The access security of wireless devices is a serious challenge in present wireless network security. Radio frequency (RF) fingerprint recognition technology as an important non-password authentication technology attracts more and more attention, because of its full use of radio frequency characteristics that cannot be imitated to achieve certification. In this paper, a RF fingerprint identification method based on dimensional reduction and machine learning is proposed as a component of intrusion detection for resolving authentication security issues. We compare three kinds of dimensional reduction methods, which are the traditional PCA, RPCA and KPCA. And we take random forests, support vector machine, artificial neural network and grey correlation analysis into consideration to make decisions on the dimensional reduction data. Finally, we obtain the recognition system with the best performance. Using a combination of RPCA and random forests, we achieve 90% classification accuracy is achieved at SNR 
 $$\ge $$
  10 dB when reduced dimension is 76. The proposed method improves wireless device authentication and improves security protection due to the introduction of RF fingerprinting.

The individual identification method of wireless device based on dimensionality reduction and machine learning

Machine Learning (ML) for Optical Communication (OC) is certainly a hot topic emerged recently and will continue to raise interest at least for the next few years. The rate of research development in this area is growing very rapidly. Novelty of this research direction resides mainly in the peculiarity of the application field, rather than in the methodological approaches, which are (at least up to now) state-of-the-art ML algorithms. Reviewing the literature shows that many of the ML algorithms have not yet been used in this area, and many of the OC applications are not considered yet, which reflects the fact that the research topic is pristine. Accordingly, tutorial investigations are quiet necessary in this filed to help researchers be aware about the last progressions and cavities of this field. Although several tutorials have been released recently, they considered this topic from OC view, and neglected ML view. However, it is required to have an investigations about the ML algorithms used in this subject. Accordingly, for the first time, this paper reviews ML for OC literature from ML viewpoint. This view could be really helpful because only OC experts work on ML for OC, and they are not ML experts, so it could really help them to have a comprehensive view on the ML subjects implantable in OC. It has worth to mention that compared with other works, this survey reviews much more investigations; therefore, it has more generality, and gives the reader to have a comprehensive overview on this topic.

pdf/a-survey-on-machine-learning-for-optical-communication-4zpx7s20eu.pdf

A Survey on Machine Learning for Optical Communication [Machine Learning View]

In this paper, cross-linked polyethylene (XLPE) cables of the same batch from Factory A, which ran from 1 to 8 years in Jiangsu Province, are sampled. Some widely accepted aging characterization methods of XLPE cables such as the gel content test, differential scanning calorimetry (DSC) test, tensile test and hardness test are employed to obtain the physicochemical, mechanical and electrical properties of the samples. Then, some lifespan prediction parameters significantly correlated with operating time are obtained through correlation calculations. Finally, a prediction method is proposed to predict the operating time of XLPE cables from Factory A. The test results indicate that parameters including the gel content Cge, the crystallinity XC, tensile strength σ, ultimate elongation δ, the dielectric permittivity e, and the dielectric loss Jtan are significantly correlated with operating time, which can be used in evaluating the aging degree of XLPE cables. Moreover, due to the high accuracy of the experimental verification, it turns out that the lifespan prediction method proposed in this paper can be used to determine the operating time of XLPE cables from Factory A in future research.

https://www.mdpi.com/2076-3417/10/15/5381/pdf

Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables

With the rapid development of communication and information technology, it is difficult for traditional signal detection and recognition methods to accurately acquire and identify the intelligence under complex environments. In order to solve this problem, this paper proposes a subtle feature extraction and recognition algorithm for radiation source individual signals based on multidimensional hybrid features. Firstly, Hilbert transform was performed on the radiation source signals from 10 identical radio devices, and the subtle features of different radiation sources’ signals were extracted. Then, traditional principal component analysis (PCA) algorithm was used to extract and reduce the principal components of the extracted feature data sets. Aiming at the insufficiency of traditional PCA algorithm, an improved principal component analysis algorithm was proposed. At last, a gray relation algorithm was used to classify and identify the radiation source individual signals, and the recognition rate was calculated. Experimental results show that Hilbert transform combined with the improved PCA algorithm can achieve a recognition rate of 99.67% for the "fingerprint" features of radiation source individual signals under the signal-to-noise ratio (SNR) of 20 dB. Compared with the traditional algorithms, the recognition rate increased by 5.67%. Therefore, it provides a powerful theoretical basis for extracting subtle features of radiation source devices under complex electromagnetic environments.

https://www.mdpi.com/2079-9292/8/2/246/pdf

An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals

The article discusses the development of a system that provides more throughput up to 40 Gbit instead of 10 Gbit using the TWDM-PON (Time and Wavelength Division-Multiplexed Passive Optical Network) system, which was chosen by the international companies FSAN (Full-Service Access Network) and the ITU-T (Telecommunication Standardization Sector of the International Telecommunication Union) group. This system uses the time and frequency division of channels. In this article, this system is developed, and its operation is simulated at a distance of 40 km and with data transfer up to 40 Gbit/s. Also, the system was optimized and the optimal bandwidths for the optical and electrical filters were selected, as well as the optical power of the laser, etc.

The Fiber-Optic Communication System in the Enterprise

Prediction of congestion degree is critical for optical networks to cooperate with the network control strategies. Proposed in this paper is a method of congestion degree prediction for optical networks based on BP artificial neural network. The principles are presented, and the simulation results show that our proposed is competent for network congestion degree prediction.

Prediction of congestion degree for optical networks based on bp artificial neural network

It is a key requirement for any perimeter intrusion detection system to distinguish the intrusion from environmental events without reducing the efficiency. We implement and demonstrate a fiber optic perimeter detection system, which screens the event signal by its features in frequency domain. These features are extracted by method of principal component analysis (PCA) and then identified by support vector machine (SVM). Experimental results show that the combination of PCA and SVM improve evidently the anti-disturbance ability.

Fiber optic perimeter detection based on principal component analysis

In the fiber optic perimeter security system, which is based on Mach-Zehnder interferometer, the human intrusion and partial environmental noise can cause the fiber vibration. Distinguishing intrusion from environmental events without reducing the efficiency is a key requirement for any perimeter intrusion detection systems. In this paper, an signal classification system is presented for detection and recognition. This system compares event signal features, which are based on frequency domain and time domain respectively. Firstly, this research analyzes data extraction system based on human engineering. This model preprocesses time-domain data according to the characteristics of human behavior. After an event detection and feature extraction proce-dure, a classification algorithm applies K-means clustering method and the cosine similarity. In this method, the combination of signal in time and frequency domain is used to classify and improve the anti disturbance ability of the system and reduce the false alarm rate.

Fiber optic perimeter detection based on human engineering

We propose a traffic-aware load balancing (TALB) scheme of fronthaul network and establish a software-defined optical fronthaul network (SD-OFNet) testbed. The experimental results demonstrate that TALB scheme is able to improve network performance significantly.

Experimental Demonstration of Traffic-aware Load Balancing for Fronthaul Network

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Prediction of congestion degree for optical networks based on bp artificial neural network

Fiber optic perimeter detection based on principal component analysis

Fiber optic perimeter detection based on human engineering

Experimental Demonstration of Traffic-aware Load Balancing for Fronthaul Network