Cooperative
Cooperative
复制标题
DOI：
10.1177/107755875000700805
复制
发表时间：
1950-08
影响因子：
2.5
通讯作者：
中科院分区：
文献类型：
--
作者：
结合频谱感知(SS)和主用户(PU)TRAFfic预测为认知无线电网络提供了一个平台，从中可以做出知情和主动的运营决策。这些决策的成功在很大程度上取决于预测的准确性。允许次级用户(SU)以协作方式执行这些预测允许改进该过程的准确性，因为各个SU可能由于不良的信道条件而遭受SS和预测不准确的影响。为了克服这些问题，本文提出了一种合作预测PU TRAFfic行为的方法，该方法通过SU合作将SS和预测结合起来。研究了用于合作预测的融合前和融合后场景，并考虑了一些二元预测方法(包括作者自己的简单技术)。研究了在不同PU TRAFfic条件下，10个SU经历不同信道条件下的协作预测性能，并提出了一种次优协作预测算法以最小化协作预测误差。仿真结果表明，该预测方法的预测精度受到PU-TRAF-fl-c模式的影响，而合作预测在所考虑的大多数TRAF-fi-c条件下，预测精度不会有显著的提高。然而，这是以增加计算复杂性为代价的。融合前的情景被发现是最准确的情景(提高了25%)，但也比没有融合时的复杂程度高出11倍。发现合作预测算法进一步改善了这些结果。
Combining spectrum sensing (SS) and primary user (PU) traffic forecasting provides a cognitive radio network with a platform from which informed and proactive operational decisions can be made. The success of these decisions is largely dependent on prediction accuracy. Allowing secondary users (SU) to perform these predictions in a collaborative manner allows for an improvement in the accuracy of this process, since individual SUs may suffer from SS and prediction inaccuracies due to poor channel conditions. To overcome these problems a collaborative approach to forecasting PU traffic behaviour, that combines SS and forecasting through SU cooperation, has been proposed in this article. Both pre-fusion and post-fusion scenarios for cooperative prediction were investigated and a number of binary prediction methods were considered (including the authors’ own simple technique). Cooperative prediction performance was investigated, under various PU traffic conditions, for a group of ten SUs experiencing different channel conditions and a sub-optimal cooperative forecasting algorithm was proposed to minimise cooperative prediction error. Simulation results indicated that the accuracy of the prediction methods was influenced by the PU traffic pattern and that cooperative prediction lead to a significant improvement in prediction accuracy under most of the traffic conditions considered. However, this came at the cost of increased computational complexity. The pre-fusion scenario was found to be the most accurate scenario (up to 25 % improvement), but was also eleven times more complex than when no fusion was employed. The cooperative forecasting algorithm was found to further improve these results.