Link Layer Multicasting with Smart Antennas: No Client Left Behind

1. Link Layer Multicasting with Smart Antennas: No Client Left Behind Souvik Sen, Jie Xiong, Rahul Ghosh, Romit Roy Choudhury Duke University

2. Wireless Multicast Use-Cases • Widely used service • Interactive classrooms, Smart home, Airports … • MobiTV, Vcast, MediaFlo … • Single transmission to reach all clients

3. Motivation • Today: • Multicast rate dictated by rate of weakest client (1 Mbps) • Inefficient channel utilization • Goal: • Improve multicast throughput • Uphold same reliability 1 Mbps 5.5 Mbps 11 Mbps

4. Problem is Non-Trivial • Scattered clients, different channel conditions • Time-varying wireless channel • Absence of per-packet feedback 1 Mbps 5.5 Mbps 11 Mbps

5. Solution – also Non-Trivial 11 Mbps 1 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 AP Transmission @ 1 Mbps Clients Clients

9. Measurements in Duke Campus AP Transmission @ 2 Mbps Clients

10. Measurements in Duke Campus AP Transmission @ 5.5 Mbps Clients

11. Measurements in Duke Campus AP Transmission @ 11 Mbps Clients

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

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

14. 4 3 6 5 1 2 Intuition

15. 4 3 6 5 1 2 Intuition 1 Mbps Omni

16. 4 3 6 5 1 2 Intuition 11 Mbps Omni

17. 4 3 6 5 1 2 Intuition 4 Mbps Directional 11 Mbps Omni

18. 4 4 3 3 6 6 5 5 1 1 2 2 Intuition 4 Mbps Directional 11 Mbps Omni 1 Mbps Omni

19. Intuition to Reality Few directional transmissions to cover few clients

20. Challenges • Partitioning the client set with optimal omni and directional rates • Estimation of wireless channel • Providing a guaranteed packet delivery ratio

21. Proposed Protocol - BeamCast Link Quality Estimator BeamCast Retransmission Manager Multicast Scheduler

22. Link Quality Estimator (LQE) • 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

23. Link Quality Estimator (LQE) • Theoretical relationship between delivery ratio (DR) and SNR

24. Multicast Scheduler (MS) • 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

25. Multicast Scheduler (MS) How to determine optimal transmission rate for each beam?

26. Multicast Scheduler (MS) • Problem becomes harder with overlapping beams Beam1 9 Mbps 1 2 7 Mbps 5 11 Mbps 4 6 Mbps 3 Mbps Beam2 3 Beam4 Beam3

27. Multicast Scheduler (MS) • Problem becomes harder with overlapping beams Beam1 9 Mbps 1 2 7 Mbps 5 11 Mbps 4 6 Mbps 3 Mbps Beam2 3 Beam4

28. Multicast Scheduler (MS) • Problem becomes harder with overlapping beams Beam1 9 Mbps 1 2 7 Mbps 5 11 Mbps 4 6 Mbps 3 Mbps 3 Beam4 Beam3

29. Multicast Scheduler (MS) • Problem becomes harder with overlapping beams Beam1 @ 7 Mbps 9 Mbps 1 Beam4 @ 11 Mbps 2 7 Mbps 5 11 Mbps 4 6 Mbps 3 Mbps 3 Beam3 @ 3 Mbps Dynamic Programming used to solve the problem

30. Retransmission Manager • 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

31. Evaluation • Qualnet simulation • Comparison with Feedback enabled 802.11 • Main Parameters : • Dynamic channels : Rayleigh, Rician fading; External interference • Antenna beamwidth: 45o, 60o, 90o • Factor of rate improvement with beamforming: 3, 4 • Metrics : Throughput, Delivery Ratio, Fairness • Application specified Minimum Delivery Ratio: 90%

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. Limitations • 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

36. Conclusions • 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

37. Thanks !

38. Questions or Thoughts ??

39. Smart Antennas in Multicast • 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

40. System Settings • 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

41. System Settings • Antenna Model A • Improvement in data rate is possible • C = W log2 (1 + SINR) Higher with beamforming antennas

42. Fairness • Jain’s Fairness Index Both schemes are comparable

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