Cognitive radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, and regulatory and standardization bodies have put their significant efforts toward the realization of CR technology. However, as this technology adapts its transmission based on the surrounding radio environment, several practical issues may need to be considered. In practice, several imperfections, such as noise uncertainty, channel/interference uncertainty, transceiver hardware imperfections, signal uncertainty, and synchronization issues, may severely deteriorate the performance of a CR system. To this end, the investigation of realistic solutions toward combating various practical imperfections is very important for the successful implementation of cognitive technology. In this direction, first, this survey paper provides an overview of the enabling techniques for CR communications. Subsequently, it discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches toward addressing these imperfections. Finally, it provides some interesting open research issues.

/pdf/cognitive-radio-techniques-under-practical-imperfections-a-4ilvc1h7jn.pdf

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Automatic modulation classification (AMC) is an promising technology for non-cooperative communication systems in both military and civilian scenarios. Recently, deep learning (DL) based AMC methods have been proposed with outstanding performances. However, both high computing cost and large model sizes are the biggest hinders for deployment of the conventional DL based methods, particularly in the application of internet-of-things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. In this correspondence, a novel DL based lightweight AMC (LightAMC) method is proposed with smaller model sizes and faster computational speed. We first introduce a scaling factor for each neuron in convolutional neural network (CNN) and enforce scaling factors sparsity via compressive sensing. It can give an assist to screen out redundant neurons and then these neurons are pruned. Experimental results show that the proposed LightAMC method can effectively reduce model sizes and accelerate computation with the slight performance loss.

LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing

Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.

https://www.mdpi.com/1424-8220/17/6/1457/pdf

Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

With growing interest in using cognitive radio (CR) technology in wireless communication systems for vehicles, it is envisioned that future vehicles will be CR-enabled. This paper discusses CR technologies for vehicular networks aimed at improving vehicular communication efficiency. CR for vehicular networks has the potential of becoming a killer CR application in the future due to a huge consumer market for vehicular communications. This paper surveys novel approaches and discusses research challenges related to the use of cognitive radio technology in vehicular ad hoc networks. We review how CR technologies such as dynamic spectrum access, adaptive software-defined radios, and cooperative communications will enhance vehicular communications and, hence, present the potential of transforming vehicle communication in terms of efficiency and safety. Our work is different from existing works in that we provide recent advances and open research directions on applying cognitive radio in vehicular ad hoc networks (CR-VANETs) focusing on architecture, machine learning, cooperation, reprogrammability, and spectrum management as well as QoE optimization for infotainment applications. A taxonomy of recent advances in cognitive radio for vehicular networks is also provided. In addition, several challenges and requirements have been identified. The research on applying CR in vehicular networks is still in its early stage, and there are not many experimental platforms due to their complex setup and requirements. Some related testbeds and research projects are provided at the end.

/pdf/cognitive-radio-for-vehicular-ad-hoc-networks-cr-vanets-452puv8xq9.pdf

Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges

This paper presents an overview of feature-based (FB) methods developed for Automatic classification of digital modulations. Only the most well-known features and classifiers are considered, categorized, and defined. The features include instantaneous time domain (ITD) parameters, Fourier transform (FT), wavelet transform (WT), higher order moments (HOM) to name a few. The classifiers are artificial neural networks (ANN), support vector machines (SVMs), and decision tree (DT). We also highlight the advantages and disadvantages of each technique in classifying a certain modulation scheme. The objective of this work is to assist newcomers to the field to choose suitable algorithms for intended applications. Furthermore, this work is expected to help in determining the limitations associated with the available FB automatic modulation classification (AMC) methods.

An overview of feature-based methods for digital modulation classification

Modulation type is one of the most important characteristics used in signal waveform identification and classification. Spatial correlation is a crucial factor for practical multiple-input multiple-output (MIMO) systems. This paper addresses the problem of blind digital modulation identification in spatially-correlated MIMO systems. The proposed algorithm is verified using higher order statistical moments and cumulants of the received signal. The purpose is to discriminate among different M-ary shift keying linear modulation schemes without any priori signal information. This study employs several MIMO techniques to identify the modulation with and without channel state information (CSI). The proposed classifier shows a high identification performance in acceptable signal-to-noise ratio (SNR) range.

/pdf/blind-digital-modulation-identification-for-spatially-483rxauz68.pdf

Blind Digital Modulation Identification for Spatially-Correlated MIMO Systems

Modulation type is one of the most important characteristics used in signal waveform identification. In this paper, an algorithm for automatic digital modulation recognition is proposed. The proposed algorithm is verified using higher-order statistical moments (HOM) of continuous wavelet transform (CWT) as a features set. A multilayer feed-forward neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying modulation schemes and themodulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis is used to reduce the network complexity and to improve the classifier's performance. The proposed algorithm is evaluated through confusion matrix and false recognition probability. The proposed classifier is shown to be capable of recognizing the modulation scheme with high accuracy over wide signal-to-noise ratio (SNR) range over both additive white Gaussian noise (AWGN) and different fading channels.

/pdf/automatic-modulation-recognition-using-wavelet-transform-and-1f1c7olipf.pdf

Automatic modulation recognition using wavelet transform and neural networks in wireless systems

Blind Digital Modulation Identification forSpatially-Correlated MIMO Systems

Here, a new spectrum sensing method, called mean-to-square extreme eigenvalue (MSEE), is proposed. Considering a multiple antenna communication system, the proposal is drawn from the arithmetic-to-geometric mean (AGM) algorithm using only the smallest and the largest eigenvalues of the covariance matrix of the received signal. The aim of MSEE is to avoid the heavy computational costs of AGM method. First, based on the random matrix theory, a theoretical development to set the threshold of the proposal is provided. Then, the validity of the expression is verified by simulations. Finally, simulation results show an interesting performance of MSEE compared with several spectrum sensing methods in the literature.

Blind Spectrum Sensing Using Extreme Eigenvalues for Cognitive Radio Networks

Cognitive radio (CR) was proposed as a solution to the spectrum scarcity problem. One of the basic functions of any CR is spectrum sensing. Most existing works on spectrum sensing consider the Gaussian noise assumption. In practice, this assumption is not always valid since several existing noise types exhibit non-Gaussian and impulsive behavior. Hence, it is very beneficial to study spectrum sensing in the presence of impulsive noise. In this paper, we propose two new multiple-antenna-based spectrum sensing methods, assuming that the underlying noise follows a symmetric $\alpha$ -stable distribution. This assumption is justified by a distribution fitting of some measurements of the noise acting on the GSM-R antennas onboard trains. The first proposed sensing method is based on the covariation properties of $\alpha$ -stable processes, whereas the second proposed method has the strategy of filtering the corrupted signals before applying a traditional spectrum sensing method. These two methods do not require a priori knowledge about the primary-user signal. Simulation results show that the proposed algorithms provide good spectrum sensing performance in the presence of $\alpha$ -stable distributed impulsive noise.

Kais Hassan

Papers

Blind Digital Modulation Identification for Spatially-Correlated MIMO Systems

Automatic modulation recognition using wavelet transform and neural networks in wireless systems

Blind Digital Modulation Identification forSpatially-Correlated MIMO Systems

Blind Spectrum Sensing Using Extreme Eigenvalues for Cognitive Radio Networks

Multiple-Antenna-Based Blind Spectrum Sensing in the Presence of Impulsive Noise