A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

认知无线网络中基于加权选择融合的协作频谱预测策略（英文）

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.

为了进一步提高认知无线网络中频谱预测的准确性,提出一种利用空间多样性进行加权选择融合的协作频谱预测策略.首先,综合考虑主用户行为特点以及信号衰落的影响,设计一种基于遗传算法的神经网络进行本地预测.然后,设计一种基于迭代自组织数据分析算法的融合筛选方法来选择性能最好的本地预测器进行协作融合.此外,考虑到本地预测器之间的空间多样性,提出了一种基于预测可靠性的加权融合规则.最后,采用高斯近似方法对所提出的基于加权选择融合的协作预测策略的性能进行分析,并给出了全局预测精度和吞吐量的表达式.实验结果表明,基于加权选择融合的协作频谱预测策略相比于其他的协作频谱预测策略具有更高的预测准确性,并且能够使得认知无线网络中的吞吐量得到很大程度的提高.