# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 1 Introduction  
With the rapid growth of diverse technologies and the proliferation of systems and wireless communication devices, there is a rising need for radio spectrum. An an example, billions of items already proliferate in the Internet of Things (IoT) ecosystem [1], leading to novel application domains such as Smart Cities [2], from an all-embracing perspective including variant sub-applications, Industry 4.0, e-government, and others [3]. The availability of radio spectrum has become limited as it is a precious resource. Moreover, statistics from the Federal Communications Commission (FCC) indicate that the existing fixed spectrum allocation policy has led to the inefficient utilization of licensed spectrum bands [4]. Therefore, cognitive radio (CR) [5] has emerged as a promising technology that incorporates intelligent spectrum management techniques to effectively utilize frequency bands in specific times and locations (known as spectrum gaps or, more generically, white spaces) when not in use by licensed users [6, 7]. In this situation, unlicensed users can transmit their signals with the understanding that the transmission of licensed users is adequately safeguarded. This is accomplished through a procedure known as spectrum sensing (SS), which involves the detection and analysis of the radio frequency (RF) environment [8, 9]. Spectrum sensing entails checking whether the licensed user (primary user--PU) is currently transmitting, thereby enabling the unlicensed user (so-called secondary user--SU) to transmit their signals. Conventional SS methods, such as energy detection and matched filtering, struggle to cope with the variability and complexity of real-world wireless environments. The reliability of spectrum sensing is affected by signal fading, shadowing and the presence of various types of interference. Traditional SS algorithms cannot adequately adapt to these challenges, leading to inaccurate or delayed spectrum occupancy detection. Moreover, they usually fail to take full advantage of the temporal, frequency or spatial dependencies that exist in the detected signals, resulting in rather limited performance [10; 11; 12]. Machine learning (ML) techniques have demonstrated significant potential in improving the performance of spectrum sensing. By leveraging ML algorithms, CR networks can learn and adapt to the dynamics of the radio frequency (RF) environment, enhancing the accuracy of spectrum occupancy detection and reducing false positives. Accordingly, researchers have recently turned their attention to ML algorithms for signal sensing [13; 14]. In the context of ML algorithms, deep neural networks (DNNs) excel at learning intricate patterns and representations from data, making them suitable for a wide range of recognition/classification tasks, and, specifically, for spectrum sensing. Deep learning (DL) methods, particularly recurrent neural networks (RNNs), have emerged as the most widely adopted machine learning algorithms. RNNs are renowned for their capability to leverage historical data in order to make precise predictions about the future or current state. RNN algorithms are utilized in [15], where the authors use a signal covariance matrix-based SS algorithm and long short-term memory (LSTM) to jointly extract the spatial cross-correlation features of multiple signals received by the antenna array and the temporal autocorrelation features of single signals. The individual detection performed by each SU using ML is computationally complex and can be inaccurate. Indeed, ML algorithms require a large amount of training data to be able to recognize the temporal, frequency and location dependencies that exist in the transmitted and received signal. The end-user terminal usually does not have sufficient computing and memory resources to store and process the volumes of data needed to train an ML algorithm. As discussed earlier, a common idea is to use cooperative sensing, in which SUs share their sensing results or collected data to cooperatively decide the current state of the spectrum.