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...
Saved in:
| Published in | ACM transactions on algorithms Vol. 14; no. 4; pp. 1 - 29 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY, USA
ACM
31.10.2018
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1549-6325 1549-6333 1549-6333 |
| DOI | 10.1145/3209678 |
Cover
| 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 |
| Author_xml | – sequence: 1 givenname: Boris orcidid: 0000-0003-3110-4702 surname: Aronov fullname: Aronov, Boris email: boris.aronov@nyu.edu organization: New York University, NY, USA – sequence: 2 givenname: Matthew J. surname: Katz fullname: Katz, Matthew J. email: matya@cs.bgu.ac.il organization: Ben-Gurion University, Beer-Sheva, Israel |
| BookMark | eNp9z89LwzAUwPEgE9ymePeUm16q-dEmKZ7m_DUoKjrP5TVNZ6RtRhOV_fdOO3cQ8fQevA8PviM0aF1rEDqk5JTSODnjjKRCqh00pEmcRoJzPtjuLNlDI-9fCeEp52qIzi8g6BdT4gdn24AzpyFY12Lb4qfZ3SO-tLDooPH43QKe1AtTdGA1njtX-320W0HtzcFmjtHz9dV8ehtl9zez6SSLgEkZIk6oZsDSKtWllKoURgpJE0KoSRlIJSsBpaJGqEoyUhRaVIoyw7RRQFhs-Bid9H_f2iWsPqCu82VnG-hWOSX5V3S-iV7T457qznnfmeofGf2S2obv9rAurP_wR70H3Wyf_hw_AQB9bNw |
| CitedBy_id | crossref_primary_10_1016_j_tcs_2022_07_017 crossref_primary_10_1007_s00454_021_00368_3 crossref_primary_10_1145_3501303 crossref_primary_10_1145_3477144 crossref_primary_10_1137_19M128733X |
| Cites_doi | 10.1016/0020-0190(86)90055-4 10.5555/1370949 10.1109/18.825799 10.5555/2133036.2133155 10.1145/2847257 10.1016/0031-3203(84)90064-5 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 10.5555/2133036.2133156 10.5555/2563475 |
| ContentType | Journal Article |
| Copyright | ACM |
| Copyright_xml | – notice: ACM |
| DBID | AAYXX CITATION ADTOC UNPAY |
| DOI | 10.1145/3209678 |
| DatabaseName | CrossRef Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1549-6333 |
| EndPage | 29 |
| ExternalDocumentID | 10.1145/3209678 10_1145_3209678 3209678 |
| GrantInformation_xml | – fundername: Israel Science Foundation grantid: 1045/10,1884/16 funderid: http://dx.doi.org/10.13039/501100001742 – fundername: United States-Israel Binational Science Foundation grantid: 2014/170 – fundername: National Science Foundation grantid: CCF-11-17336,CCF-12-18791,CCF-15-40656 funderid: http://dx.doi.org/10.13039/100000001 |
| GroupedDBID | -DZ .4S .DC 23M 4.4 5GY 5VS 8US AAKMM AALFJ AAYFX ABPPZ ACM ADBCU ADL ADMLS ADPZR AEBYY AENEX AENSD AFWIH AFWXC AIKLT ALMA_UNASSIGNED_HOLDINGS ARCSS ASPBG AVWKF BDXCO CCLIF CS3 D0L EBS EDO EJD FEDTE GUFHI HGAVV H~9 I07 J9A LHSKQ MK~ ML~ P1C P2P RNS ROL TUS W7O ZCA AAYXX AEFXT AEJOY AKRVB AMVHM CITATION ADTOC AFFNX UNPAY XOL |
| ID | FETCH-LOGICAL-a277t-301c2a29f9cd778d6e76715001e92a787f6ad81e68f720bbc6f812e2ce8a024e3 |
| IEDL.DBID | UNPAY |
| ISSN | 1549-6325 1549-6333 |
| IngestDate | Sun Oct 26 03:13:45 EDT 2025 Wed Oct 01 06:00:22 EDT 2025 Thu Apr 24 22:58:22 EDT 2025 Fri Feb 21 01:11:37 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| 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 |
| License | Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a277t-301c2a29f9cd778d6e76715001e92a787f6ad81e68f720bbc6f812e2ce8a024e3 |
| ORCID | 0000-0003-3110-4702 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://dl.acm.org/doi/pdf/10.1145/3209678 |
| PageCount | 29 |
| ParticipantIDs | unpaywall_primary_10_1145_3209678 crossref_primary_10_1145_3209678 crossref_citationtrail_10_1145_3209678 acm_primary_3209678 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2018-10-31 |
| PublicationDateYYYYMMDD | 2018-10-31 |
| PublicationDate_xml | – month: 10 year: 2018 text: 2018-10-31 day: 31 |
| PublicationDecade | 2010 |
| PublicationPlace | New York, NY, USA |
| PublicationPlace_xml | – name: New York, NY, USA |
| PublicationTitle | ACM transactions on algorithms |
| PublicationTitleAbbrev | ACM TALG |
| PublicationYear | 2018 |
| Publisher | ACM |
| Publisher_xml | – name: ACM |
| 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 e_1_2_1_20_1 Aronov Boris (e_1_2_1_3_1) 2014 e_1_2_1_23_1 e_1_2_1_21_1 e_1_2_1_22_1 e_1_2_1_27_1 e_1_2_1_28_1 e_1_2_1_25_1 e_1_2_1_26_1 e_1_2_1_29_1 Moscibroda T. (e_1_2_1_24_1) Andrews M. (e_1_2_1_2_1) Aschner Rom (e_1_2_1_4_1) e_1_2_1_7_1 e_1_2_1_8_1 e_1_2_1_30_1 e_1_2_1_5_1 e_1_2_1_6_1 e_1_2_1_12_1 Goussevskaia O. (e_1_2_1_13_1) 1880 e_1_2_1_1_1 e_1_2_1_10_1 e_1_2_1_11_1 e_1_2_1_16_1 e_1_2_1_17_1 e_1_2_1_14_1 e_1_2_1_15_1 e_1_2_1_9_1 e_1_2_1_18_1 e_1_2_1_19_1 |
| 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. – ident: e_1_2_1_5_1 doi: 10.1016/0020-0190(86)90055-4 – volume-title: Proceedings of Algorithms for Sensor Systems—10th International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics (ALGOSENSORS’14) ident: e_1_2_1_4_1 – ident: e_1_2_1_11_1 doi: 10.5555/1370949 – ident: e_1_2_1_15_1 doi: 10.1109/18.825799 – ident: e_1_2_1_17_1 doi: 10.5555/2133036.2133155 – volume-title: Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM’06) ident: e_1_2_1_24_1 – ident: e_1_2_1_23_1 doi: 10.1145/2847257 – ident: e_1_2_1_6_1 doi: 10.1016/0031-3203(84)90064-5 – ident: e_1_2_1_9_1 doi: 10.1145/2339123.2339125 – ident: e_1_2_1_25_1 doi: 10.1007/978-3-540-30140-0_49 – ident: e_1_2_1_16_1 doi: 10.1145/2390176.2390183 – ident: e_1_2_1_19_1 doi: 10.1137/140959067 – ident: e_1_2_1_22_1 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 – ident: e_1_2_1_29_1 doi: 10.1007/978-3-642-03417-6_17 – ident: e_1_2_1_1_1 doi: 10.5555/2394893.2394955 – ident: e_1_2_1_10_1 doi: 10.5555/184671 – ident: e_1_2_1_18_1 doi: 10.1007/978-3-642-02927-1_44 – ident: e_1_2_1_30_1 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 – ident: e_1_2_1_12_1 doi: 10.5555/2805882.2806035 – ident: e_1_2_1_26_1 doi: 10.1006/jsco.1994.1042 – volume-title: Proceedings of the 28th IEEE International Conference on Computer Communications (INFOCOM’09) ident: e_1_2_1_2_1 – ident: e_1_2_1_8_1 doi: 10.5555/3034197.3034361 – ident: e_1_2_1_20_1 doi: 10.1145/1993636.1993688 – ident: e_1_2_1_21_1 doi: 10.5555/2133036.2133156 – ident: e_1_2_1_7_1 doi: 10.5555/2563475 |
| SSID | ssj0039338 |
| Score | 2.2245326 |
| 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... |
| SourceID | unpaywall crossref acm |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 1 |
| 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 |
| URI | https://dl.acm.org/doi/10.1145/3209678 https://dl.acm.org/doi/pdf/10.1145/3209678 |
| UnpaywallVersion | publishedVersion |
| Volume | 14 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1549-6333 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0039338 issn: 1549-6333 databaseCode: ADMLS dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVEBS databaseName: Mathematics Source customDbUrl: eissn: 1549-6333 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0039338 issn: 1549-6333 databaseCode: AMVHM dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/mathematics-source providerName: EBSCOhost |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1bS8MwGA26PeiLd_FOBPGt2qVtkuLTvDFFh6gDfRq5fJVibYfrFP31Jms2bwi-9eFryO3jnDb5zkFoR4MfShkrDyKqvVCShicoCz1IJJcsUoorW-982aatTnh-F905mRxbC6Mz087T8Ajf5nRPJ07QNtoPiKHbjE-iOo0M766heqd91bwfCqKGsUeDocOqew6CqkL265sWgNTTNwCaGuQ98fYqsuwLqpzOVvZE_aEYob1M8rg3KOWeev8h1fi_Ds-hGUcucbPaDfNoAvIFNDsybsAujxfRwaGwi6XxVZHmJb4oqv92OM3xzVn7Gh-nwl7a6uOXVOBm9mAPl1OFb4si6y-hzunJ7VHLcy4KniCMlZ7JYEUEiZNYaca4psAoMzTQb0BMhMnXhArNG0B5wogvpaKJAX0gCrgwAA7BMqrlRQ4rCPuSQdSQQJgyny1maCAFDST4BCItebCKFsxcdHuVTkbXjX8V7Y4mvKuc8Lj1v8i6VVF09BmIx4GjNn6FbI9X7K-YtX_ErKNpw3x4BUIbqFY-D2DTsItSbqF68_jy4mbLba8PHODIOQ |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1bS8MwGA26PeiLd3HeiCC-Vbu0TVJ8mjdUdIg60KeRy1cp1na4TtFfb7Jm84bgWx--htw-zmmT7xyEtjX4oZSx8iCi2gslaXqCstCDRHLJIqW4svXOl2162gnP76I7J5Nja2F0Ztp5Gh7h25zu6cQJ2kZ7ATF0m_FJVKeR4d01VO-0r1r3Q0HUMPZoMHRYdc9BUFXIfn3TApB6-gZAU4O8J95eRZZ9QZWT2cqeqD8UI7SXSR53B6XcVe8_pBr_1-E5NOPIJW5Vu2EeTUC-gGZHxg3Y5fEi2j8QdrE0virSvMQXRfXfDqc5vjlrX-OjVNhLW338kgrcyh7s4XKq8G1RZP0l1Dk5vj089ZyLgicIY6VnMlgRQeIkVpoxrikwygwN9JsQE2HyNaFC8yZQnjDiS6loYkAfiAIuDIBDsIxqeZHDCsK-ZBA1JRCmzGeLGRpIQQMJPoFISx400IKZi26v0snouvE30M5owrvKCY9b_4usWxVFR5-BeBw4auNXyNZ4xf6KWf1HzBqaNsyHVyC0jmrl8wA2DLso5abbVh_i-sal |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Batched+Point+Location+in+SINR+Diagrams+via+Algebraic+Tools&rft.jtitle=ACM+transactions+on+algorithms&rft.au=Aronov%2C+Boris&rft.au=Katz%2C+Matthew+J.&rft.date=2018-10-31&rft.pub=ACM&rft.issn=1549-6325&rft.eissn=1549-6333&rft.volume=14&rft.issue=4&rft.spage=1&rft.epage=29&rft_id=info:doi/10.1145%2F3209678&rft.externalDocID=3209678 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1549-6325&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1549-6325&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1549-6325&client=summon |