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 Validation through Measurements

7 Measurements in Duke Campus AP Clients

8 Measurements in Duke Campus AP Clients 1 Mbps AP Clients

9 Measurements in Duke Campus 2 Mbps AP Clients

10 Measurements in Duke Campus 5.5 Mbps AP Clients

11 AP Clients Measurements in Duke Campus 11 Mbps

12 Measurements in Duke Campus Client index Delivery Ratio Topologies are characterized by very few weak clients

13 Reality Weak clients tend to be clustered over small regions shadow regions

14 Intuition

15 Intuition Mbps Omni

16 Intuition Mbps Omni

17 Intuition Mbps Directional 11 Mbps Omni

18 Intuition Mbps Omni 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 Scheduler Retransmission 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) Mbps 7 Mbps 3 Mbps 6 Mbps 11 Mbps Beam 1 Beam 2 Beam 3 Beam 4

27 Problem becomes harder with overlapping beams Multicast Scheduler (MS) Mbps 7 Mbps 3 Mbps 6 Mbps 11 Mbps Beam 1 Beam 2 Beam 4

28 Problem becomes harder with overlapping beams Multicast Scheduler (MS) Mbps 7 Mbps 3 Mbps 6 Mbps 11 Mbps Beam 1 Beam 3 Beam 4

29 Problem becomes harder with overlapping beams Multicast Scheduler (MS) Mbps 7 Mbps 3 Mbps 6 Mbps 11 Mbps Beam 7 Mbps Beam 3 Mbps Beam 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 Main Parameters : 1.Dynamic channels : Rayleigh, Rician fading; External interference 2.Antenna beamwidth: 45 o, 60 o, 90 o 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 interference Requires link layer loss discrimination Focuses on “one-AP-many-clients” scenario Multi-AP environment will require coordination Ideas can be extended to EWLAN architectures Controller 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 n 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 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 log 2 (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