Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks
The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectivel...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhiqun, Hu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
7 |
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| Übergeordnetes Werk: |
Enthalten in: Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption - Moosavi, Maryam ELSEVIER, 2014transfer abstract, Beijing |
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| Übergeordnetes Werk: |
volume:22 ; year:2015 ; number:6 ; pages:94-100 ; extent:7 |
| Links: |
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| DOI / URN: |
10.1016/S1005-8885(15)60700-2 |
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ELV039876314 |
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| 520 | |a The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. | ||
| 520 | |a The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. | ||
| 650 | 7 | |a attractor selection |2 Elsevier | |
| 650 | 7 | |a activity |2 Elsevier | |
| 650 | 7 | |a WiFi-offloading algorithm |2 Elsevier | |
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| 700 | 1 | |a Xiangming, Wen |4 oth | |
| 700 | 1 | |a Zhaoming, Lu |4 oth | |
| 700 | 1 | |a Yiqing, Wang |4 oth | |
| 700 | 1 | |a Dabing, Ling |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |a Moosavi, Maryam ELSEVIER |t Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption |d 2014transfer abstract |g Beijing |w (DE-627)ELV023058757 |
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10.1016/S1005-8885(15)60700-2 doi GBVA2015017000028.pica (DE-627)ELV039876314 (ELSEVIER)S1005-8885(15)60700-2 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 370 VZ 5,3 ssgn Zhiqun, Hu verfasserin aut Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. attractor selection Elsevier activity Elsevier WiFi-offloading algorithm Elsevier heterogeneous wireless networks Elsevier Xiangming, Wen oth Zhaoming, Lu oth Yiqing, Wang oth Dabing, Ling oth Enthalten in Moosavi, Maryam ELSEVIER Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption 2014transfer abstract Beijing (DE-627)ELV023058757 volume:22 year:2015 number:6 pages:94-100 extent:7 https://doi.org/10.1016/S1005-8885(15)60700-2 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 GBV_ILN_613 AR 22 2015 6 94-100 7 045F 620 |
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10.1016/S1005-8885(15)60700-2 doi GBVA2015017000028.pica (DE-627)ELV039876314 (ELSEVIER)S1005-8885(15)60700-2 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 370 VZ 5,3 ssgn Zhiqun, Hu verfasserin aut Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. attractor selection Elsevier activity Elsevier WiFi-offloading algorithm Elsevier heterogeneous wireless networks Elsevier Xiangming, Wen oth Zhaoming, Lu oth Yiqing, Wang oth Dabing, Ling oth Enthalten in Moosavi, Maryam ELSEVIER Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption 2014transfer abstract Beijing (DE-627)ELV023058757 volume:22 year:2015 number:6 pages:94-100 extent:7 https://doi.org/10.1016/S1005-8885(15)60700-2 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 GBV_ILN_613 AR 22 2015 6 94-100 7 045F 620 |
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10.1016/S1005-8885(15)60700-2 doi GBVA2015017000028.pica (DE-627)ELV039876314 (ELSEVIER)S1005-8885(15)60700-2 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 370 VZ 5,3 ssgn Zhiqun, Hu verfasserin aut Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. attractor selection Elsevier activity Elsevier WiFi-offloading algorithm Elsevier heterogeneous wireless networks Elsevier Xiangming, Wen oth Zhaoming, Lu oth Yiqing, Wang oth Dabing, Ling oth Enthalten in Moosavi, Maryam ELSEVIER Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption 2014transfer abstract Beijing (DE-627)ELV023058757 volume:22 year:2015 number:6 pages:94-100 extent:7 https://doi.org/10.1016/S1005-8885(15)60700-2 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 GBV_ILN_613 AR 22 2015 6 94-100 7 045F 620 |
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10.1016/S1005-8885(15)60700-2 doi GBVA2015017000028.pica (DE-627)ELV039876314 (ELSEVIER)S1005-8885(15)60700-2 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 370 VZ 5,3 ssgn Zhiqun, Hu verfasserin aut Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. attractor selection Elsevier activity Elsevier WiFi-offloading algorithm Elsevier heterogeneous wireless networks Elsevier Xiangming, Wen oth Zhaoming, Lu oth Yiqing, Wang oth Dabing, Ling oth Enthalten in Moosavi, Maryam ELSEVIER Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption 2014transfer abstract Beijing (DE-627)ELV023058757 volume:22 year:2015 number:6 pages:94-100 extent:7 https://doi.org/10.1016/S1005-8885(15)60700-2 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 GBV_ILN_613 AR 22 2015 6 94-100 7 045F 620 |
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10.1016/S1005-8885(15)60700-2 doi GBVA2015017000028.pica (DE-627)ELV039876314 (ELSEVIER)S1005-8885(15)60700-2 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 370 VZ 5,3 ssgn Zhiqun, Hu verfasserin aut Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. attractor selection Elsevier activity Elsevier WiFi-offloading algorithm Elsevier heterogeneous wireless networks Elsevier Xiangming, Wen oth Zhaoming, Lu oth Yiqing, Wang oth Dabing, Ling oth Enthalten in Moosavi, Maryam ELSEVIER Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption 2014transfer abstract Beijing (DE-627)ELV023058757 volume:22 year:2015 number:6 pages:94-100 extent:7 https://doi.org/10.1016/S1005-8885(15)60700-2 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 GBV_ILN_613 AR 22 2015 6 94-100 7 045F 620 |
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Enthalten in Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption Beijing volume:22 year:2015 number:6 pages:94-100 extent:7 |
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Enthalten in Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption Beijing volume:22 year:2015 number:6 pages:94-100 extent:7 |
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Agmatine protects against intracerebroventricular streptozotocin-induced water maze memory deficit, hippocampal apoptosis and Akt/GSK3β signaling disruption |
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Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks |
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The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. |
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The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. |
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The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment. |
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