Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Abstract

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

“…For improved double threshold energy detector, the optimization problem when K, L = 1, 2… K, and SNR are known, can be defined by the Eqs. ( 12), ( 13), (17), and (18).…”

“…In some cases, the optimal sample size might be too large. This is the main disadvantage in spectrum sensing at a low SNR environment because of the limitation on the maximal allowable sensing time and this may lead to inefficient spectrum utilization as is mentioned earlier in this subsection [18]. The sample size here can be modified by using a neural network through feeding it as a targeted output and predefined another network requirement like SNR and detection, false alarm, probabilities as a targeted input to calculate the optimum sample size [19].…”

1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing

“…Soni B. et al [38] proposed an LSTM-based SS method that learns implicit features from the spectrum data, such as temporal correlations using PU activity statistics, and it achieved improved detection performance and classification accuracy at low signal-to-noise ratios. In the same context of CSS, Xu M. et al [39] proposed a multi-feature combination network, which simultaneously extracts spatial and temporal features through a parallel structure that leverages the complementary modeling capabilities of 1DCNN and gated recurrent unit (GRU) networks. The experimental results indicated that the proposed approach achieved competitive performance.…”

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

“…Another important tool, owing to the computing power and the amount of available data, is neural networks (NNs), used widely from pattern recognition and image classification to financial market behavior prediction and autonomous vehicle driving [24]. For spectrum sensing, for example, in [25], an NN was implemented to obtain the local information on single-node spectrum detection (spatial and temporal features). The information (extracted features) from multiple nodes fed another NN, thus permitting cooperation in the CRN.…”