Application of Cognitive Radio in the Context of Railways : Blind Modulation Schemes Recognition over High-Speed Channels

Un système de transport ferroviaire intelligent est essentiellement caractérisé par son niveau d'autonomie de prise de décision en fonction des conditions qui lui sont extérieurs. Afin de renforcer son intelligence et son autonomie, cette nouvelle génération de systèmes de transport intègre des multiples technologies et standards de communication et de traitement de l'information. L'intégration de ces technologies permet aux opérateurs du transport ferroviaire de réduire les coûts d'exploitation et de maintenance et d'attirer un plus grand nombre de passagers en leur facilitant l'accès ainsi que l'exploitation du transport ferroviaire tout en leur offrant des nouveaux services à bord. Cependant l'utilisation de plusieurs standards de communication ainsi que l'augmentation du trafic (le nombre de passagers et le nombre de véhicules mis en service) déclenchent un besoin sans précédent des ressources radio, notamment au niveau du spectre fréquentiel. En effet, avec la demande croissante des ressources radio, la Radio Intelligente (RI) se présente comme une technologie émergente qui améliore les performances des systèmes radio existants en intégrant l'intelligence artificielle avec la radio logicielle.Any intelligent railway transport system is mainly characterized by its autonomy in making decisions in terms of its external conditions. In order to improve its cognition and autonomy, this new generation of transport systems integrates multiple technologies and standards of communication and information processing. The integration of these technologies allows rail operators to reduce operational and maintenance costs and attracts more passengers by making easier rail transport access and use while offering new services on board. However, using multiple communication standards and increasing traffic (number of passengers and vehicles in service) trigger an unprecedented need for radio resources, particularly frequency spectrum. Indeed, with the growing of radio resources demand, Cognitive Radio (CR) is an emerging technology that improves the performance of existing radio systems by the integration of artificial intelligence and software defined radio (SDR)

Novel medical image encryption scheme based on chaos a DNA encoding

In this paper, we propose a new chaos-based encryption scheme for medical images. It is based on a combination of chaos and DNA computing under the scenario of two encryption rounds, preceded by a key generation layer, and follows the permutation-substitution-diffusion structure. The SHA-256 hash function alongside the initial secret keys is employed to produce the secret keys of the chaotic systems. Each round of the proposed algorithm involves six steps, i.e., block-based permutation, pixel-based substitution, DNA encoding, bit-level substitution (i.e., DNA complementing), DNA decoding, and bit-level diffusion. A thorough search of the relevant literature yielded only this time the pixel-based substitution and the bit-level substitution are used in cascade for image encryption. The key-streams in the bit-level substitution are based on the logistic-Chebyshev map, while the sine-Chebyshev map allows producing the key-streams in the bit-level diffusion. The final encrypted image is obtained by repeating once the previous steps using new secret keys. Security analyses and computer simulations both confirm that the proposed scheme is robust enough against all kinds of attacks. Its low complexity indicates its high potential for real-time and secure image applications

Denoising higher-order moments for blind digital modulation identification in multiple-antenna systems

This letter proposes a new technique that substantially improves blind digital modulation identification (DMI) algorithms that are based on higher-order statistics (HOS). The proposed technique takes advantage of noise power estimation to make an offset on higher-order moments (HOM), thus getting an estimate of noise-free HOM. When tested for multiple-antenna systems, the proposed method outperforms other DMI algorithms, in terms of identification accuracy, that are based only on cumulants or do not consider HOM denoising, even for a receiver with impairments. The improvement is achieved with the same order of complexity of the common HOS-based DMI algorithms in the same context

Blind digital modulation identification for time-selective MIMO channels

This paper addresses the problem of blind digital modulation identification in time-selective Multiple-Input Multiple-Output channels. Our objective is to recognize modulation schemes in highly-mobile communication environments, for military or high-speed railway applications, without signal knowledge or Channel State Information at the receiver. The proposed identification process is based on Blind Source Separation (BSS) and feature classification. We introduce a sliding window technique for the BSS of a faded-mixture to overcome the effect of the high mobility. Then, to improve the recognition of modulation schemes, we adopt a specific multi Artificial- Neural-Network (ANN) classifier, where each ANN is trained to be used within a particular Signal-to-Noise Ratio range. The proposed identifier has a good probability for achieving correct identifications under high velocity for typical carrier frequency and bandwidth

Chaotic Dingo Optimization Algorithm: Application in Feature Selection for Beamforming Aided Spectrum Sensing

International audienceSpectrum sensing based on Beamforming, like others classification problem, require feature selection to perform learning algorithms and enhance the classification task. This paper proposes a novel version of the Dingo Optimization Algorithm (DOA) to optimize feature selection for a Deep Neural Network (DNN) classifier. Two improvements are introduced to avoid the premature convergence problem and stagnation in the local optima of the original DOA. First, the chaos strategy is executed to produce a high level of diversification in the algorithm, which improves its ability to escape from potential local optimums. Second, the weight factor is introduced to boot up the search process to the global optima. Here, the aim is to improve the DOA for feature selection in the deep learning approach in order to enhance the performance of blind spectrum sensing based on Beamforming in the context of cognitive radio (CR). Through simulations results, we illustrate that our algorithm, called Chaotic Dingo Optimization Algorithm (CDOA), outperforms the original one and a set of state-of-the-art optimization algorithms (i.e., HS, BBO, PSO, and SA) for feature selection in the learning approach

Smart Full-Exploitation of Beamforming Fusion assisted Spectrum Sensing for Cognitive Radio

International audienceThis paper proposes blind spectrum sensing (SS) in a narrowband context called Beamforming Fusion assisted Spectrum Sensing (BFSS). Considering a channel with angles of arrival (AoA), we jointly exploit beamforming algorithms to make decisions about the detection of users on frequency resources. The proposed method is totally blind and does not require knowledge of the noise power, the channel estimation, and the source signal. A state-of-the-art comparison of SS methods using beamforming is provided to validate our contribution in a shallow SNR region

Fourth order MCA and chaos-based image encryption scheme

This paper presents a fast and efficient cryptosystem for enciphering digital images. It employs two of the most prominent dynamical systems-chaotic maps and cellular automata. The key streams in the proposed encryption scheme are derived from the SHA-256 hash function. Hash functions produce the digest of the input plaintext, known as a hash value, which can be considered as a unique signature of the input. This makes the keys more plaintext dependent, which is a desirable property of a robust cryptosystem. These key streams are used as the secret keys (i.e., initial conditions and control parameters) of an improved one-dimensional (1-D) chaotic map, i.e., the Logistic-Sine map. As far as we know, this paper is a first that combines the well-known diffusion-confusion architecture and the fourth order 1-D memory cellular automata (MCA) for image encryption. First, a pixel-wise XOR operation is applied to the original image, followed by a pixel-wise random permutation. The resulting image is decomposed into four blocks according to the quadtree decomposition strategy. Then, a fourth order reversible MCA is applied, the blocks obtained from the quadtree decomposition are considered as the initial MCA configurations, and the transition rules are determined using the chaotic map. The performance analyses show that the proposed encryption scheme presents a high immunity against all kind of attacks while maintaining a low complexity, which outcome a notably better performance/complexity trade-off compared to some recently proposed image schemes

A hybrid Modified Black Widow Optimization and PSO Algorithm: Application in Feature Selection for Cognitive Radio Networks

International audienceIn spectrum sensing issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature selection. This paper proposes a new hybrid optimization algorithm to optimize feature selection for a Deep Neural Network (DNN) classifier. To surpass the premature convergence problem and improve the exploitation ability of the original Black Widow Optimization Algorithm (BWO), we mix a modified version of BWO and Particle Swarm Optimization (PSO), called MBWPSO. The aim is to enhance the performance of a blind spectrum sensing approach in the context of cognitive radio (CR) for wireless communications. Computer simulations show that the MBWPSO algorithm outperforms the original one and a set of state-of-the-art algorithms (i.e., HS, BBO, PSO, and SA) algorithms. The MBWPSO also exhibits the best performance once applied for feature selection in the above contex