Investigating Depth-Fanout Trade-Off in WiMAX Mesh Networks Salim Nahle Luigi Iannone Benoit Donnet Timur Friedman Laboratoire LIP6 – CNRS Université Pierre et Marie Curie – Paris 6 First Weird Workshop on WiMAX, Wireless and Mobiliy

1 Overview  Introduction  Depth-fanout trade-off  A traffic model for mesh trees  Simulation  Conclusions and future work

2 Introduction  Wireless mesh networks (WMNs) have many advantages.  based WMNs have been widely studied.  Problem: short range.  Consequence: dense and suboptimal deployments.  IEEE promises to transcend this limitation.  It operates in two modes: PMP and MESH

3 Introduction

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6 Dead zones Long links Route around obstacles Multiple shorter hops What is the best number of hops ?

7 Overview  Introduction  Depth-fanout trade-off  A traffic model for mesh trees  Simulation  Conclusions and future work

8 Trade-off: fanout vs depth 3000m 830m 600m Maximum depth = 2 Maximum depth = 7

9 Depth vs average hop distance

10 Depth vs control overhead

11 Bit rate as a function of Distance Graph from Betancur et al. NS2 workshop 2006 Distance (m) Bit rate (Mbps)

12 Example 4Km Data Rate = 2.2Mbps 2Km 7.2 Mbps Data Rate = 3.6Mbps D S D S 1.3Km 11 Mbps 1.3Km 11 Mbps D S Data Rate = 3.6Mbps 2.2 Mbps

13 Overview  Introduction  Depth-fanout trade-off  A traffic model for mesh trees  Simulation  Conclusion and future work

14 WiMAX Mesh mode (Background)  Mesh mode specifications were integrated into the IEEE  They define the control mechanisms and management messages to establish connections in Mesh Network architecture.

15 Traffic model for mesh tree (1/2) We assume a balanced or a quasi-balanced tree. Parameters:  C a : average number of children SSs per parent node  m: tree depth or number of levels in the WiMAX mesh tree.

16 Traffic model for mesh tree (2/2) Four types of traffic patterns at each SS:  Traffic in the uplink direction towards the Internet via the BS  Traffic in the downlink direction from the Internet via the BS  Intra-mesh traffic in the uplink direction  Intra-mesh traffic in the downlink direction Note that, within the mesh context, uplink and downlink are defined as the traffic in the direction of the mesh BS and traffic away from the mesh BS repectively.

17 Traffic via BS (1/2) l l ll l ll uuuu 3u l l ll l ll uuuu 7u BS 7d 3d d d d d d d d d Traffic per link Note : l =d+u 3l3d3u A 1 -A 2 ldu A 2 -A 3 7l7d7u A 0 -A 1 TotalDownlinkUplink u: outgoing own traffic d: Incoming own traffic l: Average total traffic

18 Average traffic on all the links A i –A i-1 : = (Number of nodes in level A i )*l + (Average traffic from A i+1 ) = + {(Number of nodes in level A i+1 )*l + (Average traffic from A i+2 )} = Average traffic per link: Traffic via BS (2/2)

19 u BS Ca=2 N= =13 Intra-mesh traffic per node = u u Traffic sent= Traffic received Intra-mesh Traffic (1/2)

20 U i+1 UiUi u uu u uu u u uu … u U i+2 U i = + With Intra-mesh Traffic (2/2)

21 Overview  Introduction  Depth-fanout trade-off  A traffic model for mesh trees  Simulation  Conclusions and future work

22 Simulation Objective:  Study the impact of the tree depth m on the aggregate throughput capacity. Simulation setup:  Number of nodes = 49  Distributed uniformly in a 7*7 grid topology  m varies between 1 and 7  Traffic Requests are sent as just described

23 Throughput via the BS

24 Intra-mesh throughput

25 Overview  Introduction  Depth-fanout trade-off  A traffic model for mesh trees  Simulation  Conclusions and future work

26 Conclusions  Increasing the depth may increase the throughput even without allowing for concurrent transmissions  Only long hops must be split.  Recent extension shows better results

27 Future work  Different traffic models  Allowing for concurrent transmissions (future work).  Investigate distributed scheduling capacity.  Optimizing the number of time slots used by each scheme is another perspective.

28 Questions Thank you