Song Wei Enabling Distributed Throughput Maximization in Wireless Mesh Networks A Partitioning Approach

Problem ● Channel Scheduling  Centralized  Distributed

Model ● Graph theory  Conflict graph  Graph coloring ● Throughput Constrains ● Arrival Process ● Stable Region

A Simple Example

● Conflict Graph

A Simple Example ● Stability Region For single radio, b=1. For two radios, b=2. Etc :Arrival process on link (i, j) lambda: arrival rate Stability Region:

A Simple Example ● Scheduling  Centralized: Yes  Distributed: ?

A Simple Example ● Partition: 100% Distributed Scheduling 1,2 2,3 3,4 4,5 5, ,2 2,3 3,4 4,5 5,6 6.1

A Simple Example ● Capability Increase

A Simple Example ● Insight  Topology study  Divide and conquer  Better the dividing

Topology for 100% DS ● Local Pooling

Topology for 100% DS ● Local Pooling Building Blocks  1-5  6  7

Topology for 100% DS ● Construction of Local Pooling network  Clique  Clique Connection  Tree of Cliques  Segments connected Cliques  Tree of Blocks

Topology for 100% DS ● Tree

Partition ● Problem  Multi Radio – Multi Channel Model  Formulation

Partition ● Algorithm  BFS : O(k(m+n))  MCI :

Improve Capacity

● How to define Capacity here?  Average  Max ● What’s the bottle neck?  Degrees of vertexes in first partitions are too high ● How to solve it?  Balance degree of vertex

Improve Capacity ● R-Greedy

Improve Capacity ● R-MaxD  Alter path  Consider Max Degree Vertex

Improve Capacity ● R-AvgD  Alter path  Consider All Vertex with no TF degradation

Performance Evaluation ● Partitioning

Performance Evaluation ● Capability Expansion

Questions ● Thanks