Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks
Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks
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DOI：
10.1007/s11276-021-02627-w
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发表时间：
2021-05-10
影响因子：
3
通讯作者：
AsSadhan, Basil
中科院分区：
文献类型：
--
作者：
Awe, Olusegun Peter;Babatunde, Daniel Adebowale;AsSadhan, Basil
我们使用参数化机器学习方法解决分布式认知无线电网络中的频谱感知问题。特别是，为了减轻感测性能下降，由于在散射体的存在下的次级用户（SU）的移动性，我们提出并研究了一个分类器，该分类器使用基于导频的二阶卡尔曼滤波跟踪器估计主用户（PU）发射机和移动的SU之间的缓慢变化的信道增益。该算法以SU终端的能量测量值为特征向量，采用K均值聚类算法进行初始化，两个质心对应于PU发射机的活动和非活动状态。在移动性下，根据卡尔曼滤波器给出的信道估计，调整对应于活动PU状态的质心，并使用自适应K均值聚类技术对PU活动进行分类决策。此外，为了解决SU接收机可能会遇到位置相关的同信道干扰的可能性，我们提出了一种二次多项式回归算法，用于估计噪声加干扰功率的存在下的移动性，可用于适应质心对应于非活动的PU状态。仿真结果验证了该算法的有效性。
We address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K-means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K-means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.