Dejun Yang (Arizona State University)

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Presentation transcript:

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)

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

Introduction

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

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

Interference Model Physical Interference Model or SINR Model l2 l1 l3 β SINR

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

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

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 2+4+5+2+3+4+3=23

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 2+4+5+4+3+4+3=25

Our Greedy Algorithm - k-Max-cut 2 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

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

Our Greedy Algorithm – Illustration 1 5 links N0 = 1, β = 1 LB = 0, UB = 5 k = 2 l1 l2 l3 l4 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

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

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

Simulation Results - Unidirection Add labels Homogeneous Network Heterogeneous Network

Simulation Results - Bidirection Homogeneous Network Heterogeneous Network

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.

Thank You!