On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs
The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wildman, Jeffrey [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: IEEE ACM transactions on networking - New York, NY : IEEE, 1993, 24(2016), 4, Seite 2086-2099 |
|---|---|
| Übergeordnetes Werk: |
volume:24 ; year:2016 ; number:4 ; pages:2086-2099 |
| Links: |
|---|
| DOI / URN: |
10.1109/TNET.2015.2451090 |
|---|
| Katalog-ID: |
OLC1983293970 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1983293970 | ||
| 003 | DE-627 | ||
| 005 | 20220219084609.0 | ||
| 007 | tu | ||
| 008 | 161013s2016 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1109/TNET.2015.2451090 |2 doi | |
| 028 | 5 | 2 | |a PQ20161012 |
| 035 | |a (DE-627)OLC1983293970 | ||
| 035 | |a (DE-599)GBVOLC1983293970 | ||
| 035 | |a (PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460 | ||
| 035 | |a (KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 620 |a 004 |q DNB |
| 084 | |a 54.00 |2 bkl | ||
| 084 | |a 05.00 |2 bkl | ||
| 100 | 1 | |a Wildman, Jeffrey |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs |
| 264 | 1 | |c 2016 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. | ||
| 650 | 4 | |a Limiting | |
| 650 | 4 | |a Distribution functions | |
| 650 | 4 | |a primary interference | |
| 650 | 4 | |a Stability analysis | |
| 650 | 4 | |a IEEE transactions | |
| 650 | 4 | |a Connectivity | |
| 650 | 4 | |a Erbium | |
| 650 | 4 | |a Upper bound | |
| 650 | 4 | |a local pooling | |
| 650 | 4 | |a giant component | |
| 650 | 4 | |a greedy maximal scheduling | |
| 650 | 4 | |a random graphs | |
| 650 | 4 | |a Interference | |
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Networking and Internet Architecture | |
| 650 | 4 | |a Information Theory | |
| 700 | 1 | |a Weber, Steven |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t IEEE ACM transactions on networking |d New York, NY : IEEE, 1993 |g 24(2016), 4, Seite 2086-2099 |w (DE-627)165670215 |w (DE-600)1150634-9 |w (DE-576)034200843 |x 1063-6692 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2016 |g number:4 |g pages:2086-2099 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1109/TNET.2015.2451090 |3 Volltext |
| 856 | 4 | 2 | |u http://ieeexplore.ieee.org/document/7173059 |
| 856 | 4 | 2 | |u http://arxiv.org/abs/1409.0932 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_4125 | ||
| 936 | b | k | |a 54.00 |q AVZ |
| 936 | b | k | |a 05.00 |q AVZ |
| 951 | |a AR | ||
| 952 | |d 24 |j 2016 |e 4 |h 2086-2099 | ||
| author_variant |
j w jw |
|---|---|
| matchkey_str |
article:10636692:2016----::nhrceiighlclolnfcoogedmxmlce |
| hierarchy_sort_str |
2016 |
| bklnumber |
54.00 05.00 |
| publishDate |
2016 |
| allfields |
10.1109/TNET.2015.2451090 doi PQ20161012 (DE-627)OLC1983293970 (DE-599)GBVOLC1983293970 (PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460 (KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim DE-627 ger DE-627 rakwb eng 620 004 DNB 54.00 bkl 05.00 bkl Wildman, Jeffrey verfasserin aut On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. Limiting Distribution functions primary interference Stability analysis IEEE transactions Connectivity Erbium Upper bound local pooling giant component greedy maximal scheduling random graphs Interference Computer Science Networking and Internet Architecture Information Theory Weber, Steven oth Enthalten in IEEE ACM transactions on networking New York, NY : IEEE, 1993 24(2016), 4, Seite 2086-2099 (DE-627)165670215 (DE-600)1150634-9 (DE-576)034200843 1063-6692 nnns volume:24 year:2016 number:4 pages:2086-2099 http://dx.doi.org/10.1109/TNET.2015.2451090 Volltext http://ieeexplore.ieee.org/document/7173059 http://arxiv.org/abs/1409.0932 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_151 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 54.00 AVZ 05.00 AVZ AR 24 2016 4 2086-2099 |
| spelling |
10.1109/TNET.2015.2451090 doi PQ20161012 (DE-627)OLC1983293970 (DE-599)GBVOLC1983293970 (PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460 (KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim DE-627 ger DE-627 rakwb eng 620 004 DNB 54.00 bkl 05.00 bkl Wildman, Jeffrey verfasserin aut On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. Limiting Distribution functions primary interference Stability analysis IEEE transactions Connectivity Erbium Upper bound local pooling giant component greedy maximal scheduling random graphs Interference Computer Science Networking and Internet Architecture Information Theory Weber, Steven oth Enthalten in IEEE ACM transactions on networking New York, NY : IEEE, 1993 24(2016), 4, Seite 2086-2099 (DE-627)165670215 (DE-600)1150634-9 (DE-576)034200843 1063-6692 nnns volume:24 year:2016 number:4 pages:2086-2099 http://dx.doi.org/10.1109/TNET.2015.2451090 Volltext http://ieeexplore.ieee.org/document/7173059 http://arxiv.org/abs/1409.0932 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_151 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 54.00 AVZ 05.00 AVZ AR 24 2016 4 2086-2099 |
| allfields_unstemmed |
10.1109/TNET.2015.2451090 doi PQ20161012 (DE-627)OLC1983293970 (DE-599)GBVOLC1983293970 (PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460 (KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim DE-627 ger DE-627 rakwb eng 620 004 DNB 54.00 bkl 05.00 bkl Wildman, Jeffrey verfasserin aut On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. Limiting Distribution functions primary interference Stability analysis IEEE transactions Connectivity Erbium Upper bound local pooling giant component greedy maximal scheduling random graphs Interference Computer Science Networking and Internet Architecture Information Theory Weber, Steven oth Enthalten in IEEE ACM transactions on networking New York, NY : IEEE, 1993 24(2016), 4, Seite 2086-2099 (DE-627)165670215 (DE-600)1150634-9 (DE-576)034200843 1063-6692 nnns volume:24 year:2016 number:4 pages:2086-2099 http://dx.doi.org/10.1109/TNET.2015.2451090 Volltext http://ieeexplore.ieee.org/document/7173059 http://arxiv.org/abs/1409.0932 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_151 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 54.00 AVZ 05.00 AVZ AR 24 2016 4 2086-2099 |
| allfieldsGer |
10.1109/TNET.2015.2451090 doi PQ20161012 (DE-627)OLC1983293970 (DE-599)GBVOLC1983293970 (PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460 (KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim DE-627 ger DE-627 rakwb eng 620 004 DNB 54.00 bkl 05.00 bkl Wildman, Jeffrey verfasserin aut On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. Limiting Distribution functions primary interference Stability analysis IEEE transactions Connectivity Erbium Upper bound local pooling giant component greedy maximal scheduling random graphs Interference Computer Science Networking and Internet Architecture Information Theory Weber, Steven oth Enthalten in IEEE ACM transactions on networking New York, NY : IEEE, 1993 24(2016), 4, Seite 2086-2099 (DE-627)165670215 (DE-600)1150634-9 (DE-576)034200843 1063-6692 nnns volume:24 year:2016 number:4 pages:2086-2099 http://dx.doi.org/10.1109/TNET.2015.2451090 Volltext http://ieeexplore.ieee.org/document/7173059 http://arxiv.org/abs/1409.0932 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_151 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 54.00 AVZ 05.00 AVZ AR 24 2016 4 2086-2099 |
| allfieldsSound |
10.1109/TNET.2015.2451090 doi PQ20161012 (DE-627)OLC1983293970 (DE-599)GBVOLC1983293970 (PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460 (KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim DE-627 ger DE-627 rakwb eng 620 004 DNB 54.00 bkl 05.00 bkl Wildman, Jeffrey verfasserin aut On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. Limiting Distribution functions primary interference Stability analysis IEEE transactions Connectivity Erbium Upper bound local pooling giant component greedy maximal scheduling random graphs Interference Computer Science Networking and Internet Architecture Information Theory Weber, Steven oth Enthalten in IEEE ACM transactions on networking New York, NY : IEEE, 1993 24(2016), 4, Seite 2086-2099 (DE-627)165670215 (DE-600)1150634-9 (DE-576)034200843 1063-6692 nnns volume:24 year:2016 number:4 pages:2086-2099 http://dx.doi.org/10.1109/TNET.2015.2451090 Volltext http://ieeexplore.ieee.org/document/7173059 http://arxiv.org/abs/1409.0932 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_151 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 54.00 AVZ 05.00 AVZ AR 24 2016 4 2086-2099 |
| language |
English |
| source |
Enthalten in IEEE ACM transactions on networking 24(2016), 4, Seite 2086-2099 volume:24 year:2016 number:4 pages:2086-2099 |
| sourceStr |
Enthalten in IEEE ACM transactions on networking 24(2016), 4, Seite 2086-2099 volume:24 year:2016 number:4 pages:2086-2099 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Limiting Distribution functions primary interference Stability analysis IEEE transactions Connectivity Erbium Upper bound local pooling giant component greedy maximal scheduling random graphs Interference Computer Science Networking and Internet Architecture Information Theory |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
IEEE ACM transactions on networking |
| authorswithroles_txt_mv |
Wildman, Jeffrey @@aut@@ Weber, Steven @@oth@@ |
| publishDateDaySort_date |
2016-01-01T00:00:00Z |
| hierarchy_top_id |
165670215 |
| dewey-sort |
3620 |
| id |
OLC1983293970 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1983293970</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220219084609.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161013s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TNET.2015.2451090</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161012</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1983293970</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1983293970</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">004</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">05.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wildman, Jeffrey</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Limiting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distribution functions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">primary interference</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stability analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IEEE transactions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Connectivity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Erbium</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Upper bound</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">local pooling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">giant component</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">greedy maximal scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">random graphs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Interference</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Networking and Internet Architecture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information Theory</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Weber, Steven</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE ACM transactions on networking</subfield><subfield code="d">New York, NY : IEEE, 1993</subfield><subfield code="g">24(2016), 4, Seite 2086-2099</subfield><subfield code="w">(DE-627)165670215</subfield><subfield code="w">(DE-600)1150634-9</subfield><subfield code="w">(DE-576)034200843</subfield><subfield code="x">1063-6692</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:2086-2099</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TNET.2015.2451090</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/document/7173059</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://arxiv.org/abs/1409.0932</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">05.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2016</subfield><subfield code="e">4</subfield><subfield code="h">2086-2099</subfield></datafield></record></collection>
|
| author |
Wildman, Jeffrey |
| spellingShingle |
Wildman, Jeffrey ddc 620 bkl 54.00 bkl 05.00 misc Limiting misc Distribution functions misc primary interference misc Stability analysis misc IEEE transactions misc Connectivity misc Erbium misc Upper bound misc local pooling misc giant component misc greedy maximal scheduling misc random graphs misc Interference misc Computer Science misc Networking and Internet Architecture misc Information Theory On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs |
| authorStr |
Wildman, Jeffrey |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)165670215 |
| format |
Article |
| dewey-ones |
620 - Engineering & allied operations 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1063-6692 |
| topic_title |
620 004 DNB 54.00 bkl 05.00 bkl On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs Limiting Distribution functions primary interference Stability analysis IEEE transactions Connectivity Erbium Upper bound local pooling giant component greedy maximal scheduling random graphs Interference Computer Science Networking and Internet Architecture Information Theory |
| topic |
ddc 620 bkl 54.00 bkl 05.00 misc Limiting misc Distribution functions misc primary interference misc Stability analysis misc IEEE transactions misc Connectivity misc Erbium misc Upper bound misc local pooling misc giant component misc greedy maximal scheduling misc random graphs misc Interference misc Computer Science misc Networking and Internet Architecture misc Information Theory |
| topic_unstemmed |
ddc 620 bkl 54.00 bkl 05.00 misc Limiting misc Distribution functions misc primary interference misc Stability analysis misc IEEE transactions misc Connectivity misc Erbium misc Upper bound misc local pooling misc giant component misc greedy maximal scheduling misc random graphs misc Interference misc Computer Science misc Networking and Internet Architecture misc Information Theory |
| topic_browse |
ddc 620 bkl 54.00 bkl 05.00 misc Limiting misc Distribution functions misc primary interference misc Stability analysis misc IEEE transactions misc Connectivity misc Erbium misc Upper bound misc local pooling misc giant component misc greedy maximal scheduling misc random graphs misc Interference misc Computer Science misc Networking and Internet Architecture misc Information Theory |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
s w sw |
| hierarchy_parent_title |
IEEE ACM transactions on networking |
| hierarchy_parent_id |
165670215 |
| dewey-tens |
620 - Engineering 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
IEEE ACM transactions on networking |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)165670215 (DE-600)1150634-9 (DE-576)034200843 |
| title |
On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs |
| ctrlnum |
(DE-627)OLC1983293970 (DE-599)GBVOLC1983293970 (PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460 (KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim |
| title_full |
On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs |
| author_sort |
Wildman, Jeffrey |
| journal |
IEEE ACM transactions on networking |
| journalStr |
IEEE ACM transactions on networking |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
txt |
| container_start_page |
2086 |
| author_browse |
Wildman, Jeffrey |
| container_volume |
24 |
| class |
620 004 DNB 54.00 bkl 05.00 bkl |
| format_se |
Aufsätze |
| author-letter |
Wildman, Jeffrey |
| doi_str_mv |
10.1109/TNET.2015.2451090 |
| dewey-full |
620 004 |
| title_sort |
on characterizing the local pooling factor of greedy maximal scheduling in random graphs |
| title_auth |
On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs |
| abstract |
The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. |
| abstractGer |
The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. |
| abstract_unstemmed |
The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_151 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 |
| container_issue |
4 |
| title_short |
On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs |
| url |
http://dx.doi.org/10.1109/TNET.2015.2451090 http://ieeexplore.ieee.org/document/7173059 http://arxiv.org/abs/1409.0932 |
| remote_bool |
false |
| author2 |
Weber, Steven |
| author2Str |
Weber, Steven |
| ppnlink |
165670215 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1109/TNET.2015.2451090 |
| up_date |
2024-07-03T20:27:13.963Z |
| _version_ |
1803591020932235264 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1983293970</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220219084609.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161013s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TNET.2015.2451090</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161012</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1983293970</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1983293970</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)a1326-de76b190ad3474cbcb122465949f465ed46c6bbebaa41a6a0068a8b46f7f43460</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0226258420160000024000402086oncharacterizingthelocalpoolingfactorofgreedymaxim</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">004</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">05.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wildman, Jeffrey</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this paper, we provide rigorous characterizations of the LoP factor in large networks modeled as Erdős-Rényi (ER) and random geometric (RG) graphs under the primary interference model. We employ threshold functions to establish critical values for either the edge probability or communication radius to yield useful bounds on the range and expectation of the LoP factor as the network grows large. For sufficiently dense random graphs, we find that the LoP factor is between 1/2 and 2/3, while sufficiently sparse random graphs permit GMS optimality (the LoP factor is 1) with high probability. We then place LoP within a larger context of commonly studied random graph properties centered around connectedness. We observe that edge densities permitting connectivity generally admit cycle subgraphs that form the basis for the LoP factor upper bound of 2/3. We conclude with simulations to explore the regime of small networks, which suggest the probability that an ER or RG graph satisfies LoP and is connected decays quickly in network size.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Limiting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distribution functions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">primary interference</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stability analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IEEE transactions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Connectivity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Erbium</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Upper bound</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">local pooling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">giant component</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">greedy maximal scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">random graphs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Interference</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Networking and Internet Architecture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information Theory</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Weber, Steven</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE ACM transactions on networking</subfield><subfield code="d">New York, NY : IEEE, 1993</subfield><subfield code="g">24(2016), 4, Seite 2086-2099</subfield><subfield code="w">(DE-627)165670215</subfield><subfield code="w">(DE-600)1150634-9</subfield><subfield code="w">(DE-576)034200843</subfield><subfield code="x">1063-6692</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:2086-2099</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TNET.2015.2451090</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/document/7173059</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://arxiv.org/abs/1409.0932</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">05.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2016</subfield><subfield code="e">4</subfield><subfield code="h">2086-2099</subfield></datafield></record></collection>
|
| score |
7.3988304 |