link layer multicasting with smart antennas: no client left behind
Post on 08-Jan-2016
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Link Layer Multicasting with Smart Antennas:
No Client Left Behind
Souvik Sen, Jie Xiong, Rahul Ghosh, Romit Roy Choudhury
Duke University
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Widely used service
Interactive classrooms, Smart home, Airports …
MobiTV, Vcast, MediaFlo …
Single transmission to reach all clients
Wireless Multicast Use-Cases
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Today: Multicast rate dictated by rate of weakest
client (1 Mbps) Inefficient channel utilization
Goal: Improve multicast throughput Uphold same reliability
Motivation
1 Mbps
11 Mbps
5.5 Mbps
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1. Scattered clients, different channel conditions
2. Time-varying wireless channel
3. Absence of per-packet feedback
Problem is Non-Trivial
1 Mbps
11 Mbps
5.5 Mbps
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Solution – also Non-Trivial
1 Mbps
11 Mbps
Low rate transmission leads to lower throughput High rate transmission leads lower fairness
Past research mostly assume omnidirectional antennas
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Problem Validationthrough Measurements
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Measurements in Duke Campus
AP
Clients
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Measurements in Duke Campus
AP
ClientsTransmission @ 1 Mbps
AP
Clients
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Measurements in Duke Campus
Transmission @ 2 Mbps
AP
Clients
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Measurements in Duke Campus
Transmission @ 5.5 Mbps
AP
Clients
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AP
Clients
Measurements in Duke Campus
Transmission @ 11 Mbps
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Measurements in Duke Campus
Client index
Deli
very
R
ati
o
Topologies are characterized by very few weak clients
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Reality
Weak clients tend to be clustered over small regions
shadow
regions
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Intuition
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3
4
5
6
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Intuition
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3
4
5
6
1 Mbps Omni
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Intuition
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3
4
5
6
11 Mbps Omni
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Intuition
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3
4
5
64 Mbps Directional
11 Mbps Omni
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Intuition
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3
4
56
1 Mbps Omni
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3
4
56
4 Mbps Directional
11 Mbps Omni
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Intuition to Reality
Few directional transmissions to cover few clients
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Partitioning the client set with optimal omni and directional rates
Estimation of wireless channel
Providing a guaranteed packet delivery ratio
Challenges
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BeamCast
Link Quality Estimator
Multicast SchedulerRetransmission Manager
Proposed Protocol - BeamCast
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How to estimate the “bottleneck” rate for each client?
Bottleneck rate = Max. rate to support a given delivery ratio
AP takes feedback from the clients periodically
LQE creates a database using the feedback
Bottleneck rates are updated by using this database
Link Quality Estimator (LQE)
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Theoretical relationship between delivery ratio (DR) and SNR
Link Quality Estimator (LQE)
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How to determine optimal transmission schedule?
A schedule = 1 omni + many directional transmissions
Optimal schedule = Schedule with minimum transmission time
MS extracts distinct client data rates from feedback
We assume,Beamforming rate = F x Omnidirectional rate ; F > 1
Multicast Scheduler (MS)
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Multicast Scheduler (MS)
How to determine optimal transmission rate for each beam?
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Problem becomes harder with overlapping beams
Multicast Scheduler (MS)
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2
3
4
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9 Mbps
7 Mbps
3 Mbps6 Mbps
11 Mbps
Beam1
Beam2
Beam3
Beam4
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Problem becomes harder with overlapping beams
Multicast Scheduler (MS)
1
2
3
4
5
9 Mbps
7 Mbps
3 Mbps6 Mbps
11 Mbps
Beam1
Beam2Beam4
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Problem becomes harder with overlapping beams
Multicast Scheduler (MS)
1
2
3
4
5
9 Mbps
7 Mbps
3 Mbps6 Mbps
11 Mbps
Beam1
Beam3
Beam4
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Problem becomes harder with overlapping beams
Multicast Scheduler (MS)
1
2
3
4
5
9 Mbps
7 Mbps
3 Mbps6 Mbps
11 Mbps
Beam1 @ 7 Mbps
Beam3 @ 3 Mbps
Beam4 @ 11 Mbps
Dynamic Programming used to solve the problem
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To cope with packet loss
Receives lost packet information from the clients periodically
Retransmits a subset of lost packets
Choose packets using a simple heuristic
Retransmission Manager
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Qualnet simulation
Comparison with Feedback enabled 802.11
Main Parameters :
1.Dynamic channels : Rayleigh, Rician fading; External interference2.Antenna beamwidth: 45o, 60o, 90o 3.Factor of rate improvement with beamforming: 3, 4
Metrics : Throughput, Delivery Ratio, Fairness
Application specified Minimum Delivery Ratio: 90%
Evaluation
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Multicast Throughput
BeamCast performs better with increasing Fading !
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Multicast Throughput
Throughput decreases with increase in client density
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Delivery Ratio
Increased delivery ratio for all clients, hence,
No Client Left Behind
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Switching delay has been assumed to be negligible
Rate reduction for both fading and interferenceRequires link layer loss discrimination
Focuses on “one-AP-many-clients” scenarioMulti-AP environment will require coordination
Ideas can be extended to EWLAN architecturesController assisted scheduling – better interference mitigation
Limitations
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Opportunistic beamforming for wireless multicasting
Multiple high rate directional vs. a single omni transmission
Rate estimation, scheduling and retransmission to achieve high throughput at a specified delivery ratio
A potential tool for next generation wireless multicast
Conclusions
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Thanks !
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Questions or Thoughts ??
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Jaikeo et. al talk about multicasting in ad-hoc networks-Assume multi-beam antenna model-Provide an analysis for collision probability-Do not consider asymmetry in transmission range
Ge et. al characterize optimal transmission rates-Discuss throughput and stability tradeoff
Papathanasiou et. al discuss multicast in IEEE 802.11n based network
-Minimize total Tx power but still provides a guaranteed SNR-Assume perfect channel state information is available
Smart Antennas in Multicast
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We assume IEEE 802.11 based WLANs
Beamforming antennas are mounted on access points (AP)
Clients are equipped with simple omnidirectional antennas
Clients are scattered around AP and remain stationary
Surrounding is characterized by wireless multipath and shadowing effects
System Settings
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Antenna Model
System Settings
A
Improvement in data rate is possible
C = W log2 (1 + SINR)
Higher with beamforming antennas
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Jain’s Fairness Index
Fairness
Both schemes are comparable
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