From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo-location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo-location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data-rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an all-inclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques

Location Privacy-Preserving Strategies for Secondary Spectrum Use

The scarcity of wireless spectrum resources and the overwhelming demand for wireless broadband resources have prompted industry, government agencies and academia within the wireless communities to develop and come up with effective solutions that can make additional spectrum available for broadband data. As part of these ongoing efforts, cognitive radio networks (CRNs) have emerged as an essential technology for enabling and promoting dynamic spectrum access and sharing, a paradigm primarily aimed at addressing the spectrum scarcity and shortage challenges by permitting and enabling unlicensed or secondary users (SUs) to freely search, locate and exploit unused licensed spectrum opportunities. Despite their great potentials for improving spectrum utilization efficiency and for addressing the spectrum shortage problem, CRNs suffer from serious location privacy issues, which essentially tend to disclose the location information of the SUs to other system entities during their usage of these open spectrum opportunities. Knowing that their whereabouts may be exposed, SUs can be discouraged from joining and participating in the CRNs, potentially hindering the adoption and deployment of this technology. In this thesis, we propose frameworks that are suitable for CRNs, but also preserve the location privacy information of these SU s. More specifically, 1. We propose location privacy-preserving protocols that protect the location privacy of SUs in cooperative sensing-based CRNs while allowing the SUs to perform their spectrum sensing tasks reliably and effectively. Our proposed protocols allow also the detection of malicious user activities through the adoption of reputation mechanisms. 2. We propose location privacy-preserving approaches that provide information-theoretic privacy to SU s’ location in database-driven CRNs through the exploitation of the structured nature of spectrum databases and the fact that database-driven CRNs, by design, rely on multiple spectrum databases. 3. We propose a trustworthy framework for new generation of spectrum access systems in the 3.5 GHz band that not only protects SUs’ privacy, but also ensures that they comply with the unique system requirements, while allowing the detection of misbehaving users

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios

Un cadre inter-couches pour la protection contre les interférences dans les réseaux ad-hoc radio cognitive

A fixed spectrum assignment scheme has a problem with resource deficiency in a wireless network. In 2002, the US Federal Communication Commission (FCC) reported that the radio spectrum was 20% to 85% under-utilized. The insufficient use of the spectrum is a critical issue for radio communication; as communication grows, a fixed spectrum becomes more limiting. The FCC then changed its spectrum management policy to make it more flexible by investigating the cognitive radio (CR) approach. Cognitive radio is a type of intelligent radio that explores the radio frequency environment, learns, and decides to use the unused portion of the frequency. The main functions of a CR are sensing, decision making, and sharing. However, these radios have to respect the standard wireless infrastructures by ensuring the least impact with their devices, also known as primary radios. Coexistence between CR systems and primary systems requires dedicated observation processes and interference management. In this thesis, observation from a CR point of view is presented. The overlapping area between a CR transmitter and primary radio (PR) transmitter is analysed so that it can be taken into account. The impact of this area is learnt by simulation and presented in Chapter 4. As a consequence, potential interference is envisaged. Along with observation, we investigate a proper mechanism to better prevent perturbation on PR devices using the Grey model and Kalman filter as a prediction model for predicting the density of primary receivers. In addition, we provide a strategy to combine the obtained observations into a metric that can be used in routing design in the context of coexistence between Cognitive Radio Networks (CRNs) and primary networks. The proposed strategy, using fuzzy logic, is presented in Chapter 5. In this chapter, we investigate how the routing layer reacts and makes the right decisions to maximise the spectrum resources, while avoiding interference with the primary receivers. For instance, a CR node can operate in an overlap region if primary receivers are inactive within this area. Also, we propose a routing mechanism based on the DYMO routing protocol that takes into account the observed relative impact. In the same chapter, we provide some practical scenarios illustrating the usefulness of our proposal. Interconnecting the CR nodes in CRNs is also a critical problem for the establishment of the network. We therefore present a beacon-based dissemination process in Chapter 6. In this chapter, we also describe a practical device designed for cognitive radio experiments. Even though our work affects different protocol layers, the designed framework is cross-layered. Indeed, the different components of the proposed framework access the various layers to retrieve information, process it, and react accordingly. Thus, our work constitutes a cross-layer framework for a local cognitive radio that aims to minimise the interference and maximise the network resources in cognitive radio networks.Le plan d’attribution du spectre présente un problème de déficit de ressources dans les réseaux sans fil. En 2002, la FCC (Federal Communication Commission) a rapporté que le spectre radioélectrique était de 20% à 85% sous-utilisé. L’utilisation inefficace du spectre est un problème majeur qui doit être résolu si l’on veut que les communications radio se développent. La FCC a ensuite changé la politique de gestion du spectre pour la rendre plus souple en s’interessant à l’approche radio cognitive (CR). La radio cognitive est un type de radio intelligente qui explore l’environnement de fréquences radio, apprend et décide d’utiliser la partie inutilisée du spectre. Les principales fonctions de la CR sont la détection, la prise de décision, et le partage. Cependant, ces radios doivent respecter les infrastructures sans fil standards en minimisant leur impact sur les appareils prioritaires, également appelés systèmes primaires. La coexistence entre les systèmes CR et les systèmes primaires nécessite des processus d’observation et de gestion des interférences dédiés. Dans cette thèse, nous nous sommes intéressés à la phase d’observation du point de vue CR. La zone de chevauchement entre un émetteur CR et l’émetteur primaire (PR) est analysée et prise en compte. L’impact de cette zone est appris par simulation et présenté dans le chapitre 4. En conséquence, des interférences potentielles sont envisagées. Durant la phase d’observation, nous étudions un mécanisme permettant de mieux prévenir la perturbation sur les dispositifs PR en utilisant le Grey Model et le filtre de Kalman comme modèle de prédiction de la densité des récepteurs primaires. En complément à cette observation, nous fournissons une stratégie visant à combiner les observations obtenues en une mesure qui pourra être utilisée par le routage dans le cadre de la coexistence entre réseaux radio cognitive (CRN) et réseaux primaires. La stratégie proposée utilise la logique floue et est présentée dans le chapitre 5. Dans ce chapitre, nous étudions comment la couche réseau réagit et prend les bonnes décisions pour maximiser l’utilisation des ressources du spectre, tout en évitant les interférences avec les récepteurs primaires. Par exemple, un noeud CR peut fonctionner dans une zone de recouvrement, si les récepteurs primaires sont inactifs dans cette zone. Ainsi, nous avons proposé un mécanisme de routage basé sur le protocole de routage DYMO qui prend en compte l’impact relatif observé. Dans ce même chapitre, nous avons également présenté des scénarios pratiques illustrant l’utilité de notre proposition. L’interconnexion des noeuds CR dans le CRN est aussi un problème crucial pour la mise en place du réseau. C’est pourquoi nous présentons un processus de diffusion par balises au chapitre 6. Dans ce chapitre, nous décrivons également un dispositif pratique conçu pour des expériences en radio cognitive. Même si notre travail se rapporte à différentes couches de la pile protocolaire, le cadre général que nous avons conçu est multicouches. En effet, les composants accèdent aux différentes couches pour récupérer l’information, la traiter et réagir en conséquence. Ainsi, notre travail constitue un environnement inter-couches pour un dispositif radio cognitive local visant à minimiser les interférences et à maximiser les ressources réseau dans les réseaux radio cognitive