A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Frequency Domain Features. <s> Optimizing the coexistence performance of a cognitive radio (CR) greatly depends on the extent of its awareness of the radio environment. The more knowledge a CR can acquire about primary user (PU) systems, the better it can plan its radio activities. Ideally, it would like to classify the PU systems with respect to ‘known standards’ so that the documented knowledge of PU systems can be usefully utilized. Intensive research has been done in the area of signal classification but merely with respect to modulations. We present a novel neuro-fuzzy signal classifier (NFSC) to classify signals with respect to known standards. The NFSC demonstrates excellent performance by using modern A/D based wideband data acquisition as well as traditional heterodyne narrowband transceivers in real-time coexistence environments. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Frequency Domain Features. <s> The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference avoidance, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 μs and an acquisition bandwidth of 10 MHz. The CNN distinguishes between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95 % for signal-to-noise ratio of at least −5 dB. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Frequency Domain Features. <s> This paper presents end-to-end learning from spectrum data—an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to: 1) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments and 2) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this paper is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated: 1) modulation recognition and 2) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation, and the frequency domain representation. From our analysis, we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%. <s> BIB003
In contrast to time features, frequency features contain more useful characteristics, such as bandwidth, center frequency, and power spectral density, which are essential for wireless technology recognition. What is more, effective frequency domain data can greatly alleviate the problems of data transmission and storage in actual deployment process. As one of machine learning, fuzzy classifier has been concerned in wireless technology recognition. In  , a new fuzzy logic (FL) method was presented to recognize WLAN, BT, and FSK signals. The power spectral density (PSD) information was used to get the bandwidth and center frequency for labelling the signals corresponding to standards. Results demonstrated that the proposed strategy is efficient for explicit signal features extraction. Likewise, neurofuzzy signal classifier (NFSC) to recognize nanoNET, WLAN, Atmel, and BT signals by utilizing measured PSD information is presented in BIB001 . Results show that the performance is improved by using wideband and narrowband data acquisition modes in real-time coexistence environments. As a way forward, deep learning has also been developed in recent literatures. In BIB003 , the time-domain data was also mapped into frequency-domain representation by FFT. Results show that the CNN model trained on FFT data has significant improvement in accuracy compared to time domain features. Similarly, a reduced CNN model with frequency-domain data is proposed in BIB002 . The approach is capable of identifying several common IEEE 802.x signals in coexistence. Simulations indicate that the performance of reduced CNN model is superior to most state-of-theart algorithms. In  , the authors explore the combination of averaged magnitude FFT processing and LSTM model with distributed sensor architecture. The result shows that the proposed scheme could classify DVB, GSM, LTE, Radar, Tetra, and WFM signals.