34 research outputs found
Dynamics Spectrum Sharing Environment Using Deep Learning Techniques
The recent fast expansion of mobile communication services has resulted in a scarcity of spectrum resources. The challenge of multidimensional resource allocation in cognitive radio systems is addressed in this work. Complicated and dynamic Spectrum Sharing SS systems might be vulnerable to a variety of possible security and privacy vulnerabilities, necessitating protection techniques that are adaptable, dependable, and scalable. Methods based on machine learning (ML) have repeatedly been proposed to overcome these challenges. We present a complete assessment of the current progress of ML-based SS approaches, the most crucial security challenges, and the accompanying protection mechanisms in this paper. We develop cutting-edge methodologies for improving the performance of SS communication systems in a variety of critical areas, such as ML-based cognitive radio networks (CRNs), ML-based database assisted SS networks, ML-based LTE-U networks, ML-based ambient backscatter networks, and other ML-based SS solutions. The results of the simulation trials show that the suggested strategy may successfully boost the user's incentive while reducing collisions. In terms of reward, the suggested strategy beats opportunistic multichannel ALOHA by around 10% and 30%, respectively, for the single SU and multi-SU scenarios. 
Satellite Communications
This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking
Performance analysis of cooperative diversity in land mobile satellite systems.
Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2013.Land Mobile Satellite Systems (LMSS) generally differ from other terrestrial wireless systems. The LMSS exhibit unique characteristics with regard to the physical layer, interference scenarios, channel impairements, propagation delay, link characteristics, service coverage, user and satellite mobility etc. Terrestrial wireless systems have employed the spatial diversity or MIMO (Multiple Input Multiple Output) technique in addressing the problem of providing uninterrupted service delivery to all mobile users especially in places where non-Line-of-Sight (NLoS) condition is prevalent (e.g. urban and suburban environments). For the LMSS, cooperative diversity has been proposed as a valuable alternative to the spatial diversity technique since it does not require the deployment of additional antennas in order to mitigate the fading effects. The basis of cooperative diversity is to have a group of mobile terminals sharing their antennas in order to generate a “virtual” multiple antenna, thus obtaining the same effects as the conventional MIMO system. However, the available cooperative diversity schemes as employed are based on outdated channel quality information (CQI) which is impracticable for LMSS due to its peculiar characteristics and its particularly long propagation delay. The key objective of this work is therefore to develop a cooperative diversity technology model which is most appropriate for LMSS and also adequately mitigates the outdated CQI challenge.
To achieve the objective, the feasibility of cooperative diversity for LMSS was first analyzed by employing an appropriate LMSS channel model. Then, a novel Predictive Relay Selection (PRS) cooperative diversity scheme for LMSS was developed which adequately captured the LMSS architecture. The PRS cooperative scheme developed employed prediction algorithms, namely linear prediction and pattern-matching prediction algorithms in determining the future CQI of the available relay terminals before choosing the most appropriate relay for cooperation. The performance of the PRS cooperative diversity scheme in terms of average output SNR, outage probability, average channel capacity and bit error probability were simulated, then numerically analyzed. The results of the PRS cooperative diversity model for LMSS developed not only showed the gains resulting from introducing cooperative techniques in satellite communications but also showed improvement over other cooperative techniques that based their relay selection cooperation on channels with outdated quality information (CQI). Finally, a comparison between the results obtained from the various predictive models considered was carried out and the best prediction model was recommended for the PRS cooperation
Machine Learning for Next-generation Content Delivery Networks: Deployment, Content Placement, and Performance Management
With the explosive demands for data and the growth in mobile users, content delivery networks (CDNs) are facing ever-increasing challenges to meet end-users quality-of-experience requirements, ensure scalability and remain cost-effective. These challenges encourage CDN providers to seek a solution by considering the new technologies available in today’s computer network domain. Network Function Virtualization (NFV) is a relatively new network service deployment technology used in computer networks. It can reduce capital and operational costs while yielding flexibility and scalability for network operators. Thanks to the NFV, the network functions that previously could be offered only by specific hardware appliances can now run as Virtualized Network Functions (VNF) on commodity servers or switches. Moreover, a network service can be flexibly deployed by a chain of VNFs, a structure known as the VNF Forwarding Graph or VNF-FG. Considering these advantages, the next-generation CDN will be deployed using NFV infrastructure. However, using NFV for service deployment is challenging as resource allocation in a shared infrastructure is not easy. Moreover, the integration of other paradigms (e.g., edge computing and vehicular network) into CDN will compound the complexity of content placement and performance management for the next-generation CDNs. In this regard, due to their impacts on final service and end-user perceived quality, the challenges in service deployment, content placement, and performance management should be addressed carefully. In this thesis, advanced machine learning methods are utilized to provide algorithmic solutions for the abovementioned challenges of the next generation CDNs.
Regarding the challenges in the deployment of the next-generation CDNs, we propose two deep reinforcement learning-based methods addressing the joint problems of VNF-FG’s composition and embedding, as well as function scaling and topology adaptation. As for content placement challenges, a deep reinforcement learning-based approach for content migration in an edge-based CDN with vehicular nodes is proposed. The proposed approach takes advantage of the available caching resources in the proximity of the full local caches and efficiently migrates contents at the edge of the network. Moreover, for managing the performance quality of an operating CDN, an unsupervised machine learning anomaly detection method is provided. The proposed method uses clustering to enable easier performance analysis for next-generation CDNs. Each proposed method in this thesis is evaluated by comparison to the state-of-the-art approaches. Moreover, when applicable, the optimality gaps of the proposed methods are investigated as well
Convergence vers IP des systèmes de télécommunication par satellite
Dans un contexte de convergence vers IP du monde des télécommunications, les systèmes de communication par satellite se doivent de suivre la tendance pour rester compétitifs et s'intégrer efficacement au monde Internet. Après avoir rappelé les enjeux d'une convergence dans les systèmes satellite et dressé un panorama des architectures de convergence envisageables, nous avons identifié les limites des systèmes actuels en termes de convergence vers IP. Notre choix se porte alors sur l'architecture IP/GSE pour la voie aller. Nous spécifions ensuite le protocole d'encapsulation GSE-Alt, inspiré de GSE mais adapté à la voie retour. Le déploiement de nouveaux services et l'évolution de services existants sont assurés et rendus plus aisés grâce à la couche IP. Les couches GSE et GSE-Alt optimisent le transport d'IP. Pour offrir un support de communication répondant à la diversité des exigences de qualité des services applicatif, nous définissons ensuite plusieurs mécanismes autorisant la mise en cohérence du traitement de la qualité de service (QoS) aux différents niveaux protocolaires dans les systèmes de communication par satellite. Enfin, pour permettre une interconnexion et une intégration du monde satellite au monde Internet, nous étudions les besoins en termes de déploiement du routage IP. Nous définissons alors une architecture permettant au satellite de réaliser de la commutation de niveau IP. Cette convergence vers un système « tout IP » du segment de communication par satellite est le fondement nécessaire à son insertion transparente au reste du monde des télécommunications. ABSTRACT : The world of telecommunications converging towards IP, the telecommunication satellite systems have to follow the trend to stay competitive and to be integrated to the Internet world. We first remind the issues of convergence in satellite communications, then we list the different convergence architectures conceivable in satellite systems and describe the limits of current systems in term of IP convergence. Our choice is devoted to the IP/GSE architecture for the forward link. Then, we specify the GSE-Alt protocol, inspired from GSE but adapted to the return link. The deployment of new services and the evolution of existing services are possible and made easier thanks to the IP layer. Both layers GSE and GSE-Alt optimize the transport of the IP packets. In order to propose a communication support allowing various quality of service (QoS) needs, we specify several mechanisms allowing a great coherence of the quality of service treatments at the different protocol levels. Finally, to allow an interconnection and an integration of the satellite world to the Internet world, we study the requirements in term of IP routing deployment. Therefore, we specify an architecture allowing the satellite to make the switching at the IP level. This convergence of the satellite towards an "all IP" system is the base required to its transparent insertion to the rest of the telecommunication world
Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)
Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression
Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking
The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out
User mobility prediction and management using machine learning
The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML).
The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility
Disruptive Technologies with Applications in Airline & Marine and Defense Industries
Disruptive Technologies With Applications in Airline, Marine, Defense Industries is our fifth textbook in a series covering the world of Unmanned Vehicle Systems Applications & Operations On Air, Sea, and Land. The authors have expanded their purview beyond UAS / CUAS / UUV systems that we have written extensively about in our previous four textbooks. Our new title shows our concern for the emergence of Disruptive Technologies and how they apply to the Airline, Marine and Defense industries. Emerging technologies are technologies whose development, practical applications, or both are still largely unrealized, such that they are figuratively emerging into prominence from a background of nonexistence or obscurity. A Disruptive technology is one that displaces an established technology and shakes up the industry or a ground-breaking product that creates a completely new industry.That is what our book is about. The authors think we have found technology trends that will replace the status quo or disrupt the conventional technology paradigms.The authors have collaborated to write some explosive chapters in Book 5:Advances in Automation & Human Machine Interface; Social Media as a Battleground in Information Warfare (IW); Robust cyber-security alterative / replacement for the popular Blockchain Algorithm and a clean solution for Ransomware; Advanced sensor technologies that are used by UUVs for munitions characterization, assessment, and classification and counter hostile use of UUVs against U.S. capital assets in the South China Seas. Challenged the status quo and debunked the climate change fraud with verifiable facts; Explodes our minds with nightmare technologies that if they come to fruition may do more harm than good; Propulsion and Fuels: Disruptive Technologies for Submersible Craft Including UUVs; Challenge the ammunition industry by grassroots use of recycled metals; Changing landscape of UAS regulations and drone privacy; and finally, Detailing Bioterrorism Risks, Biodefense, Biological Threat Agents, and the need for advanced sensors to detect these attacks.https://newprairiepress.org/ebooks/1038/thumbnail.jp
Bioinspired metaheuristic algorithms for global optimization
This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions