Name
Abstract | ||
|---|---|---|
The evaluation and the prediction of the radio environment is one of the key issues to improve the cognitive radio. The prediction accuracy of the transmission capability in each wireless media will directly affect the performance of the transmission performance. In this paper, a novel approach to predict the radio environment using AR model is shown in detail. In the cognitive radio system using the multi-transmission links, each wireless node selects an optimal wireless module, based on recognition of the radio environment in heterogeneous wireless communication systems. In this scheme, the reliable method to compare the heterogeneous wireless media capacity with normalized scale is necessary. The authors introduce the new index of the wireless resources as "availability of wireless transmission capability" than the existing and the well known the radio intensity like Signal/Noise ratio, BER or re-transmission ratio. This index enables us to compare the wireless resources between the heterogeneous wireless media. Moreover, it can applicable to the unlicensed band like Wi-Fi where many wireless nodes act themselves in the same area. The verification of the possibility and applicability of the radio environment prediction are shown using field data in various points. Based on the results of the verification, the possibility and the accuracy to predict the wireless media capacity according to the radio environment parameter are shown. The result is independent of locations of the field. Furthermore, the advantage of the prediction in comparison with the approach without prediction is shown from the viewpoint of statistics. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CROWNCOM.2008.4562502 | Singapore |
Keywords | Field | DocType |
autoregressive processes,cognitive radio,statistical analysis,AR model,autoregressive model,cognitive radio,heterogeneous wireless communication,radio environment prediction,statistical analysis,transmission capability,AR model,Hurst parameter,cognitive radio,prediction,radio environment | Fixed wireless,Radio resource management,Key distribution in wireless sensor networks,Wireless network,Wireless,Computer science,Computer network,Wireless WAN,Real-time computing,Wi-Fi array,Cognitive radio | Conference |
ISBN | Citations | PageRank |
978-1-4244-2302-6 | 4 | 0.51 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazunori Takeuchi | 1 | 20 | 4.80 |
| Shoji Kaneko | 2 | 59 | 8.42 |
| Shinichi Nomoto | 3 | 41 | 8.66 |