Fang Yuan, Li Lixiang, Li Yixiao, Peng Haipeng, Yang Yixian
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Computer Science and Technology, Henan Polytechnic University, 2001 Century Avenue, Henan, Jiaozuo 454003, China.
Sensors (Basel). 2020 Feb 26;20(5):1264. doi: 10.3390/s20051264.
For wireless communication networks, cognitive radio (CR) can be used to obtain the available spectrum, and wideband compressed sensing plays a vital role in cognitive radio networks (CRNs). Using compressed sensing (CS), sampling and compression of the spectrum signal can be simultaneously achieved, and the original signal can be accurately recovered from the sampling data under sub-Nyquist rate. Using a set of wideband random filters to measure the channel energy, only the recovery of the channel energy is necessary, rather than that of all the original channel signals. Based on the semi-tensor product, this paper proposes a new model to achieve the energy compression and reconstruction of spectral signals, called semi-tensor product compressed spectrum sensing (STP-CSS), which is a generalization of traditional spectrum sensing. The experimental results show that STP-CSS can flexibly generate a low-dimensional sensing matrix for energy compression and parallel reconstruction of the signal. Compared with the existing methods, STP-CSS is proved to effectively reduce the calculation complexity of sensor nodes. Hence, the proposed model markedly improves the spectrum sensing speed of network nodes and saves storage space and energy consumption.
对于无线通信网络，认知无线电（CR）可用于获取可用频谱，而宽带压缩感知在认知无线电网络（CRN）中起着至关重要的作用。利用压缩感知（CS），可以同时实现频谱信号的采样和压缩，并且能够在亚奈奎斯特速率下从采样数据中准确恢复原始信号。使用一组宽带随机滤波器来测量信道能量，只需要恢复信道能量，而不是所有原始信道信号。基于半张量积，本文提出了一种新的模型来实现频谱信号的能量压缩和重构，称为半张量积压缩频谱感知（STP-CSS），它是传统频谱感知的一种推广。实验结果表明，STP-CSS能够灵活地生成用于能量压缩和信号并行重构的低维感知矩阵。与现有方法相比，STP-CSS被证明能有效降低传感器节点的计算复杂度。因此，所提出的模型显著提高了网络节点的频谱感知速度，并节省了存储空间和能耗。
