# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 2 Related Works  
Since its advent, deep learning has made remarkable progress in the theory and practice of many fields of science and technology. In [5; 6], deep learning was especially powerful at extracting meaningful features from complex and diverse data that are difficult to analyze. This strength has been demonstrated by achieving astonishing results in computer vision [7], natural language processing [8], and audio-signal processing [9]. In recent years, several researchers have applied deep-learning technology to wireless communication fields such as physical layer [10], modulation classification [11; 12], channel estimation [13], and decoding [14]. In addition, due to their powerful performance in classification, some researchers have applied machine-learning neural networks to spectrum sensing [15; 16; 17; 18; 19; 20]. To decide whether a channel is occupied by a primary user, the work [15] adopted a basic neural network that stacks several linear layers. This method used the energy values and likelihood ratio statistics as input. In [16], a convolution neural network (CNN) was used using cyclostationary features and energy features as input. In [17], the authors also exploited a CNN for spectrum sensing in cognitive radio. Unlike [16], they considered covariance matrices of received signals as input. For the technique based on orthogonal frequency division multiplexing (OFDM), the researchers in [18] adopted a stacked autoencoder with basic linear layers. The proposed network used raw time-domain signals as input, and then the extracted features were passed to the classifier using logistic regression. To develop a context-aware cognitive radio, the authors in [19] utilized the power of each fast Fourier transform bin as input of their neural network. They verified the performance of machine-learning-based approaches using the software-defined radio platform. The authors in [20] proposed an unsupervised deep-learning-based spectrum sensing algorithm, while all the previous works in [15; 16; 17; 18; 19] proposed supervised learning-based spectrum sensing algorithms. The aforementioned studies confirmed that the powerful classification ability of neural networks can improve spectrum sensing performance, and this improvement proved that neural networks can extract essential features from signal samples. The task of detecting the number of transmitted signals is more complicated than spectrum sensing that detects the presence or absence of signals. There have been several works that detected the number of signals [21; 22]. The work [21] proposed two most common methods developed based on two famous information theoretic criteria: the Akaike information criterion (AIC) and the minimum description length (MDL) criterion [23; 24; 25; 26]. The performance of the MDL estimator was analyzed asymptotically in [22]. If there are several observed received signal vectors, the optimal estimation on the number of sources based on the maximum likelihood probability can be achieved by comparing the sample covariance matrix obtained from the observed signal vectors and the ideal covariance matrix. These methods may be utilized in commercial OFDM-based wireless technologies such as Wi-Fi and LTE. However, when such detection schemes are adopted in OFDM-based communications, there is an inherent limitation that all transmitted signals should be synchronized from a cyclic prefix point of view. Therefore, these schemes will not work in the asynchronous transmission situation where the cyclic prefixes of the transmitted signals are not properly aligned. In other words, since synchronization between all transmitted signals should be required in existing OFDM-based communications, there is no work to develop a method for detecting asynchronously transmitted signals. For a public safety network, 3 GPP has standardized public safety-LTE (PS-LTE) [27]. PS-LTE is a technology that accommodates functions required for public safety communication based on existing LTE technology.