# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 2 Related Works
Among these methods, the K-means algorithm, widely adopted for the clustering of data, has demonstrated its effectiveness in grouping data into adjacent partitions based on similar features [26; 27]. In [28], a new spectrum sensing method based on the empirical mode decomposition algorithm and K-means clustering algorithm is proposed to solve the problems related to the poor performance of traditional spectrum sensing methods under low signal-to-noise ratios. A blind multi-band spectrum sensing method requiring no knowledge of the noise power, primary signal and wireless channel is proposed based on the K-means clustering in [29]. In this approach, the K-means clustering algorithm is used to identify the occupied sub-band set and the idle sub-band set. Deep learning is a promising approach to enhancing the detection performance and supporting blind sensing, as DL models can automatically extract relevant features and patterns from data that are difficult or impossible to detect using traditional methods [30; 31]. [PERSON] et al. [32] utilized a CNN for SS, using transfer learning from computer vision to improve the detection of various signal types. Similarly, transfer learning has been applied to a residual neural network (ResNet) model for this purpose [33]. [PERSON] et al. [34] combined an LSTM network with a CNN to capture global dependencies and local features from time series data, outperforming separate RNN and CNN structures. [PERSON] et al. [35] proposed a blind SS method based on DL that used an end to end 1 DCNN and LSTM network structure. This method showed better performance than an energy detector, especially when the signal-to-noise ratio was low, and it showed the potential to significantly enhance the performance of cognitive radio networks (CRNs) in terms of efficient detection. Individual SS, where each radio device independently monitors the spectrum to detect unused or available sub-bands, has some limitations that can lead to problems in efficient spectrum usage [36]. To address these problems, the authors of [37] proposed a CNN-based CSS technique that extracts features from the observed signal to enhance the sensing performance, considering three classical CNN-based CSS schemes: LeNet, AlexNet and VGG-16. The simulation results demonstrated that the proposed schemes achieved significantly improved sensing accuracy compared to traditional ones. [PERSON] 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, [PERSON] 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 1 DCNN and gated recurrent unit (GRU) networks. The experimental results indicated that the proposed approach achieved competitive performance. In [40], a hybrid CNN-LSTM architecture for CSS was proposed, demonstrating high detection accuracy and reduced complexity compared to other DL-based methods. To further improve the detection performance, a hierarchical cooperative LSTM-based CSS method was proposed in [17] to learn the PU activity pattern at both the SU level and group level. The simulation results showed that this method outperformed the state-of-the-art approaches in terms of detection probability and classification accuracy. To overcome the limitations of the traditional CNN and LSTM [41; 42], [PERSON] et al. [43] proposed a deep neural network model that incorporates a two-layer 1 DCNN, bidirectional LSTM and self-attention (SA) techniques. The 1 DCNN extracts local patterns from a given time series, while BiLSTM mines long- and short-period dependencies in opposite directions. The SA helps to relate the features at specific positions of the time series, leading to improved performance.