoff entities per AC[lower]
higher pr.medium pr.lower pr.
25 34 43 52 61 70 79 88 97 106 115 124 133 142 151 160 1690
0.1
0.2
0.3
0.4
0.5
0.6
pro
b(a
cce
ss)
slot [s]
AIFSN[higher pr.]=2
AIFSN[medium pr.]=2
AIFSN[lower pr.]=9
8 backoff entities per AC[higher]
8 backoff entities per AC[medium]
8 backoff entities per AC[lower]
higher pr.medium pr.lower pr.
25 34 43 52 61 70 79 88 97 106 115 124 133 142 151 160 1690
0.1
0.2
0.3
0.4
0.5
0.6
pro
b(a
cce
ss)
slot [s]
AIFSN[higher pr.]=2
AIFSN[medium pr.]=2
AIFSN[lower pr.]=9
3 backoff entities per AC[higher]
3 backoff entities per AC[medium]
3 backoff entities per AC[lower]
higher pr.medium pr.lower pr.
25 34 43 52 61 70 79 88 97 106 115 124 133 142 151 160 1690
0.1
0.2
0.3
0.4
0.5
0.6
pro
b(a
cce
ss)
slot [s]
AIFSN[higher pr.]=2
AIFSN[medium pr.]=2
AIFSN[lower pr.]=9
3 backoff entities per AC[higher]
3 backoff entities per AC[medium]
3 backoff entities per AC[lower]
higher pr.medium pr.lower pr.
Stefan Mangold - ComNets Aachen University 15
Approximation of Expected Idle TimesApproximation of Expected Idle TimesApproximation of Expected Idle TimesApproximation of Expected Idle Times
Expected size of contention windowN[AC] = number of backoff entities of AC
tau[AC] = probability that a backoff entity is transmitting
Access probability per slotExpressed by geometric distribution
N AC
N AC
-persistent CSMA with N Bianchi approximation with Ncontending backoff entities per AC contending backoff entities per AC
1 AC1E CW AC E CW AC
AC 1 1 AC
!
N ACslot AIFS AC
slot AC 1 1 AC 1 AC
Stefan Mangold - ComNets Aachen University 16
CSMA Regeneration Cycle ProcessCSMA Regeneration Cycle ProcessCSMA Regeneration Cycle ProcessCSMA Regeneration Cycle Process C: inter-AC collision H: high priority access M: medium priority access L: low priority access
CWmax = max(CWmax[AC])
C
slot
1
H M L
slot-1
2
CWmax+1
P1,C
P1,LP1,MP1,H
P2,C
P2,LP2,MP2,H
1 111
P1,2
P2,3
Pslot-2, slot-1
Pslot-1, slot
Pslot, slot+1
PCWmax, CWmax+1
Pslot-1,C
Pslot-2,LPslot-1,MPslot-1,H
Pslot,C
Pslot,LPslot,MPslot,H
PCWmax+1,C
PCWmax+1,LPCWmax+1,M
PCWmax+1,H
C: inter-AC collision H: high priority access M: medium priority access L: low priority access
CWmax = max(CWmax[AC])
C
slot
1
H M L
slot-1
2
CWmax+1
P1,C
P1,LP1,MP1,H
P2,C
P2,LP2,MP2,H
1 111
P1,2
P2,3
Pslot-2, slot-1
Pslot-1, slot
Pslot, slot+1
PCWmax, CWmax+1
Pslot-1,C
Pslot-2,LPslot-1,MPslot-1,H
Pslot,C
Pslot,LPslot,MPslot,H
PCWmax+1,C
PCWmax+1,LPCWmax+1,M
PCWmax+1,H
State transition diagram for the Markov chain
States C, H, M, L represent busy system
States 1, 2, 3..., CWmax+1 represent idle system
Time is progressing in steps of a slot
State of the chain changes with state transition probabilities as indicated in the figure
Stefan Mangold - ComNets Aachen University 17
Markov Chain (1)Markov Chain (1)Markov Chain (1)Markov Chain (1)
Resulting state transition probabilitiesaccess:
collision:
idle:
slot,H slot slot slot
slot,M slot slot slot
slot,L slot slot slot
P High 1 Medium 1 Low ,
P Medium 1 High 1 Low ,
P Low 1 High 1 Medium .
slot,C slot slot slot
slot slot slot
slot slot slot
slot slot slot
P High Medium 1 Low
High Low 1 Medium
Medium Low 1 High
High Medium Low .
slot,slot 1slot,H slot,M slot,L slot,C
0, slot CWmaxP
1 P P P P , else
Stefan Mangold - ComNets Aachen University 18
Markov Chain (2)Markov Chain (2)Markov Chain (2)Markov Chain (2)
Resulting stationary distributionshigh:
other:
slot 1CWmax 1
H 1,H slot,H i,i 1 1slot 2 i 1
(this defines the relative priority of the AC "High")
p P P P p
AC High:
slot 1CWmax 1
M 1,M slot,M i,i 1 1 1slot 2 i 1
slot 1CWmax 1
L 1,L slot,L i,i 1 1 1slot 2 i 1
slot 1CWmax 1
C 1,C slot,C i,i 1 1slot 2 i 1
p P P P p : Medium p,
p P P P p : Low p,
p P P P p.
Stefan Mangold - ComNets Aachen University 19
ResultResultResultResult
The priority vector
Share of capacity
Modified Bianchi model provides the saturation throughput
H M L
AC
1 , , High , Medium , Low
AC
H
M
L
Thrp HighsatThrp Thrp Thrp Medium .share sat sat
Thrp Lowsat
Stefan Mangold - ComNets Aachen University 20
Scenario & Results (1)Scenario & Results (1)Scenario & Results (1)Scenario & Results (1)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
512 bytes frame body, no RTS/CTS
4+4+4 backoff entities
sha
re (
=th
rp.)
pe
r A
C (
no
rm.)
sim.
analyt.
AIFS:
CWmin:
16 PF:
higher priority legacy priority lower priority
variable pr. (high low) sim.variable pr. (high low) apprx.legacy pr. sim.legacy pr. apprx.lower pr. sim.lower pr. apprx.
2 2 2 2 2 2 2 2 2 2 2 2 3 4 5 6 7 8 9 9 9 9 9 9 9 9 9 9
7 7 7 7 7 8 9 10 12 13 14 15 15 15 15 15 15 15 15 17 19 21 23 25 27 29 31 31
24 26 28 30 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 40
analyt. sim.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
512 bytes frame body, no RTS/CTS
4+4+4 backoff entities
sha
re (
=th
rp.)
pe
r A
C (
no
rm.)
sim.
analyt.
AIFS:
CWmin:
16 PF:
higher priority legacy priority lower priority
variable pr. (high low) sim.variable pr. (high low) apprx.legacy pr. sim.legacy pr. apprx.lower pr. sim.lower pr. apprx.
2 2 2 2 2 2 2 2 2 2 2 2 3 4 5 6 7 8 9 9 9 9 9 9 9 9 9 9
7 7 7 7 7 8 9 10 12 13 14 15 15 15 15 15 15 15 15 17 19 21 23 25 27 29 31 31
24 26 28 30 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 40
analyt. sim.
receiving station
variablepriority
legacypriority
lowpriority
receiving station
variablepriority
legacypriority
lowpriority
Four backoff entities per AC (4/4/4)Variable, legacy and low priority
Results of WARP2 simulation indicate accurate approximation
Stefan Mangold - ComNets Aachen University 21
Scenario & Results (2)Scenario & Results (2)Scenario & Results (2)Scenario & Results (2)
10/2/4 backoff entities per ACBackoff entities with variable priority are more dominant, as expected
Results of WARP2 simulation indicate accurate approximation
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
512 bytes frame body, no RTS/CTS
10+2+4 backoff entities
sha
re (
=th
rp.)
pe
r A
C (
no
rm.)
sim.
analyt.
AIFS:
CWmin:
16 PF:
higher priority legacy priority lower priority
variable pr. (high low) sim.variable pr. (high low) apprx.legacy pr. sim.legacy pr. apprx.lower pr. sim.lower pr. apprx.
2 2 2 2 2 2 2 2 2 2 2 2 3 4 5 6 7 8 9 9 9 9 9 9 9 9 9 9
7 7 7 7 7 8 9 10 12 13 14 15 15 15 15 15 15 15 15 17 19 21 23 25 27 29 31 31
24 26 28 30 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 40
analyt.
sim.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
512 bytes frame body, no RTS/CTS
10+2+4 backoff entities
sha
re (
=th
rp.)
pe
r A
C (
no
rm.)
sim.
analyt.
AIFS:
CWmin:
16 PF:
higher priority legacy priority lower priority
variable pr. (high low) sim.variable pr. (high low) apprx.legacy pr. sim.legacy pr. apprx.lower pr. sim.lower pr. apprx.
2 2 2 2 2 2 2 2 2 2 2 2 3 4 5 6 7 8 9 9 9 9 9 9 9 9 9 9
7 7 7 7 7 8 9 10 12 13 14 15 15 15 15 15 15 15 15 17 19 21 23 25 27 29 31 31
24 26 28 30 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 40
analyt.
sim.
receiving station
variablepriority
legacypriority
lowpriority
receiving station
variablepriority
legacypriority
lowpriority
Stefan Mangold - ComNets Aachen University 22
Scenario & Results (3)Scenario & Results (3)Scenario & Results (3)Scenario & Results (3)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
512 bytes frame body, no RTS/CTS
2+10+4 backoff entities
sha
re (
=th
rp.)
pe
r A
C (
no
rm.)
sim.
analyt.
AIFS:
CWmin:
16 PF:
higher priority legacy priority lower priority
variable pr. (high low) sim.variable pr. (high low) apprx.legacy pr. sim.legacy pr. apprx.lower pr. sim.lower pr. apprx.
2 2 2 2 2 2 2 2 2 2 2 2 3 4 5 6 7 8 9 9 9 9 9 9 9 9 9 9
7 7 7 7 7 8 9 10 12 13 14 15 15 15 15 15 15 15 15 17 19 21 23 25 27 29 31 31
24 26 28 30 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 40
analyt.
sim.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
512 bytes frame body, no RTS/CTS
2+10+4 backoff entities
sha
re (
=th
rp.)
pe
r A
C (
no
rm.)
sim.
analyt.
AIFS:
CWmin:
16 PF:
higher priority legacy priority lower priority
variable pr. (high low) sim.variable pr. (high low) apprx.legacy pr. sim.legacy pr. apprx.lower pr. sim.lower pr. apprx.
2 2 2 2 2 2 2 2 2 2 2 2 3 4 5 6 7 8 9 9 9 9 9 9 9 9 9 9
7 7 7 7 7 8 9 10 12 13 14 15 15 15 15 15 15 15 15 17 19 21 23 25 27 29 31 31
24 26 28 30 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 40
analyt.
sim.
receiving station
variablepriority
legacypriority
lowpriority
receiving station
variablepriority
legacypriority
lowpriority
2/10/4 backoff entities per ACBackoff entities with variable priority are more dominant, as expected
WARP2 simulation results deviate for different persistent factors
Stefan Mangold - ComNets Aachen University 23
EDCF SummaryEDCF SummaryEDCF SummaryEDCF Summary
EDCF MAC protocol is distributed (as DCF, simple)Multiple queues per station (queue = backoff entity)The presented model can be used for prediction of expected share of capacity per backoff entityThe model can be extended towards delay and loss predictionEDCF supports QoS, but cannot guarantee as resulting share depends on activity of other backoff entities
QoS Support in legacy 802.11? no!
QoS Support in 802.11e EDCF? yes, but no guarantee!
Stefan Mangold - ComNets Aachen University 24
HCF Controlled Medium AccessHCF Controlled Medium AccessHCF Controlled Medium AccessHCF Controlled Medium Access
EDCF cannot guarantee QoS, because of distributed MAC
For guarantee, controlled medium access allows access right after PIFS, without backoff
Similar to polling in legacy 802.11 (PCF)
CTS
RTSAIFS[AC] DATA (MSDU)
ACK
EDCF-TXOP gained by contention-basedchannel access during contention period
duration < EDCF-TXOPlimit
QoS CF-Poll
optimal CAP allocationtime for HC 1
delayed start of TXOP
CAPallocation
PIFS
delayed CAP allocationtime for HC 1
time
tolerated by HC 1
under control of HC 1
busychannel
CTS
RTSAIFS[AC] DATA (MSDU)
ACK
EDCF-TXOP gained by contention-basedchannel access during contention period
duration < EDCF-TXOPlimit
QoS CF-Poll
optimal CAP allocationtime for HC 1
delayed start of TXOP
CAPallocation
PIFS
delayed CAP allocationtime for HC 1
time
tolerated by HC 1
under control of HC 1
busychannel
Stefan Mangold - ComNets Aachen University 25
HCF in Overlapping BSSHCF in Overlapping BSSHCF in Overlapping BSSHCF in Overlapping BSS
Controlled medium access requires an isolated BSS
No other backoff entity must access the medium with highest priority (after PIFS), otherwise collisions occur!
This is a very strict requirement, and difficult to achieve in an unlicensed frequency band
Dynamic frequency selection may help, as in HiperLAN/2
512 byte frame body: 2304 byte frame body:
1 2 10 0
0.2
0.4
0.6
0.8
1
number of HCs allocating CAPs
satu
ratio
n th
rp. (
no
rm.)
CW=0, AIFSN=1
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
1 2 10 0
0.2
0.4
0.6
0.8
1
number of HCs allocating CAPs
satu
ratio
n th
rp. (
no
rm.)
CW=0, AIFSN=1
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
1 2 10 0
0.2
0.4
0.6
0.8
1
number of HCs allocating CAPs
satu
ratio
n th
rp. (
no
rm.)
CW=0, AIFSN=1
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
1 2 10 0
0.2
0.4
0.6
0.8
1
number of HCs allocating CAPs
satu
ratio
n th
rp. (
no
rm.)
CW=0, AIFSN=1
BPSK1/2 (6 Mbit/s)16QAM1/2 (24 Mbit/s)64QAM3/4 (54 Mbit/s)
Stefan Mangold - ComNets Aachen University 26
HCF Controlled Access SummaryHCF Controlled Access SummaryHCF Controlled Access SummaryHCF Controlled Access Summary
The controlled medium access is often referred to as HCF
This coordination function is not distributed, it is centralized (requires a Hybrid Coordinator)
It works only in isolated scenarios, which is not a very likely scenario in unlicensed bands
The coexistence problem of overlapping BSSs will be discussed in the following
QoS Support in legacy 802.11? no!
QoS Support in 802.11e EDCF? yes, but no guarantee!
QoS Support with 802.11e HCF? not in unlicensed bands!
Stefan Mangold - ComNets Aachen University 27
Scenario: two BSSs Sharing one ChannelScenario: two BSSs Sharing one ChannelScenario: two BSSs Sharing one ChannelScenario: two BSSs Sharing one Channel
CCHC(player 2)
CCHC(player 1)
vectors indicate"has control over"
CCHC'sdetection ranges
802.11station
802.11station
HiperLAN/2station
HiperLAN/2station
CCHC(player 2)
CCHC(player 1)
vectors indicate"has control over"
CCHC'sdetection ranges
802.11station
802.11station
HiperLAN/2station
HiperLAN/2station
Basic service sets are modeled as players that attempt to optimize their outcomesSingle stage game: one superframe (~200ms)Multi stage game: repeated interaction
Stefan Mangold - ComNets Aachen University 28
The Superframe as Single Stage GameThe Superframe as Single Stage GameThe Superframe as Single Stage GameThe Superframe as Single Stage Game
[0...1]
QoS [0...1]
[0...1]
Allocation process during a superframe:
QoS:
SFDUR(n)[ms]
the periodic beacon is successfullytransmitted by one of the CCHCs
TBTT TBTT
time
1...L1 TXOPs allocatedby CCHC1 (here, L1=3)
d11(n) [ms] d3
1(n) [ms]d21(n) [ms]
DL1(n) = D3
1(n) [ms]D11(n) [ms] D2
1(n) [ms]
t11(n) t3
1(n)t21(n)
nth CCHC superframe = thenth single-stage game
allocatedby CCHC1
allocated byCCHC2
SFDUR(n)[ms]
the periodic beacon is successfullytransmitted by one of the CCHCs
TBTT TBTT
time
1...L1 TXOPs allocatedby CCHC1 (here, L1=3)
d11(n) [ms] d3
1(n) [ms]d21(n) [ms]
DL1(n) = D3
1(n) [ms]D11(n) [ms] D2
1(n) [ms]
t11(n) t3
1(n)t21(n)
nth CCHC superframe = thenth single-stage game
allocatedby CCHC1
allocated byCCHC2
Stefan Mangold - ComNets Aachen University 29
Abstract Representation of QoSAbstract Representation of QoSAbstract Representation of QoSAbstract Representation of QoS
Throughput: normalized share of capacity
Delay: normalized resource allocation interval
Jitter: normalized delay variation
,
iL (n)i i
ll 1
1(n) d (n)
SFDUR(n)
i
i imax l l 1...L (n) 1
1(n) max D (n)
SFDUR(n)
i
i i imax l l 1
l 1...L (n) 1
1(n) max D (n) D (n)
SFDUR(n)
Stefan Mangold - ComNets Aachen University 30
Player "i" and opponent player "–i" have individual requirementsPlayers select demands to meet requirementsThrough allocation process, players observe outcomes per single stage game: observed QoS
This single stage game is repeated with every superframePlayers adapt behaviors in the multi stage game
The PlayerThe PlayerThe PlayerThe Player
action ai of player i:select demand based
on requirement,observation, and
estimated demand ofplayer -i
z -1allocation process
requirement
time
ireq
ireq
iobs
iobs
n
n
idem
idem
n
n
demand of player -i
time
idem
idem
n
n
demand
time time
observation(outcome)
action ai of player i:select demand based
on requirement,observation, and
estimated demand ofplayer -i
z -1allocation process
requirement
time
ireq
ireq
iobs
iobs
n
n
idem
idem
n
n
demand of player -i
time
idem
idem
n
n
demand
time time
observation(outcome)
Stefan Mangold - ComNets Aachen University 31
Allocation Process (Formal Description)Allocation Process (Formal Description)Allocation Process (Formal Description)Allocation Process (Formal Description)
Required:
If this process can be formally described through an accurate approximation, we can analyze
Expected outcomes (existence of Nash equilibrium (NE))
Stability (convergence to NE)
Fairness (position of NE in bargaining domain)
It can be discussed…… what QoS support is feasible for the individual players (player = CCHC = BSS)
… what level of QoS can be achieved
… if mutual cooperation improves the outcome per player
i i idem dem obsi i idem dem obs
, , i, i {1,2}
.
Stefan Mangold - ComNets Aachen University 32
Observed payoffs in a single stage game:
Stationary distributions:p0: idle channel (EDCF background traffic)
p1: player 1 allocates radio resource
p2: player 2 backing off while player 1 allocates resource
State transition probabilities:
Markov ChainMarkov ChainMarkov ChainMarkov Chain
P23
P41
P43
P10 P21P34 P03
p0p4 p3 p2p1
P30 P01 P12
P23
P41
P43
P10 P21P34 P03
p0p4 p3 p2p1
P30 P01 P12
21,2dem
01 dem2 1dem dem
P , 0
1 11,2dem dem
12 dem2 2dem dem
P min 1, 01
Stefan Mangold - ComNets Aachen University 33
Result and EvaluationResult and EvaluationResult and EvaluationResult and Evaluation
Resulting observations for both players:
Comparison with simulation results (YouShi):
i ii dem demobs i i i i
dem dem dem dem
i i idem dem dem
allocationinterval unwanted increaseof allocationinterval (
iobs
demand delayed )
TXOPlimit
Stefan Mangold - ComNets Aachen University 34
The Utility FunctionThe Utility FunctionThe Utility FunctionThe Utility Function
Players attempt to meet their requirementsTherefore, players attempt to maximize the observed payoff (outcome), by using a utility function
i i i i i i i i idem obs req obs reqU U ( , , ) U ( , ), U
Stefan Mangold - ComNets Aachen University 35
Existence of Nash Equilibrium (NE)Existence of Nash Equilibrium (NE)Existence of Nash Equilibrium (NE)Existence of Nash Equilibrium (NE)
Proposition: in the Single Stage Game of two coexisting CCHCs exists a Nash equilibrium in the action space A.
Proof: show that the outcome (the payoff V) is continuous in A, and show that it is quasi-concave in Ai.
There exists at least one Nash equilibrium, which can be calculated as:
a=action, V=payoff, N=number of players (N=2)
idemi i i
idem
a a* 0 grad V (a) i , with grad
Stefan Mangold - ComNets Aachen University 36
Pareto EfficiencyPareto EfficiencyPareto EfficiencyPareto Efficiency
Players that take rational actions will automatically adjust into a NE (because there is at least one NE)
If the NE is unique, the respective action profile can be predicted as expected point of operation
However, there may exist action profiles in the single stage game that lead to higher payoffs
If such profiles do not exist, the NE is referred to as Pareto efficient (Pareto optimal)
Pareto efficiency can be determined by numerical search
Can be shown in bargaining domain … (next page)
Stefan Mangold - ComNets Aachen University 37
Bargaining DomainBargaining DomainBargaining DomainBargaining Domain
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
1
payoff of player i: V i(a
i,a
-i)
payo
ff o
f pl
ayer
-i:
V -
i (ai ,a
-i)
Nash equilibrium
Pareto boundary
fair share
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
1
payoff of player i: V i(a
i,a
-i)
payo
ff o
f pl
ayer
-i:
V -
i (ai ,a
-i)
Nash equilibrium
Pareto boundary
fair share
Stefan Mangold - ComNets Aachen University 38
Persist: demand=requirementShown are YouShi simulation results and analytical apprx.Poor delay performance for pl.2
Strategy: PersistStrategy: PersistStrategy: PersistStrategy: Persist
0
0.6
1
1
required observeddemanded
analyt. apprx.(dashed line not visible)
simulated(solid line)
arrow indicates thatplayers generallyattempt to maximizethroughput
some variationsbecause of EDCF
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.04
0.1
1
time (s), SFDUR = 200ms
required observed maxdemanded
analyt. apprx.(dashed line)
simulated(solid line)
arrow indicates thatplayers generallyattempt to minimizedelays
0
0.6
1
2
required observeddemanded
simulated(solid line)
analyt. apprx.(dashed line)
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.023
0.1
2
time (s), SFDUR = 200ms
required observed maxdemanded
simulated(solid line) analyt. apprx.
(dashed line)
pl1 pl2
Stefan Mangold - ComNets Aachen University 39
Persist/Best Response/CooperationPersist/Best Response/CooperationPersist/Best Response/CooperationPersist/Best Response/Cooperation
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
best
coop
persist
defect
time (s), SFDUR = 200ms
pl 1pl 2
both players demandrequirements throughoutall stages (BEH-P)
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.2
0.4
1
time (s), SFDUR = 200ms
Util
itie
s U
1,2
pl 1pl 2
in total, player 1 observes a higher payoff than player 2 whenboth demand their requirements
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
best
coop
persist
defect
time (s), SFDUR = 200ms
pl 1pl 2
after 2s, both players changetheir behavior from BEH-P toBEH-B independently, then attempting to improve their individual payoffs
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.2
0.4
1
time (s), SFDUR = 200ms
Util
itie
s U
1,2
pl 1pl 2
in total, player 2 gains and player 1suffers from playing the best responses
neither player is able to improve its outcomeby unilaterally changing its behavior from whatis demanded after the process converged into NE
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
best
coop
persist
defect
time (s), SFDUR = 200ms
pl 1pl 2
both players cooperate after 2s
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.2
0.4
1
time (s), SFDUR = 200ms
Util
itie
s U
1,2
pl 1pl 2
in total, payoffs are higher in cooperationthan in NE, therefor the NE is not Paretoefficient in this example
Stefan Mangold - ComNets Aachen University 40
Cooperation can be beneficial for both players, and is established in repeated interactions (multi stage game)Cooperation and punishment:
Payoff discounting in multi stage game:
How to establish CooperationHow to establish CooperationHow to establish CooperationHow to establish Cooperation
n=n0
for a number of stages,depending on discouning factorCOOPERATE:
BEH-CPUNISH(1):
BEH-DPUNISH(n’):
BEH-D
opponentdefects
any behaviorof opponent
any behaviorof opponent
otherwise
n=n0
for a number of stages,depending on discouning factorCOOPERATE:
BEH-CPUNISH(1):
BEH-DPUNISH(n’):
BEH-D
opponentdefects
any behaviorof opponent
any behaviorof opponent
otherwise
i n iMSG
n 0V V (n)
Stefan Mangold - ComNets Aachen University 41
Condition for CooperationCondition for CooperationCondition for CooperationCondition for Cooperation
It is more efficient to cooperate instead of defect (instead of playing best response), if…
It depends on the discounting factor (importance/shadow of future) if mutual support is achievable:
The more important the future is, the more likely is the establishment of cooperationFor example, CCHCs will interact for many superframes
n n' 1k k ki i i i i i i
CC DC CD CCk n k n 1 k n n' 1
V V V V
i ii CC DC
i iCD DC
V V
V V
Stefan Mangold - ComNets Aachen University 42
Dependence on Discounting FactorDependence on Discounting FactorDependence on Discounting FactorDependence on Discounting Factor
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
i
VCOOPi ( i=1)
i = 1
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
i
VCOOPi ( i=1)
i = 1
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
i
VCOOPi ( i=0.8)
i = 0.8
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
i
VCOOPi ( i=0.8)
i = 0.8
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
i
VCOOPi ( i=0.75)
i = 0.75
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
i
VCOOPi ( i=0.75)
i = 0.75
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
iV
COOPi ( i=0.6)
i = 0.6
1 2 3 4 5 6 7 8 9 100.5
1
1.5
stages of punishment through player -i
VM
SG
i o
f pla
yer
iV
COOPi ( i=0.6)
i = 0.6
Future counts
Future is less important
Stefan Mangold - ComNets Aachen University 43
Wrap UpWrap UpWrap UpWrap Up
There is always a Nash equilibrium in the single stage game
If the outcome of the Nash equilibrium is not satisfying, a player may attempt to punish the opponent, for establishment of mutual support
Depending on the behaviors of the CCHCs (the interacting players), and their requirements, cooperation can be achieved
QoS can be supported if cooperation is established
QoS Support in legacy 802.11? no!
QoS Support in 802.11e EDCF? yes, but no guarantee!
QoS Support with 802.11e HCF? not in unlicensed bands!
QoS Support with shared radio resources? with mutual support: yes!
Stefan Mangold - ComNets Aachen University 44
ConclusionsConclusionsConclusionsConclusions
IEEE 802.11e EDCF will provide basic means for QoS support
The controlled medium access of HCF (polling) cannot support QoS in unlicensed frequency bands
New analytical model for EDCF is developedallows to predict and control QoS
New approach for coexisting radio networksmay help radio networks operating in unlicensed bands to support QoS
Results will be used in …Contributions to IEEE 802.11e
IEEE 802.19 coexistence discussions
Spectrum etiquette development at Wi-Fi alliance
Development of Spectrum Agile Radios (DARPA)
Backup SlidesBackup Slides
Stefan Mangold - ComNets Aachen University 46
ArchitectureArchitectureArchitectureArchitecture
WirelessStation Wireless
Station
WirelessStationIBSS
Wired Station(Access Point, AP)
WirelessStation
WirelessStation
WirelessStation
BSS
Wired Station(Access Point, AP)
WirelessStation
WirelessStation
WirelessStation
BSS
802.x LANvia Portal
DS
WirelessStation Wireless
Station
WirelessStationIBSS
Wired Station(Access Point, AP)
WirelessStation
WirelessStation
WirelessStation
BSS
Wired Station(Access Point, AP)
WirelessStation
WirelessStation
WirelessStation
BSS
802.x LANvia Portal
DS
Infrastructure Basic Service Set (BSS)one station is the access point
Independent Basic Service Set (IBSS)ad-hoc
Stefan Mangold - ComNets Aachen University 47
Medium Access - ExampleMedium Access - ExampleMedium Access - ExampleMedium Access - Example
Station 1 initiates frame exchange firstOther stations set the Network Allocation Vector (NAV)Distributed approach difficult for station to support QoS
CTS
RTS
time
randombackoff(7 slots)
randomback-off(9 slots)
station 3defers, but
keeps backoffcounter (=2)
ACK
DATA
new randombackoff
(10 slots)
stationdefersDATA
ACK
ACK
DATA
remainingbackoff(2 slots)
SIFS
SIFS
SIFS
SIFS
SIFS
DIFS
DIFS
DIFS
DIFS
NAVs
station 1
station 2
NAVreset
stations set NAV uponreceiving RTS
station 6 sets NAV upon receiving CTS,this station is hidden to station 1
NAVupdates
station 5
station 4
station 3
station 6
NAV (timer)
transmission
CTS
RTS
time
randombackoff(7 slots)
randomback-off(9 slots)
station 3defers, but
keeps backoffcounter (=2)
ACK
DATA
new randombackoff
(10 slots)
stationdefersDATA
ACK
ACK
DATA
remainingbackoff(2 slots)
SIFS
SIFS
SIFS
SIFS
SIFS
DIFS
DIFS
DIFS
DIFS
NAVs
station 1
station 2
NAVreset
stations set NAV uponreceiving RTS
station 6 sets NAV upon receiving CTS,this station is hidden to station 1
NAVupdates
station 5
station 4
station 3
station 6
NAV (timer)
transmission
Stefan Mangold - ComNets Aachen University 48
Multiple Backoff Entities per StationMultiple Backoff Entities per StationMultiple Backoff Entities per StationMultiple Backoff Entities per Station
transmission
one priority
backoff:DIFS
151023
legac y 802.11 stationwith one backoff entity:
PF[AC] notpart of
802.11e
upon parallel access at the same slot, the higher priority ACbackoff entity transmits, the other backoff entity/entities act
as if a collision occured
transmission
higher priority lower priority
4 Access Categories AC0 - AC3 representing 4priorities, with 4 independent backoff entities
AC0
backoff:AIFSN[0]CWmin[0]CWmax[0]
AC1
backoff:AIFSN[1]CWmin[1]CWmax[1]
AC2
backoff:AIFSN[2]CWmin[2]CWmax[2]
AC3
backoff:AIFSN[3]CWmin[3]CWmax[3]
IEEE 802.11e station with four backoff entities:
8 priorities 0 - 7 according to 802.1D aremapped to 4 Access Categories (ACs)
7 4 015 236
AIFSN = 1,2,3…AIFS = SIFS + aSlotTime x AIFSN
backoffentity
backoffentity
transmission
one priority
backoff:DIFS
151023
legac y 802.11 stationwith one backoff entity:
PF[AC] notpart of
802.11e
upon parallel access at the same slot, the higher priority ACbackoff entity transmits, the other backoff entity/entities act
as if a collision occured
transmission
higher priority lower priority
4 Access Categories AC0 - AC3 representing 4priorities, with 4 independent backoff entities
AC0
backoff:AIFSN[0]CWmin[0]CWmax[0]
AC1
backoff:AIFSN[1]CWmin[1]CWmax[1]
AC2
backoff:AIFSN[2]CWmin[2]CWmax[2]
AC3
backoff:AIFSN[3]CWmin[3]CWmax[3]
IEEE 802.11e station with four backoff entities:
8 priorities 0 - 7 according to 802.1D aremapped to 4 Access Categories (ACs)
7 4 015 236
AIFSN = 1,2,3…AIFS = SIFS + aSlotTime x AIFSN
backoffentity
backoffentity
Stefan Mangold - ComNets Aachen University 49
Markov ChainMarkov ChainMarkov ChainMarkov Chain
slot,AC
slot,C
slot,slot 1
P s t 1 AC| s t slot P , AC H,M,L,
P s t 1 C| s t slot P ,
P s t 1 slot 1| s t slot P , slot 1 CWmax 1
ACt
Ct
slott
limP s t AC p , AC H,M,L,
limP s t C p ,
limP s t slot p , slot 1 CWmax 1
State transition probabilities
Stationary distributions
Stefan Mangold - ComNets Aachen University 50
Allocation Process (Example)Allocation Process (Example)Allocation Process (Example)Allocation Process (Example)
Two single stage games (two superframes):
Two players interact with each otherA third player models the EDCF background trafficFor analysis, a formal description of this process is needed
0 40 80 120 160 200 240 280 320 360 400
beacons(at TBTTs)
player 1
player 2
player 3 (EDCF)
TX
OP
s
time [ms]
collisioncollision collision
0 40 80 120 160 200 240 280 320 360 400
beacons(at TBTTs)
player 1
player 2
player 3 (EDCF)
TX
OP
s
time [ms]
collisioncollision collision
Stefan Mangold - ComNets Aachen University 51
Best Response: adapt demand to achieve highest outcome (myopic competition)
Action profile (demand) converges to NE
0
0.6
1
1
required observed demanded
apprx. sim.
observed thrp. decreases, because now theopponent player 2 plays its best response
as both players play theirbest responses, the demandsconverge into Nash equilibrium (NE)
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.04
0.1
1
time (s), SFDUR = 200ms
required observed maxdemanded
sim. apprx.
demanding NE
0
0.6
1
2
required observed demanded
sim. apprx.
now demanding high thrp. when converging into NE
this player gains from playingthe best response
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.023
0.1
2
time (s), SFDUR = 200ms
required observed maxdemanded
sim. apprx.
demanding NE
Strategy: Best ResponseStrategy: Best ResponseStrategy: Best ResponseStrategy: Best Response
pl1 pl2
Stefan Mangold - ComNets Aachen University 52
Cooperation: reduced demand, shorter resource allocationsNow both players achieve higher outcomes (next page…)
Strategy: CooperationStrategy: CooperationStrategy: CooperationStrategy: Cooperation
0
0.6
1
1
required observeddemanded
apprx.(not visible)
sim.
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.04
0.1
1
time (s), SFDUR = 200ms
required observed maxdemanded
apprx.
sim.
0
0.6
1
2
required observeddemanded
apprx. sim.
0.2 1 1.8 2.6 3.4 4.2 5 5.8 6.6 7.4
0
0.023
0.1
2
time (s), SFDUR = 200ms
required observed maxdemandedapprx.
sim.
pl1 pl2