Probability theory and mathematical statistics

Spectrum handoff in cognitive radio networks

In the coming 6G communications, network densification, high throughput, positioning accuracy, energy efficiency, and many other key performance indicator requirements are becoming increasingly strict In the future, how to improve work efficiency while saving costs is one of the foremost research directions in wireless communications Being able to learn from experience is an important way to approach this vision Transfer learning (TL) encourages new tasks/domains to learn from experienced tasks/domains for helping new tasks become faster and more efficient TL can help save energy and improve efficiency with the correlation and similarity information between different tasks in many fields of wireless communications Therefore, applying TL to future 6G communications is a very valuable topic TL has achieved some good results in wireless communications In order to improve the development of TL applied in 6G communications, this article performs a comprehensive review of the TL algorithms used in different wireless communication fields, such as base stations/access points switching, indoor wireless localization and intrusion detection in wireless networks, etc Moreover, the future research directions of mutual relationship between TL and 6G communications are discussed in detail Challenges and future issues about integrate TL into 6G are proposed at the end This article is intended to help readers understand the past, present, and future between TL and wireless communications

Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges

Recent advances in wireless technologies have led to several autonomous deployments of such networks. As nodes across distributed networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and predict the RF signals and associated parameters that characterize the RF environment. However, in the presence of adversaries, malicious activities such as jamming and spoofing are inevitable, making most machine learning techniques ineffective in such environments. In this paper we propose the Radio Frequency Adversarial Learning (RFAL) framework for building a robust system to identify rogue RF transmitters by designing and implementing a generative adversarial net (GAN). We hope to exploit transmitter specific “signatures” like the in-phase (I) and quadrature (Q) imbalance (i.e., the I/Q imbalance ) present in all transmitters for this task, by learning feature representations using a deep neural network that uses the I/Q data from received signals as input. After detection and elimination of the adversarial transmitters RFAL further uses this learned feature embedding as “fingerprints” for categorizing the trusted transmitters. More specifically, we implement a generative model that learns the sample space of the I/Q values of known transmitters and uses the learned representation to generate signals that imitate the transmissions of these transmitters. We program 8 universal software radio peripheral (USRP) software defined radios (SDRs) as trusted transmitters and collect “over-the-air” raw I/Q data from them using a Realtek Software Defined Radio (RTL-SDR), in a laboratory setting. We also implement a discriminator model that discriminates between the trusted transmitters and the counterfeit ones with 99.9% accuracy and is trained in the GAN framework using data from the generator. Finally, after elimination of the adversarial transmitters, the trusted transmitters are classified using a convolutional neural network (CNN), a fully connected deep neural network (DNN) and a recurrent neural network (RNN) to demonstrate building of an end-to-end robust transmitter identification system with RFAL. Experimental results reveal that the CNN, DNN, and RNN are able to correctly distinguish between the 8 trusted transmitters with 81.6%, 94.6% and 97% accuracy respectively. We also show that better “trusted transmission” classification accuracy is achieved for all three types of neural networks when data from two different types of transmitters (different manufacturers) are used rather than when using the same type of transmitter (same manufacturer).

RFAL: Adversarial Learning for RF Transmitter Identification and Classification

An automatic modulation classification has a very broad application in wireless communications. Recently, deep learning has been used to solve this problem and achieved superior performance. In most cases, the input size is fixed in convolutional neural network (CNN)-based modulation classification. However, the duration of the actual radio signal burst is variable. When the signal length is greater than the CNN input length, how to make full use of the complete signal burst to improve the classification accuracy is a problem needs to be considered. In this paper, three fusion methods are proposed to solve this problem, such as voting-based fusion, confidence-based fusion, and feature-based fusion. The simulation experiments are done to analyze the performance of these methods. The results show that the three fusion methods perform better than the non-fusion method. The performance of the two fusion methods based on confidence and feature is very close, which is better than that of the voting-based fusion.

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Fusion Methods for CNN-Based Automatic Modulation Classification

Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method.

Spectrum sensing based on deep learning classification for cognitive radios

In modern society, the demand for radio spectrum resources is increasing. As the information carriers of wireless transmission data, radio signals exhibit the characteristics of big data in terms of volume, variety, value, and velocity. How to uniformly handle these radio signals and obtain value from them is a problem that needs to be studied. In this paper, a big data processing architecture for radio signals is presented and a new approach of end-to-end signal processing based on deep learning is discussed in detail. The radio signal intelligent search engine is used as an example to verify the architecture, and the system components and experimental results are introduced. In addition, the applications of the architecture in cognitive radio, spectrum monitoring, and cyberspace security are introduced. Finally, challenges are discussed, such as unified representation of radio signal features, distortionless compression of wideband sampled data, and deep neural networks for radio signals.

Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used are very limited in scale. In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper, we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900 000 and 13 000 000,, respectively, and with categories of 3143 and 5157, respectively. We use the same inception-residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. The experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets.

Shichuan Chen

Papers

Fusion Methods for CNN-Based Automatic Modulation Classification

Spectrum sensing based on deep learning classification for cognitive radios

Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification