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Exact and approximate link scheduling algorithms under the physical interference model
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Workshop on Discrete Algothrithms and Methods for MOBILE Computing and Communications archive
Proceedings of the fifth international workshop on Foundations of mobile computing table of contents
Toronto, Canada
SESSION: Wireless networks I (scheduling) table of contents
Pages 45-54  
Year of Publication: 2008
ISBN:978-1-60558-244-3
Authors
Qiang-Sheng Hua  The University of Hong Kong, Hong Kong, Hong Kong
Francis C.M. Lau  The University of Hong Kong, Hong Kong, Hong Kong
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
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ABSTRACT

Given n arbitrarily distributed single-hop wireless links, using the physical interference model, the objective is to minimize the scheduling length. This is an open problem (Problem 1) proposed by Locher et al. [21]. In this paper, we solve this open problem at the cost of moderately exponential time. Specifically, this paper gives two classes of exact and approximate link scheduling algorithms, one based on the somewhat straightforward link independent set covering, and the other on counting the number of set covers. Let p(n) denote the time of checking whether the spectral radius of an irreducible non-negative matrix is smaller than 1 or not, then the time complexity for the set covering based exact algorithm is O(2C(n,n/2)), whereas the proposed counting based exact scheduling algorithm called ESA_MLSAT needs only time O(3n*n*(logn)2*p(n)) with polynomial space. If exponential space is allowed, the time complexity can be further reduced to O(2n*n*(logn)2*p(n)). The time complexity for the set covering based approximate algorithm is O(C(n,n/2)*logn*p(n)) with approximation ratio O(logn). The time complexity of the first counting based approximation algorithm is O(n2*polylog(n)) with approximation ratio O(n/logn), the time complexity of the second counting based approximation algorithm is O(n(1+log3*(logn)(k-1))*polylog(n)) with approximation ratio O(n/(logn)k), and the time complexity of the third counting based approximate algorithm is O((C(n,n/2)+3(e(-epsilon)*n)*n*logn)*logn*p(n)) with approximation ratio ceiling function of (1+epsilon). All these approximation algorithms use polynomial space.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Qiang-Sheng Hua: colleagues
Francis C.M. Lau: colleagues