Batched Point Location in SINR Diagrams via Algebraic Tools

The SINR (Signal to Interference plus Noise Ratio) model for the quality of wireless connections has been the subject of extensive recent study. It attempts to predict whether a particular transmitter is heard at a specific location, in a setting consisting of n simultaneous transmitters and backgro...

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Published inACM transactions on algorithms Vol. 14; no. 4; pp. 1 - 29
Main Authors Aronov, Boris, Katz, Matthew J.
Format Journal Article
LanguageEnglish
Published New York, NY, USA ACM 31.10.2018
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Online AccessGet full text
ISSN1549-6325
1549-6333
1549-6333
DOI10.1145/3209678

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Abstract The SINR (Signal to Interference plus Noise Ratio) model for the quality of wireless connections has been the subject of extensive recent study. It attempts to predict whether a particular transmitter is heard at a specific location, in a setting consisting of n simultaneous transmitters and background noise. The SINR model gives rise to a natural geometric object, the SINR diagram, which partitions the space into n regions where each of the transmitters can be heard and the remaining space where no transmitter can be heard. Efficient point location in the SINR diagram, i.e., being able to build a data structure that facilitates determining, for a query point, whether any transmitter is heard there, and if so, which one, has been recently investigated in several articles. These planar data structures are constructed in time at least quadratic in n and support logarithmic-time approximate queries. Moreover, the performance of some of the proposed structures depends strongly not only on the number n of transmitters and on the approximation parameter ε, but also on some geometric parameters that cannot be bounded a priori as a function of n or ε. In this article, we address the question of batched point location queries, i.e., answering many queries simultaneously. Specifically, in one dimension, we can answer n queries exactly in amortized polylogarithmic time per query, while in the plane we can do it approximately. In another result, we show how to answer n2 queries exactly in amortized polylogarithmic time per query, assuming the queries are located on a possibly non-uniform n × n grid. All these results can handle arbitrary power assignments to the transmitters. Moreover, the amortized query time in these results depends only on n and ε. We also show how to speed up the preprocessing in a previously proposed point-location structure in SINR diagram for uniform-power sites, by almost a full order of magnitude. For this, we obtain results on the sensitivity of the reception regions to slight changes in the reception threshold, which are of independent interest. Finally, these results demonstrate the (so far underutilized) power of combining algebraic tools with those of computational geometry and other fields.
AbstractList The SINR (Signal to Interference plus Noise Ratio) model for the quality of wireless connections has been the subject of extensive recent study. It attempts to predict whether a particular transmitter is heard at a specific location, in a setting consisting of n simultaneous transmitters and background noise. The SINR model gives rise to a natural geometric object, the SINR diagram, which partitions the space into n regions where each of the transmitters can be heard and the remaining space where no transmitter can be heard. Efficient point location in the SINR diagram, i.e., being able to build a data structure that facilitates determining, for a query point, whether any transmitter is heard there, and if so, which one, has been recently investigated in several articles. These planar data structures are constructed in time at least quadratic in n and support logarithmic-time approximate queries. Moreover, the performance of some of the proposed structures depends strongly not only on the number n of transmitters and on the approximation parameter ε, but also on some geometric parameters that cannot be bounded a priori as a function of n or ε. In this article, we address the question of batched point location queries, i.e., answering many queries simultaneously. Specifically, in one dimension, we can answer n queries exactly in amortized polylogarithmic time per query, while in the plane we can do it approximately. In another result, we show how to answer n2 queries exactly in amortized polylogarithmic time per query, assuming the queries are located on a possibly non-uniform n × n grid. All these results can handle arbitrary power assignments to the transmitters. Moreover, the amortized query time in these results depends only on n and ε. We also show how to speed up the preprocessing in a previously proposed point-location structure in SINR diagram for uniform-power sites, by almost a full order of magnitude. For this, we obtain results on the sensitivity of the reception regions to slight changes in the reception threshold, which are of independent interest. Finally, these results demonstrate the (so far underutilized) power of combining algebraic tools with those of computational geometry and other fields.
The SINR (Signal to Interference plus Noise Ratio) model for the quality of wireless connections has been the subject of extensive recent study. It attempts to predict whether a particular transmitter is heard at a specific location, in a setting consisting of n simultaneous transmitters and background noise. The SINR model gives rise to a natural geometric object, the SINR diagram , which partitions the space into n regions where each of the transmitters can be heard and the remaining space where no transmitter can be heard. Efficient point location in the SINR diagram, i.e., being able to build a data structure that facilitates determining, for a query point, whether any transmitter is heard there, and if so, which one, has been recently investigated in several articles. These planar data structures are constructed in time at least quadratic in n and support logarithmic-time approximate queries. Moreover, the performance of some of the proposed structures depends strongly not only on the number n of transmitters and on the approximation parameter ε , but also on some geometric parameters that cannot be bounded a priori as a function of n or ε . In this article, we address the question of batched point location queries, i.e., answering many queries simultaneously. Specifically, in one dimension, we can answer n queries exactly in amortized polylogarithmic time per query, while in the plane we can do it approximately. In another result, we show how to answer n 2 queries exactly in amortized polylogarithmic time per query, assuming the queries are located on a possibly non-uniform n × n grid. All these results can handle arbitrary power assignments to the transmitters. Moreover, the amortized query time in these results depends only on n and ε . We also show how to speed up the preprocessing in a previously proposed point-location structure in SINR diagram for uniform-power sites, by almost a full order of magnitude. For this, we obtain results on the sensitivity of the reception regions to slight changes in the reception threshold, which are of independent interest. Finally, these results demonstrate the (so far underutilized) power of combining algebraic tools with those of computational geometry and other fields.
ArticleNumber 41
Author Katz, Matthew J.
Aronov, Boris
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Cites_doi 10.1016/0020-0190(86)90055-4
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10.1145/2339123.2339125
10.1007/978-3-540-30140-0_49
10.1145/2390176.2390183
10.1137/140959067
10.1145/1814370.1814391
10.5555/304952
10.5555/235229
10.1007/978-3-642-03417-6_17
10.5555/2394893.2394955
10.5555/184671
10.1007/978-3-642-02927-1_44
10.1007/978-3-540-45078-8_13
10.1145/1288107.1288122
10.5555/2805882.2806035
10.1006/jsco.1994.1042
10.5555/3034197.3034361
10.1145/1993636.1993688
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Issue 4
Keywords batched point location
Wireless networks
SINR diagram
fast polynomial multiplication
fast polynomial multipoint evaluation
range searching
SINR model
algebraic methods
Language English
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References M. M. Halldórsson and P. Mitra. 2011. Wireless capacity with oblivious power in general metrics. In Proceedings of the 22nd ACM-SIAM Symposium on Discrete Algorithms (SODA’11). 1538--1548.
Franz Aurenhammer and Herbert Edelsbrunner. 1984. An optimal algorithm for constructing the weighted Voronoi diagram in the plane. Patt. Recog. 17, 2 (1984), 251--257.
Chen Avin, Yuval Emek, Erez Kantor, Zvi Lotker, David Peleg, and Liam Roditty. 2012. SINR diagrams: Convexity and its applications in wireless networks. J. ACM 59, 4, Article 18 (2012), 34 pages. 10.1145/2339123.2339125
Dario Bini and Victor Y. Pan. 1994. Polynomial and Matrix Computations, Vol. 1: Fundamental Algorithms. Birkhäuser.
M. de Berg, O. Cheong, M. van Kreveld, and M. H. Overmars. 2008. Computational Geometry: Algorithms and Applications (3rd ed.). Springer-Verlag, Berlin. http://www.cs.ruu.nl/geobook.
M. Andrews and M. Dinitz. 2009. Maximizing capacity in arbitrary wireless networks in the SINR model: Complexity and game theory. In Proceedings of the 28th IEEE International Conference on Computer Communications (INFOCOM’09). 1332--1340.
Chen Avin, Asaf Cohen, Yoram Haddad, Erez Kantor, Zvi Lotker, Merav Parter, and David Peleg. 2017. SINR diagram with interference cancellation. Ad Hoc Netw. 54 (2017), 1--16.
M. Sharir and P. K. Agarwal. 1995. Davenport-Schinzel Sequences and Their Geometric Applications. Cambridge University Press, New York. http://us.cambridge.org/titles/catalogue.asp?isbn=0521470250.
H. Edelsbrunner and R. Seidel. 1986. Voronoi diagrams and arrangements. Discrete Comput. Geom. 1 (1986), 25--44.
T. Moscibroda and R. Wattenhofer. 2006. The complexity of connectivity in wireless networks. In Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM’06). 25--37.
M. M. Halldórsson. 2012. Wireless scheduling with power control. ACM Trans. Algorithms 9, 1 (2012), 7:1--7:20. 10.1145/2390176.2390183
T. Kesselheim. 2011. A constant-factor approximation for wireless capacity maximization with power control in the SINR model. In Proceedings of the 22nd ACM-SIAM Symposium on Discrete Algorithms (SODA’11). 1549--1559.
Guillaume Moroz and Boris Aronov. 2016. Computing the distance between piecewise-linear bivariate functions. ACM Trans. Algorithms 12, 1 (2016), 3:1--3:13. 10.1145/2847257
Rom Aschner, Gui Citovsky, and Matthew J. Katz. 2014. Exploiting geometry in the SINRk model. In Proceedings of Algorithms for Sensor Systems—10th International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics (ALGOSENSORS’14). 125--135.
P. Gupta and P. R. Kumar. 2000. The capacity of wireless networks. IEEE Trans. Inform. Theor. 46, 2 (2000), 388--404. 10.1109/18.825799
M. M. Halldórsson and R. Wattenhofer. 2009. Wireless communication is in APX. In Proceedings Automata, Languages and Programming—36th International Colloquium (ICALP’09), Part I. 525--536. 10.1007/978-3-642-02927-1_44
Erez Kantor, Zvi Lotker, Merav Parter, and David Peleg. 2011. The topology of wireless communication. In Proceedings of the 43rd ACM Symposium on Theory of Computing (STOC’11). 383--392. 10.1145/1993636.1993688
Franz Aurenhammer, Rolf Klein, and D.-T. Lee. 2013. Voronoi Diagrams and Delaunay Triangulations. World Scientific. http://www.worldscientific.com/worldscibooks/10.1142/8685.
P.-J. Wan, X. Jia, and F. F. Yao. 2009. Maximum independent set of links under physical interference model. In Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (WASA’09). 169--178. 10.1007/978-3-642-03417-6_17
Victor Y. Pan. 1994. Simple multivariate polynomial multiplication. J. Symb. Comput. 18, 3 (1994), 183--186. 10.1006/jsco.1994.1042
Deepak Ajwani, Saurabh Ray, Raimund Seidel, and Hans Raj Tiwary. 2007. On computing the centroid of the vertices of an arrangement and related problems. In Proceedings of the 10th International Workshop on Algorithms and Data Structures (WADS’07). 519--528.
Martin Ziegler. 2003. Fast relative approximation of potential fields. In Proceedings of the 8th International Workshop on Algorithms and Data Structures (WADS’03). 140--149.
O. Goussevskaia, Y. A. Oswald, and R. Wattenhofer. 2007. Complexity in geometric SINR. In Proceedings of the 8th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’07). 100--109. 10.1145/1288107.1288122
Joachim von zur Gathen. 1999. Modern Computer Algebra. Cambridge University Press, Cambridge.
Sariel Har-Peled and Nirman Kumar. 2015. Approximating minimization diagrams and generalized proximity search. SIAM J. Comput. 44, 4 (2015), 944--974.
Michael Nüsken and Martin Ziegler. 2004. Fast multipoint evaluation of bivariate polynomials. In Proceedins 12th European Symposium on Algorithms (ESA’04). 544--555.
O. Goussevskaia, M. M. Halldórsson, R. Wattenhofer, and E. Welzl. 2009. Capacity of arbitrary wireless networks. In Proceedings of the 28th IEEE International Conference on Computer Communications (INFOCOM’09). 1872--1880.
Zvi Lotker and David Peleg. 2010. Structure and algorithms in the SINR wireless model. SIGACT News 41, 2 (2010), 74--84. 10.1145/1814370.1814391
Boris Aronov and Matthew J. Katz. 2015. Batched point location in SINR diagrams via algebraic tools. In Proceedings of Automata, Languages, and Programming—42nd International Colloquium (ICALP’15), Part I. 65--77. See also arXiv:1412.0962 {cs.CG}, 2014.
Franz Aurenhammer. 1986. The one-dimensional weighted Voronoi diagram. Inform. Process. Lett. 22, 3 (1986), 119--123. 10.1016/0020-0190(86)90055-4
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References_xml – reference: M. M. Halldórsson. 2012. Wireless scheduling with power control. ACM Trans. Algorithms 9, 1 (2012), 7:1--7:20. 10.1145/2390176.2390183
– reference: Franz Aurenhammer, Rolf Klein, and D.-T. Lee. 2013. Voronoi Diagrams and Delaunay Triangulations. World Scientific. http://www.worldscientific.com/worldscibooks/10.1142/8685.
– reference: Rom Aschner, Gui Citovsky, and Matthew J. Katz. 2014. Exploiting geometry in the SINRk model. In Proceedings of Algorithms for Sensor Systems—10th International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics (ALGOSENSORS’14). 125--135.
– reference: M. M. Halldórsson and R. Wattenhofer. 2009. Wireless communication is in APX. In Proceedings Automata, Languages and Programming—36th International Colloquium (ICALP’09), Part I. 525--536. 10.1007/978-3-642-02927-1_44
– reference: Victor Y. Pan. 1994. Simple multivariate polynomial multiplication. J. Symb. Comput. 18, 3 (1994), 183--186. 10.1006/jsco.1994.1042
– reference: Michael Nüsken and Martin Ziegler. 2004. Fast multipoint evaluation of bivariate polynomials. In Proceedins 12th European Symposium on Algorithms (ESA’04). 544--555.
– reference: O. Goussevskaia, Y. A. Oswald, and R. Wattenhofer. 2007. Complexity in geometric SINR. In Proceedings of the 8th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’07). 100--109. 10.1145/1288107.1288122
– reference: Sariel Har-Peled and Nirman Kumar. 2015. Approximating minimization diagrams and generalized proximity search. SIAM J. Comput. 44, 4 (2015), 944--974.
– reference: P. Gupta and P. R. Kumar. 2000. The capacity of wireless networks. IEEE Trans. Inform. Theor. 46, 2 (2000), 388--404. 10.1109/18.825799
– reference: Erez Kantor, Zvi Lotker, Merav Parter, and David Peleg. 2011. The topology of wireless communication. In Proceedings of the 43rd ACM Symposium on Theory of Computing (STOC’11). 383--392. 10.1145/1993636.1993688
– reference: Joachim von zur Gathen. 1999. Modern Computer Algebra. Cambridge University Press, Cambridge.
– reference: Deepak Ajwani, Saurabh Ray, Raimund Seidel, and Hans Raj Tiwary. 2007. On computing the centroid of the vertices of an arrangement and related problems. In Proceedings of the 10th International Workshop on Algorithms and Data Structures (WADS’07). 519--528.
– reference: Dario Bini and Victor Y. Pan. 1994. Polynomial and Matrix Computations, Vol. 1: Fundamental Algorithms. Birkhäuser.
– reference: M. Andrews and M. Dinitz. 2009. Maximizing capacity in arbitrary wireless networks in the SINR model: Complexity and game theory. In Proceedings of the 28th IEEE International Conference on Computer Communications (INFOCOM’09). 1332--1340.
– reference: P.-J. Wan, X. Jia, and F. F. Yao. 2009. Maximum independent set of links under physical interference model. In Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (WASA’09). 169--178. 10.1007/978-3-642-03417-6_17
– reference: Chen Avin, Yuval Emek, Erez Kantor, Zvi Lotker, David Peleg, and Liam Roditty. 2012. SINR diagrams: Convexity and its applications in wireless networks. J. ACM 59, 4, Article 18 (2012), 34 pages. 10.1145/2339123.2339125
– reference: M. de Berg, O. Cheong, M. van Kreveld, and M. H. Overmars. 2008. Computational Geometry: Algorithms and Applications (3rd ed.). Springer-Verlag, Berlin. http://www.cs.ruu.nl/geobook.
– reference: Boris Aronov and Matthew J. Katz. 2015. Batched point location in SINR diagrams via algebraic tools. In Proceedings of Automata, Languages, and Programming—42nd International Colloquium (ICALP’15), Part I. 65--77. See also arXiv:1412.0962 {cs.CG}, 2014.
– reference: Franz Aurenhammer. 1986. The one-dimensional weighted Voronoi diagram. Inform. Process. Lett. 22, 3 (1986), 119--123. 10.1016/0020-0190(86)90055-4
– reference: H. Edelsbrunner and R. Seidel. 1986. Voronoi diagrams and arrangements. Discrete Comput. Geom. 1 (1986), 25--44.
– reference: Chen Avin, Asaf Cohen, Yoram Haddad, Erez Kantor, Zvi Lotker, Merav Parter, and David Peleg. 2017. SINR diagram with interference cancellation. Ad Hoc Netw. 54 (2017), 1--16.
– reference: Guillaume Moroz and Boris Aronov. 2016. Computing the distance between piecewise-linear bivariate functions. ACM Trans. Algorithms 12, 1 (2016), 3:1--3:13. 10.1145/2847257
– reference: Zvi Lotker and David Peleg. 2010. Structure and algorithms in the SINR wireless model. SIGACT News 41, 2 (2010), 74--84. 10.1145/1814370.1814391
– reference: Franz Aurenhammer and Herbert Edelsbrunner. 1984. An optimal algorithm for constructing the weighted Voronoi diagram in the plane. Patt. Recog. 17, 2 (1984), 251--257.
– reference: T. Kesselheim. 2011. A constant-factor approximation for wireless capacity maximization with power control in the SINR model. In Proceedings of the 22nd ACM-SIAM Symposium on Discrete Algorithms (SODA’11). 1549--1559.
– reference: M. M. Halldórsson and P. Mitra. 2011. Wireless capacity with oblivious power in general metrics. In Proceedings of the 22nd ACM-SIAM Symposium on Discrete Algorithms (SODA’11). 1538--1548.
– reference: O. Goussevskaia, M. M. Halldórsson, R. Wattenhofer, and E. Welzl. 2009. Capacity of arbitrary wireless networks. In Proceedings of the 28th IEEE International Conference on Computer Communications (INFOCOM’09). 1872--1880.
– reference: M. Sharir and P. K. Agarwal. 1995. Davenport-Schinzel Sequences and Their Geometric Applications. Cambridge University Press, New York. http://us.cambridge.org/titles/catalogue.asp?isbn=0521470250.
– reference: Martin Ziegler. 2003. Fast relative approximation of potential fields. In Proceedings of the 8th International Workshop on Algorithms and Data Structures (WADS’03). 140--149.
– reference: T. Moscibroda and R. Wattenhofer. 2006. The complexity of connectivity in wireless networks. In Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM’06). 25--37.
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  doi: 10.1137/140959067
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  doi: 10.1145/1814370.1814391
– volume-title: Proceedings of the 28th IEEE International Conference on Computer Communications (INFOCOM’09). 1872
  year: 1880
  ident: e_1_2_1_13_1
– ident: e_1_2_1_28_1
  doi: 10.5555/304952
– ident: e_1_2_1_27_1
  doi: 10.5555/235229
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  doi: 10.1007/978-3-642-03417-6_17
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  doi: 10.5555/2394893.2394955
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  doi: 10.1007/978-3-642-02927-1_44
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  doi: 10.1007/978-3-540-45078-8_13
– volume-title: Proceedings of Automata, Languages, and Programming—42nd International Colloquium (ICALP’15)
  year: 2014
  ident: e_1_2_1_3_1
– ident: e_1_2_1_14_1
  doi: 10.1145/1288107.1288122
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  doi: 10.5555/2805882.2806035
– ident: e_1_2_1_26_1
  doi: 10.1006/jsco.1994.1042
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  ident: e_1_2_1_2_1
– ident: e_1_2_1_8_1
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  doi: 10.1145/1993636.1993688
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– ident: e_1_2_1_7_1
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Snippet The SINR (Signal to Interference plus Noise Ratio) model for the quality of wireless connections has been the subject of extensive recent study. It attempts to...
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SubjectTerms Computational geometry
Computing methodologies
Network performance evaluation
Networks
Randomness, geometry and discrete structures
Symbolic and algebraic algorithms
Symbolic and algebraic manipulation
Theory of computation
SubjectTermsDisplay Computing methodologies -- Symbolic and algebraic manipulation -- Symbolic and algebraic algorithms
Networks -- Network performance evaluation
Theory of computation -- Randomness, geometry and discrete structures -- Computational geometry
Title Batched Point Location in SINR Diagrams via Algebraic Tools
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https://dl.acm.org/doi/pdf/10.1145/3209678
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