An Efficient Direct Solution of Cave-Filling Problems
Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned...
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| Autor*in: |
Naidu, Kalpana [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on communications - New York, NY : IEEE, 1972, 64(2016), 7, Seite 3064-3077 |
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| Übergeordnetes Werk: |
volume:64 ; year:2016 ; number:7 ; pages:3064-3077 |
| Links: |
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| DOI / URN: |
10.1109/TCOMM.2016.2560813 |
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| Katalog-ID: |
OLC1979971587 |
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| 520 | |a Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. | ||
| 650 | 4 | |a Weighted waterfilling problem | |
| 650 | 4 | |a less number of flops | |
| 650 | 4 | |a Resource management | |
| 650 | 4 | |a Peak power constraint | |
| 650 | 4 | |a DSL | |
| 650 | 4 | |a OFDM | |
| 650 | 4 | |a Nickel | |
| 650 | 4 | |a sum-power constraint | |
| 650 | 4 | |a Water resources | |
| 650 | 4 | |a Throughput | |
| 650 | 4 | |a Complexity theory | |
| 650 | 4 | |a cave waterfilling | |
| 700 | 1 | |a Ali Khan, Mohammed Zafar |4 oth | |
| 700 | 1 | |a Hanzo, Lajos |4 oth | |
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10.1109/TCOMM.2016.2560813 doi PQ20161012 (DE-627)OLC1979971587 (DE-599)GBVOLC1979971587 (PRQ)c1252-d3414f3c4a5dd43cb3b495da8c1cc6c8681d84b9573bed48d3801378981392420 (KEY)0043613520160000064000703064efficientdirectsolutionofcavefillingproblems DE-627 ger DE-627 rakwb eng 620 DNB SA 5540 AVZ rvk Naidu, Kalpana verfasserin aut An Efficient Direct Solution of Cave-Filling Problems 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. Weighted waterfilling problem less number of flops Resource management Peak power constraint DSL OFDM Nickel sum-power constraint Water resources Throughput Complexity theory cave waterfilling Ali Khan, Mohammed Zafar oth Hanzo, Lajos oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 64(2016), 7, Seite 3064-3077 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:64 year:2016 number:7 pages:3064-3077 http://dx.doi.org/10.1109/TCOMM.2016.2560813 Volltext http://ieeexplore.ieee.org/document/7463063 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 64 2016 7 3064-3077 |
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10.1109/TCOMM.2016.2560813 doi PQ20161012 (DE-627)OLC1979971587 (DE-599)GBVOLC1979971587 (PRQ)c1252-d3414f3c4a5dd43cb3b495da8c1cc6c8681d84b9573bed48d3801378981392420 (KEY)0043613520160000064000703064efficientdirectsolutionofcavefillingproblems DE-627 ger DE-627 rakwb eng 620 DNB SA 5540 AVZ rvk Naidu, Kalpana verfasserin aut An Efficient Direct Solution of Cave-Filling Problems 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. Weighted waterfilling problem less number of flops Resource management Peak power constraint DSL OFDM Nickel sum-power constraint Water resources Throughput Complexity theory cave waterfilling Ali Khan, Mohammed Zafar oth Hanzo, Lajos oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 64(2016), 7, Seite 3064-3077 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:64 year:2016 number:7 pages:3064-3077 http://dx.doi.org/10.1109/TCOMM.2016.2560813 Volltext http://ieeexplore.ieee.org/document/7463063 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 64 2016 7 3064-3077 |
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10.1109/TCOMM.2016.2560813 doi PQ20161012 (DE-627)OLC1979971587 (DE-599)GBVOLC1979971587 (PRQ)c1252-d3414f3c4a5dd43cb3b495da8c1cc6c8681d84b9573bed48d3801378981392420 (KEY)0043613520160000064000703064efficientdirectsolutionofcavefillingproblems DE-627 ger DE-627 rakwb eng 620 DNB SA 5540 AVZ rvk Naidu, Kalpana verfasserin aut An Efficient Direct Solution of Cave-Filling Problems 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. Weighted waterfilling problem less number of flops Resource management Peak power constraint DSL OFDM Nickel sum-power constraint Water resources Throughput Complexity theory cave waterfilling Ali Khan, Mohammed Zafar oth Hanzo, Lajos oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 64(2016), 7, Seite 3064-3077 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:64 year:2016 number:7 pages:3064-3077 http://dx.doi.org/10.1109/TCOMM.2016.2560813 Volltext http://ieeexplore.ieee.org/document/7463063 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 64 2016 7 3064-3077 |
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10.1109/TCOMM.2016.2560813 doi PQ20161012 (DE-627)OLC1979971587 (DE-599)GBVOLC1979971587 (PRQ)c1252-d3414f3c4a5dd43cb3b495da8c1cc6c8681d84b9573bed48d3801378981392420 (KEY)0043613520160000064000703064efficientdirectsolutionofcavefillingproblems DE-627 ger DE-627 rakwb eng 620 DNB SA 5540 AVZ rvk Naidu, Kalpana verfasserin aut An Efficient Direct Solution of Cave-Filling Problems 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. Weighted waterfilling problem less number of flops Resource management Peak power constraint DSL OFDM Nickel sum-power constraint Water resources Throughput Complexity theory cave waterfilling Ali Khan, Mohammed Zafar oth Hanzo, Lajos oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 64(2016), 7, Seite 3064-3077 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:64 year:2016 number:7 pages:3064-3077 http://dx.doi.org/10.1109/TCOMM.2016.2560813 Volltext http://ieeexplore.ieee.org/document/7463063 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 64 2016 7 3064-3077 |
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10.1109/TCOMM.2016.2560813 doi PQ20161012 (DE-627)OLC1979971587 (DE-599)GBVOLC1979971587 (PRQ)c1252-d3414f3c4a5dd43cb3b495da8c1cc6c8681d84b9573bed48d3801378981392420 (KEY)0043613520160000064000703064efficientdirectsolutionofcavefillingproblems DE-627 ger DE-627 rakwb eng 620 DNB SA 5540 AVZ rvk Naidu, Kalpana verfasserin aut An Efficient Direct Solution of Cave-Filling Problems 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. Weighted waterfilling problem less number of flops Resource management Peak power constraint DSL OFDM Nickel sum-power constraint Water resources Throughput Complexity theory cave waterfilling Ali Khan, Mohammed Zafar oth Hanzo, Lajos oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 64(2016), 7, Seite 3064-3077 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:64 year:2016 number:7 pages:3064-3077 http://dx.doi.org/10.1109/TCOMM.2016.2560813 Volltext http://ieeexplore.ieee.org/document/7463063 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 64 2016 7 3064-3077 |
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The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. 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efficient direct solution of cave-filling problems |
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An Efficient Direct Solution of Cave-Filling Problems |
| abstract |
Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. |
| abstractGer |
Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. |
| abstract_unstemmed |
Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of <inline-formula> <tex-math notation="LaTeX">M^{2} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M^{2}) </tex-math></inline-formula>, under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">O(M) </tex-math></inline-formula>. Finally, simulation results corroborating the analysis are presented. |
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