Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular Networks
This paper investigates the traffic pattern prediction based on seasonal deviation and spectrum reallocation with multiple channel width in cognitive cellular networks. Compared to the existing approaches based on time series or classical statistic method, the binary exponential deviation offset pre...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/138032 |
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| _version_ | 1832547915015389184 |
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| author | Lu Wang Zhong Zhou Wei Wu |
| author_facet | Lu Wang Zhong Zhou Wei Wu |
| author_sort | Lu Wang |
| collection | DOAJ |
| description | This paper investigates the traffic pattern prediction based on seasonal deviation and spectrum reallocation with multiple channel width in cognitive cellular networks. Compared to the existing approaches based on time series or classical statistic method, the binary exponential deviation offset prediction proposed in this paper focuses on the increment or decrement on every sampling point during an exponential offset period. Then the deviations will be revised at different levels in the next prediction process. The proposed approach is validated with some real end-user data from a WiFi network and simulation experiments. Based on such a precise prediction, we allocate the channels with different bandwidth to end-users according to diverse quality-of-service (QoS), which increases both the system's profits and actual spectrum utilization. The multidimensional bounded knapsack problem is introduced to divide channels, to which the proposed balance between value density and request probability strategy gets the approximate solution. The simulation experiment results show its good performance in not only utility but also spectrum utilization of the base-stations, especially when the resources are deficient. |
| format | Article |
| id | doaj-art-5558341e19fd45fa9b05366eb0cb185b |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5558341e19fd45fa9b05366eb0cb185b2025-02-03T06:42:58ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-05-011010.1155/2014/138032138032Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular NetworksLu WangZhong ZhouWei WuThis paper investigates the traffic pattern prediction based on seasonal deviation and spectrum reallocation with multiple channel width in cognitive cellular networks. Compared to the existing approaches based on time series or classical statistic method, the binary exponential deviation offset prediction proposed in this paper focuses on the increment or decrement on every sampling point during an exponential offset period. Then the deviations will be revised at different levels in the next prediction process. The proposed approach is validated with some real end-user data from a WiFi network and simulation experiments. Based on such a precise prediction, we allocate the channels with different bandwidth to end-users according to diverse quality-of-service (QoS), which increases both the system's profits and actual spectrum utilization. The multidimensional bounded knapsack problem is introduced to divide channels, to which the proposed balance between value density and request probability strategy gets the approximate solution. The simulation experiment results show its good performance in not only utility but also spectrum utilization of the base-stations, especially when the resources are deficient.https://doi.org/10.1155/2014/138032 |
| spellingShingle | Lu Wang Zhong Zhou Wei Wu Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular Networks International Journal of Distributed Sensor Networks |
| title | Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular Networks |
| title_full | Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular Networks |
| title_fullStr | Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular Networks |
| title_full_unstemmed | Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular Networks |
| title_short | Traffic Pattern Prediction and Spectrum Allocation with Multiple Channel Width in Cognitive Cellular Networks |
| title_sort | traffic pattern prediction and spectrum allocation with multiple channel width in cognitive cellular networks |
| url | https://doi.org/10.1155/2014/138032 |
| work_keys_str_mv | AT luwang trafficpatternpredictionandspectrumallocationwithmultiplechannelwidthincognitivecellularnetworks AT zhongzhou trafficpatternpredictionandspectrumallocationwithmultiplechannelwidthincognitivecellularnetworks AT weiwu trafficpatternpredictionandspectrumallocationwithmultiplechannelwidthincognitivecellularnetworks |