1. A Simple Greedy Algorithm for Link Scheduling with the Physical Interference Model
Dejun Yang (Arizona State University)
Xi Fang (Arizona State University)
Nan Li (Carnegie Mellon University)
Guoliang Xue (Arizona State University)

2. Presentation Outline Introduction Related Work
System Model and Problem Definition
Our Greedy Algorithm
Simulation Results
Conclusion

3. Introduction

4. Related Work Link Scheduling Max-Cut Dr. Nelson Dr. Blough
Dr. Wattenhofer
Max-Cut
Names
Dr. Sahni

5. System Model l1 l2 l4 l3 x1 y2 x4 y1 x3 x2 Pt(x2) y4 y3 Static
For simplicity, only unidirectional scenario. y3

6. Interference Model Physical Interference Model or SINR Model l2 l1 l3β
SINR

7. Problem Definition
Link Scheduling with the Physical Interference Model S3 S1 l2 l4 S1 S2 l3 l1 l5

8. Current Results Our Approximation algorithm has a factor of O(g(L)).
Our greedy heuristic algorithm has the best performance so far.

9. Our Greedy Algorithm - k-Max-cut
For example, k = 3. 1 2 2 6 4 3 5 2 1 4 4 4 2 5 3 3
=23

10. Our Greedy Algorithm - k-Max-cut
For example, k = 3. 1 2 2 6 4 3 5 2 1 4 4 4 2 5 3 3
=25

11. Our Greedy Algorithm - k-Max-cut2 1 2 6 4 3 5 2 1 4 4 4 2 5 3 3 1 2 3 4 5
1,4 2 3 5 4
1,4
2,5 3 6 5 1
1,4
2,5
3,6

12. Our Greedy Algorithm - Inspiration
k-Max-Cut
N nodes to divide
Maximize the total weight of edges among groups
Link Scheduling
N links to schedule
To some extent, minimize the interference within same time slot

13. Our Greedy Algorithm – Illustration 1
5 links
N0 = 1, β = 1
LB = 0, UB = 5
k = 2
l l l l l5 l1 l2 l3 l4 l5
Put the graph and add transformation; put the labels on the matrix; add on the formula of the calculation of weight

14. Our Greedy Algorithm – Illustration 2
5 links
N0 = 1, β = 1
LB = 2, UB = 5
k = 3
l l l l l5 l1 l2 l3 l4 l5

15. Simulation Results - Settings
1000 x 1000 square
Length of link is between 1 and 30
N0 = 10-9 w
α = 2 to 4, β = 10
Power:
Homogeneous: 200 mw
Heterogeneous: 150 mw, 200 mw and 250 mw
Mention that the settings follow the one in Greedyphysical paper

16. Simulation Results - Unidirection
Add labels
Homogeneous Network
Heterogeneous Network

17. Simulation Results - Bidirection
Homogeneous Network
Heterogeneous Network

18. Conclusion
We proposed a polynomial O() time greedy algorithm based on k-Max-Cut.
It can be applied on both homogeneous and heterogeneous networks.
It can be applied on both unidirectional and bidirectional scenarios.
It improves the previously best result by 20%-30% in average.

19. Thank You!