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Jiwoong LeeUniversity of California, Berkeley
Zero Collision Random Backoff Algorithm
EE228A High speed Comm Networks
May 2 2006
2/28
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
Motivating Problem:Motivating Problem:Shared wireless medium Shared wireless medium -- How is the effect of Collision ?How is the effect of Collision ?
Example— Bechtel Engineering Library
— Some event driven-distributed sensor networks
EarthquakeSurveillance
Throughput
Size
100%
79% Theoretical limit
802.11family
3/28
Research Initiative QuestionResearch Initiative Question
Environment— In a fully distributed random access network— Without any central coordinated function
Will it be possible to build a — Zero Collision Probability Random Access
Backoff Algorithm ?
!!
BSS
(with BS)
Source/Target PairCollision DomainNetwork
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
4/28
TerminologyTerminology
Size of a network: # of current members
Capacity of a network: Max # of supportable members
Backoff slot: Allowed slot to start to access the medium
Contention Window: Available backoff slots
p-Persistency: transmission with prabability p when allowed
Chatty station: high p
Shy station: low p
Bursty station: p goes up and down
Member station: associated with BS. BS is a member.
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
5/28
Two Principles of the ZeroCollision MACTwo Principles of the ZeroCollision MACRelaxing the infinite Soft capacity constraint
– cf. Statistical Multiplexing Infinite soft capacity
— Physical systems’ Hard capacity limit802.11 family: Beacon TIM Partial Virtual BitmapHandles max 2008 associations
— Performance limitAs the network size grows
— Coverage limit802.11a: 20m range, 802.11b: 100m range, 802.11g: 50m range
This is an important observation. The small range of transmission guarantees small propagation delay and small delay spread, reducing the chance of carrier sensing error.
Sibley Auditorium: 40 laptop users among 250 attendees— User’s Mobility Pattern
No new extra join/leave at least for a few minutes
Learning— Each station remembers history of past successful transmission
and collision (Deterministic / Statistic)
CW=81st Contention
CW=162nd
No Learning
Learning
CW=83rd
STA1STA2
STA1STA2
…
…CW=8 CW=8 CW=8
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
6/28
Conventional diagram of the Backoff procedure
A Key Observation: Sufficient Statistic for Access(from each station’s point of view)
— Idle slots (Stations actively count them)
— First slot of transmission
Modeling: Backoff procedureModeling: Backoff procedure
CW
…………
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
7/28
Modeling: Backoff MatrixModeling: Backoff MatrixCW=8 fixed. CW=8 fixed.
Each station has— Self vector— Neighbor vector— Pointer vector
— Backoff Matrix
Operation— At each Idle slot: 1 unit
Cyclic Right Shift of P
— eg.
0 0 0 1 0 0 0 00 0 1 0 0 1 0 01 0 0 0 0 0 0 0
⎡ ⎤ ⎡ ⎤⎢ ⎥ ⎢ ⎥= =⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦
SB N
P
[1 0 0 0 0 0 0 0]=P
[0 0 0 1 0 0 0 0]=S[0 0 1 0 0 1 0 0]=N
↑
st
nd
On initialization, [0] [1 0 0 0 0 0 0 0]After 1 idle slot, [1] [0 1 0 0 0 0 0 0]After 2 idle slot, [2] [0 0 1 0 0 0 0 0]
=
=
=
PPP
1[ 1] [ ] Tn n+ =P P C
1
0 1 0 00 0 1 0 00 0 1 0 00 0 1 0 00 0 1 0 00 0 1 00 0 11 0 0
T
⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥= ⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦
C
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
8/28
Modeling: Problem StatementModeling: Problem Statement
Station is allowed to transmit when
Collision criterion— Local criterion:
Not exact. Suboptimal— Global criterion:
Exact. Optimal
We avoid Global criterion since— Exactness ← Global knowledge← Central Coordination
— However, I will show later that a network using local criterion for the zero collision in medium access will converge to one which is using global criterion
Now, we reduce the original fuzzy problem into two specific problems— Q. How to update N ?— Q. How to select S given N ?— Many subproblems will be defined one by one
0 or 0T T T⋅ > >S P N P S N
0T >S P
0M M
T Ti i j j
i j i≠⋅ >∑∑S P S P
??
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
9/28
Modeling: FormalizationModeling: Formalization
Scheduling Dynamics— Whenever idleslot is sensed
Zero Collision Lemma— A network achieves Zero Collision iff
1[ 1] [ ][ 1] ( [ ], [ ], )[ 1] ( [ ], [ ], )
Ti i
i i i
i i i
n nn g n n ChannelBusyIndicatorn f n n CollisionIndicator
+ =+ =+ =
P P CN N PS S N
⑴
⑵
⑶
⑷
⑸
⑹
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
{ }
[ ] [ ] 1
[ ] [ ] 0
[ ] [ ] [ ] [n],
for any , 1,2, , , for any
MTi i
iTi i
i i j j
n n
n n
n n n
i j M n ConvergenceTime
≤
=
+ = +
∈ ≥
∑S P
S N
S N S N
10/28
Result: ZeroCollision AlgorithmResult: ZeroCollision AlgorithmCSMA/ZCCSMA/ZC
If a station is not transmitting, receiving, and if the medium is detected as idle at least for [DIFS-SLOTTIME], do the followings:— If an idle slot is detected,
Decrement the slot indicator in Neighbor vectorAdvance Pointer vector
— If a busy slot is detected,Update the slot indicator in Neighbor vector
— If inner product of Self vector and Pointer vector is positive,
If tx buffer is not empty, transmit the packet— If a previous tx packet is not acknowledged,
Clear the indicator of Self vector, Choose randomly a vacant slot among the Neighbor vector, and set it to Self vector (#)
— If inner product of Self vector and Neighbor vector is positive,
Do (#)
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
11/28
1
2
3
4
0 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0
0 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0
0 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0
0 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
How to Update N and S: A vanilla exampleHow to Update N and S: A vanilla exampleCW=8, 4 stations, on Simultaneous initialization, Saturated QueuCW=8, 4 stations, on Simultaneous initialization, Saturated Queuee
T0 T1 T2 T3
T4 T5 T6 T10
1
2
3
4
0 0 0 1 0 0 0 00 0 0 0 0 0 0 01 0 0 0 0 0 0 0
0 0 1 0 0 0 0 00 0 0 0 0 0 0 01 0 0 0 0 0 0 0
0 0 0 0 0 0 1 00 0 0 0 0 0 0 01 0 0 0 0 0 0 0
0 0 0 1 0 0 0 00 0 0 0 0 0 0 01 0 0 0 0 0 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
1
2
3
4
0 0 0 1 0 0 0 00 0 0 0 0 0 0 00 1 0 0 0 0 0 0
0 0 1 0 0 0 0 00 0 0 0 0 0 0 00 1 0 0 0 0 0 0
0 0 0 0 0 0 1 00 0 0 0 0 0 0 00 1 0 0 0 0 0 0
0 0 0 1 0 0 0 00 0 0 0 0 0 0 00 1 0 0 0 0 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
1
2
3
4
0 0 0 1 0 0 0 00 0 0 0 0 0 0 00 0 1 0 0 0 0 0
0 0 1 0 0 0 0 00 0 0 0 0 0 0 00 0 1 0 0 0 0 0
0 0 0 0 0 0 1 00 0 0 0 0 0 0 00 0 1 0 0 0 0 0
0 0 0 1 0 0 0 00 0 0 0 0 0 0 00 0 1 0 0 0 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
1
2
3
4
0 0 0 1 0 0 0 00 0 1 0 0 0 0 00 0 0 1 0 0 0 0
0 0 1 0 0 0 0 00 0 0 0 0 0 0 00 0 0 1 0 0 0 0
0 0 0 0 0 0 1 00 0 1 0 0 0 0 00 0 0 1 0 0 0 0
0 0 0 1 0 0 0 00 0 1 0 0 0 0 00 0 0 1 0 0 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
1
2
3
4
0 1 0 0 0 0 0 00 0 1 1 0 0 0 00 0 0 0 0 1 0 0
0 0 1 0 0 0 0 00 0 0 1 0 0 0 00 0 0 0 0 1 0 0
0 0 0 0 0 0 1 00 0 1 1 0 0 0 00 0 0 0 0 1 0 0
0 0 0 0 0 1 0 00 0 1 1 0 0 0 00 0 0 0 0 1 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
1
2
3
4
0 1 0 0 0 0 0 00 0 1 1 0 0 0 00 0 0 0 1 0 0 0
0 0 1 0 0 0 0 00 0 0 1 0 0 0 00 0 0 0 1 0 0 0
0 0 0 0 0 0 1 00 0 1 1 0 0 0 00 0 0 0 1 0 0 0
0 0 0 0 0 1 0 00 0 1 1 0 0 0 00 0 0 0 1 0 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
1
2
3
4
0 1 0 0 0 0 0 00 0 1 1 0 1 1 0
0 0 0 0 1 0 0 0
0 0 1 0 0 0 0 00 1 0 1 0 1 1 0
0 0 0 0 1 0 0 0
0 0 0 0 0 0 1 00 1 1 1 0 1 0 0
0 0 0 0 1 0 0 0
0 0 0 0 0 1 0 00 1 1 1 0 0 1 0
0 0 0 0 1 0 0 0
⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
B
B
B
B
12/28
Expected result of the ZeroCollision MACExpected result of the ZeroCollision MAC
Throughput
Size
100%
79% Theoretical limit
802.11family
Competitors
ZC
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
13/28
Proof of ConceptProof of ConceptBuilding simulators based on ZC algorithmBuilding simulators based on ZC algorithm
ZeroSim: Two Visual MAC Simulators— Perform the same operations— Simulator 1:
1700 lines of Matlab codeScalar processingEmulates each station’s behavior Slow
— Simulator 2:2100 lines of Matlab codeVector processingEmulates a network’s behavior FasterVisualization module is shared with Simulator 1.
Experiment Platform— Intel Pentium4 x 2 nodes— Intel Xeon Cluster computer with 62 nodes
with Dual CPUs
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
14/28
ZeroSim Screenshot ZeroSim Screenshot –– ZC modeZC mode
Base Station
Subscriber Stations
Data Frame
Ack Frame
*Frame lengths are distorted for better visual understanding
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
15/28
ZeroSim Screenshot ZeroSim Screenshot –– 802.11 csma mode802.11 csma mode
*Frame lengths are distorted for better visual understanding
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
16/28
Demo: SimulationDemo: Simulation
17/28
Experiment configurationExperiment configurationTotal 21000 sets of simulation. All are done on the same PHYTotal 21000 sets of simulation. All are done on the same PHY
Saturated queueSaturated queue
Fixed Data frame 200 Bytes
Fixed Data frame 200 Bytes
20 μsec20 μsecSlot time
10 μsec10 μsecSIFS
50 μsec50 μsecDIFS
After 0 no useAfter 5 no useAccess Slot recycle
1 Mbps1 MbpsPLCP overhead Rate
Preamble 18Bytes PLCP header 6 Bytes
Preamble 18Bytes PLCP header 6 BytesPLCP overhead
Ack frame 14 BytesAck frame 14 Bytes
Traffic Model
4, ....,1284, ....,128Network size
32~1024(Dynamic)32 (or 64, 128)Congestion Window
11 Mbps11 MbpsMax Rate
802.11 CSMAZero CollisionMAC
*Most of these parameters are compatible to IEEE 802.11b/HR/DSSS/Long Preamble PHY Specification
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
18/28
Empirical results: Convergence timeEmpirical results: Convergence timeAverage of 100 times simulationAverage of 100 times simulation
ZeroCollision is achieved— After Convergence time, collision is completely free
Immediate convergence— Almost Immediate convergence— Typically less than 150 msec for all users.— Practically convergence time is lesser than this(due to small
association packets)
802.11 CSMA has ∞ convergence time
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
19/28
Empirical results: Channel UtilizationEmpirical results: Channel Utilization= Busytime/Totaltime= Busytime/Totaltime
ZeroCollision MAC > 802.11 CSMA— Theoretical limit is achieved by increasing frame length and
size of network
Is Channel Utilization a good metric ?— No. High collision network may have high utilizatoin
Theoretical limit 97.2%
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
20/28
Empirical results: Goodput ratioEmpirical results: Goodput ratio= Successfully delivered bytes / Trasnmitted bytes= Successfully delivered bytes / Trasnmitted bytes
For Pre-convergence— ZeroCollision MAC < 802.11 CSMA
For Post-convergence— ZeroCollision MAC > 802.11 CSMA
For CSMA— Goodput ratio is strictly decreasing as expected as Network
size grows
Theoretical limit 100%Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
21/28
Empirical results: ThroughputEmpirical results: Throughput= Successfully delivered bytes / Unit time / Max TX Rate= Successfully delivered bytes / Unit time / Max TX Rate
Theoretical limit 79%
For Pre-convergence— ZeroCollision MAC > 802.11 CSMA
For Post-convergence— ZeroCollision MAC > 802.11 CSMA— Theoretical limit is achievable by increasing Frame length and
Size of Network
For 802.11 CSMA— Increasing upto Size of Network = 32*
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
22/28
Features of ZeroCollisionFeatures of ZeroCollision
Guaranteed Upper Delay Bound: Dupper
— 802.11 CSMA, Dupper= ∞— ZC, Dupper= α x Network size
This holds for any kind of traffic modelα = 2.164 msec for 802.11b/HR/DSSS/Longα = 0.839 msec for 802.11a/DSSS-OFDM/LongFor 802.11b, α = Preamble + PLCP header + MAX_MPDU + SIFS + Preamble + PLCP header + MPDUforACK + DIFSFor 802.11a, α = Preamble + PLCP header + OFDM Training sequence + OFDM Signal + MAX_Data + Signal Extension + SIFS + Preamble + PLCP header + OFDM Training sequence + OFDM Signal + ACK + Signal Extension +DIFS
PT=Prob(Throughput Theoretical Limit)*— 802.11 CSMA:PT 0 as Framesize increases— ZC: PT 1 as Framesize increases
Outperforms PCF*— No resource polling/reservation overhead
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
23/28
Application: VoIP PerformanceApplication: VoIP PerformanceCapacity calculation, without silence suppressionCapacity calculation, without silence suppression
Traffic target model— Latency budget=40msec between SS and BS— Voice: G.711. 64Kbps 320 Bytes/40msec— Frame: 394 Bytes/frame— P(Loss)=0
In 802.11b PHY— One source’s portion / 40msec
= Preamble + PLCP header + MPDU + SIFS + Preamble + PLCP header + MPDUforACK + DIFS= 0.741msec
— VoIP Capacity = [40msec / 0.741msec] = 54 sources = 27 sessions
In 802.11a PHY— One source’s portion / 40msec
= Preamble + PLCP header + OFDM Training sequence + OFDM Signal + Data + Signal Extension + SIFS + Preamble + PLCP header + OFDM Training sequence + OFDM Signal + ACK + Signal Extension +DIFS= 0.541 msec
— VoIP Capacity = [40msec / 0.541msec] = 74 sources = 37 sessions
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
24/28
Application: VoIP PerformanceApplication: VoIP Performance802.11 CSMA vs ZeroCollision802.11 CSMA vs ZeroCollision
*N. Hedge, A. Proutiere, and J. Roberts, "Evaluating the voice capacity of 802.11 WLAN under distributed contorl," Proc. LANMAN, 2005
ZeroCollision MAC
400% Improvement •Capacity•Throughput(under the same latency budget)
802.11 CSMA*
25/28
Application: VoIP PerformanceApplication: VoIP PerformanceComparisonComparison
5.3%0.4%
32%8%
Throughput
T. Tung302%Average 20ms54Mbps802.11e
J. Lee560%Average 20ms11MbpsZeroCollision
G.729 8kbps Codec
G.711 64Kbps Codec
J. Lee740%Average 20ms11MbpsZeroCollision
Average 20ms
Latency
14
Capacity
N. HedgeUnknown11Mbps802.11b
ReferenceLoss RateMax Tx RateMAC
26/28
Qualitative Comparison of MACsQualitative Comparison of MACs:: Shopping guide:: Shopping guide
HighHighLowOverhead
★ ★★ ★ ★ ★★ ★ ★Stars
Infinite SoftHardOptionally Dynamic-Hard
HardCapacity
AdaptiveAdaptiveHardConfiguration
Bursty(one packet/period)
Saturated Queue
Bursty(one packet/period)
Saturated Queue
Tmax/MTmaxTmax/M
Approaches to 0TmaxTmaxThroughput
Approaches to UmaxUmaxUmax/M
Umax/CUmaxUmaxChannel Utilization
UnboundedUpper boundedUpper boundedDelay
Shortterm UnfairLongterm Fair
Access FairThroughput FairDelay Fair
Access FairThroughput FairDelay Fair
Fairness
CSMAZeroCollisionTDMAGSM
MAC
Controversial
More ★ ★ ★More Joyful shopping!
27/28
Further ResearchFurther ResearchApplication Area
— Any type of Shared medium accessIncluding the Shared LANBUS architecture of computer system
Bursty node support— Allow Multiple backoff slots for the chatty station
Performance impact of Traffic models— Elastric traffic— Elastric + Realtime traffic
Statistical Learning— Extension to rational numbers— Previous example was a Deterministic Learning
(The station thinks it backoffs with the probability of 1 to avoid the future collisions)
Safeguard against — imperfect carriersensing— Power saving nodes
Dynamic network support— Dynamic CW adjustment
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
28/28
ColclusionColclusionPerformance degradation of 802.11 CSMA
— Collision increases as the network grows
Two principles— Relaxing the infinite soft capacity condition— Learning (from the past)
Crucial Observation— Idle slot as a Sufficient statistic
Proof of Concept: ZeroSim
Performance enhancement— Upper bounded delay— Achievable maximum throughput— 400% capacity/throughput enhancement for VoIP
Comments request
Observation
Initiative Question
Terminology
Principles of ZC
Modeling
Algorithm
Expected Results
Proof of Concept
Empirical results
Features of ZC
VoIP Performance
MAC Comparison
Further Research
Conclusion
29/28
Design Philosophy IDesign Philosophy IFully Distributed Decision
— Every station should be autonomous— No Central Coordination
No predefined schedulingNo Reservation Request – Confirmation based scheduling
Least Memory Size— The memory size used to exploit the transmission
history should be minimized.
Maximize the overall Throughput— The overall throughput of the network should be
relatively increased
Utilization— When chatty stations and shy stations co-exist, the
utilization should not be degraded.Think about TDM case. Let’s avoid it.
Fairness— When plural chatty stations exist, the equillribrium
point of the share should be unique. — The share of greedy stations should meet at the unique
equilllibrium point with the fair shares
30/28
Design Philosophy IIDesign Philosophy IIComputational Complexity
— Computational complexity required at each station should be O(n1)
— Suboptimality of the solution is welcome.
Backward Compatibility— The algorithm should be modular enough— The algorithm should be highly compatible to the well-
known wireless LAN technologies – such as IEEE 802.11This means the possibility of co-existence of Exponential Backoff algorithm and Zero Collision Backoff algorithm
Generality— The algorithm should be easily applied to other kinds of
random access networks.
Support of Power Saving stations— They should not be ruled out of the benefit of the
algorithm
Support of Non-stationary network— The algorithm should be flexible enough to
accommodate a highly non-stationary network.