1

Link Layer Multicasting with Smart Antennas:

No Client Left Behind

Souvik Sen, Jie Xiong, Rahul Ghosh, Romit Roy Choudhury

Duke University

2

Widely used service

Interactive classrooms, Smart home, Airports …

MobiTV, Vcast, MediaFlo …

Single transmission to reach all clients

Wireless Multicast Use-Cases

3

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

4

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

5

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

6

Problem Validationthrough Measurements

7

Measurements in Duke Campus

AP

Clients

8

Measurements in Duke Campus

AP

ClientsTransmission @ 1 Mbps

AP

Clients

9

Measurements in Duke Campus

Transmission @ 2 Mbps

AP

Clients

10

Measurements in Duke Campus

Transmission @ 5.5 Mbps

AP

Clients

11

AP

Clients

Measurements in Duke Campus

Transmission @ 11 Mbps

12

Measurements in Duke Campus

Client index

Deli

very

R

ati

o

Topologies are characterized by very few weak clients

13

Reality

Weak clients tend to be clustered over small regions

shadow

regions

14

Intuition

12

3

4

5

6

15

Intuition

12

3

4

5

6

1 Mbps Omni

16

Intuition

12

3

4

5

6

11 Mbps Omni

17

Intuition

12

3

4

5

64 Mbps Directional

11 Mbps Omni

18

Intuition

12

3

4

56

1 Mbps Omni

12

3

4

56

4 Mbps Directional

11 Mbps Omni

19

Intuition to Reality

Few directional transmissions to cover few clients

20

Partitioning the client set with optimal omni and directional rates

Estimation of wireless channel

Providing a guaranteed packet delivery ratio

Challenges

21

BeamCast

Link Quality Estimator

Multicast SchedulerRetransmission Manager

Proposed Protocol - BeamCast

22

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)

23

Theoretical relationship between delivery ratio (DR) and SNR

Link Quality Estimator (LQE)

24

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)

25

Multicast Scheduler (MS)

How to determine optimal transmission rate for each beam?

26

Problem becomes harder with overlapping beams

Multicast Scheduler (MS)

1

2

3

4

5

9 Mbps

7 Mbps

3 Mbps6 Mbps

11 Mbps

Beam1

Beam2

Beam3

Beam4

27

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

28

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

29

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

30

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

31

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

32

Multicast Throughput

BeamCast performs better with increasing Fading !

33

Multicast Throughput

Throughput decreases with increase in client density

34

Delivery Ratio

Increased delivery ratio for all clients, hence,

No Client Left Behind

35

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

36

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

37

Thanks !

38

Questions or Thoughts ??

39

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

40

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

41

Antenna Model

System Settings

A

Improvement in data rate is possible

C = W log2 (1 + SINR)

Higher with beamforming antennas

42

Jain’s Fairness Index

Fairness

Both schemes are comparable

43

Body

Title

44

Body

Title

45

Body

Title