405 research outputs found

    WIMAX Basics from PHY Layer to Scheduling and Multicasting Approaches

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    WiMAX (Worldwide Interoperability for Microwave Access) is an emerging broadband wireless technology for providing Last mile solutions for supporting higher bandwidth and multiple service classes with various quality of service requirement. The unique architecture of the WiMAX MAC and PHY layers that uses OFDMA to allocate multiple channels with different modulation schema and multiple time slots for each channel allows better adaptation of heterogeneous user’s requirements. The main architecture in WiMAX uses PMP (Point to Multipoint), Mesh mode or the new MMR (Mobile Multi hop Mode) deployments where scheduling and multicasting have different approaches. In PMP SS (Subscriber Station) connects directly to BS (Base Station) in a single hop route so channel conditions adaptations and supporting QoS for classes of services is the key points in scheduling, admission control or multicasting, while in Mesh networks SS connects to other SS Stations or to the BS in a multi hop routes, the MMR mode extends the PMP mode in which the SS connects to either a relay station (RS) or to Bs. Both MMR and Mesh uses centralized or distributed scheduling with multicasting schemas based on scheduling trees for routing. In this paper a broad study is conducted About WiMAX technology PMP and Mesh deployments from main physical layers features with differentiation of MAC layer features to scheduling and multicasting approaches in both modes of operations

    Analysis, evaluation and improvement of RT-WMP for real-time and QoS wireless communication: Applications in confined environments

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    En los ultimos años, la innovación tecnológica, la característica de flexibilidad y el rápido despligue de las redes inalámbricas, han favorecido la difusión de la redes móviles ad-hoc (MANETs), capaces de ofrecer servicios para tareas específicas entre nodos móviles. Los aspectos relacionados al dinamismo de la topología móvil y el acceso a un medio compartido por naturaleza hacen que sea preciso enfrentarse a clases de problemas distintos de los relacionados con la redes cableadas, atrayendo de este modo el interés de la comunidad científica. Las redes ad-hoc suelen soportar tráfico con garantía de servicio mínimo y la mayor parte de las propuestas presentes en literatura tratan de dar garantías de ancho de banda o minimizar el retardo de los mensajes. Sin embargo hay situaciones en las que estas garantías no son suficientes. Este es el caso de los sistemas que requieren garantías mas fuertes en la entrega de los mensajes, como es el caso de los sistemas de tiempo real donde la pérdida o el retraso de un sólo mensaje puede provocar problemas graves. Otras aplicaciones como la videoconferencia, cada vez más extendidas, implican un tráfico de datos con requisitos diferentes, como la calidad de servicio (QoS). Los requisitos de tiempo real y de QoS añaden nuevos retos al ya exigente servicio de comunicación inalámbrica entre estaciones móviles de una MANET. Además, hay aplicaciones en las que hay que tener en cuenta algo más que el simple encaminamiento de los mensajes. Este es el caso de aplicaciones en entornos subterráneos, donde el conocimiento de la evolución de propagación de la señal entre los diferentes nodos puede ser útil para mejorar la calidad de servicio y mantener la conectividad en cada momento. A pesar de ésto, dentro del amplio abanicos de propuestas presente en la literatura, existen un conjunto de limitaciones que van de el mero uso de protocolos simulados a propuestas que no tienen en cuenta entornos no convencionales o que resultan aisladas desde el punto de vista de la integración en sistemas complejos. En esta tesis doctoral, se propone un estudio completo sobre un plataforma inalámbrica de tiempo real, utilizando el protocolo RT-WMP capaz de gestionar trafíco multimedia al mismo tiempo y adaptado al entorno de trabajo. Se propone una extensión para el soporte a los datos con calidad de servicio sin limitar las caractaristícas temporales del protocolo básico. Y con el fin de tener en cuenta el efecto de la propagación de la señal, se caracteriza el entorno por medio de un conjunto de restricciones de conectividad. La solución ha sido desarrollada y su validez ha sido demostrada extensamente en aplicaciones reales en entornos subterráneos, en redes malladas y aplicaciones robóticas

    Machine Learning Approach for Spectrum Sharing in the Next Generation Cognitive Mesh Network

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    Nowadays, there is an unexpected explosion in the demand for wireless network resources. This is due to the dramatic increase in the number of the emerging web-based services. For wireless computer network, limited bandwidth along with the transmission quality requirements for users, make quality of service (QoS) provisioning a very challenging problem. To overcome spectrum scarcity problem, Federal Communications Commission (FCC) has already started working on the concept of spectrum sharing where unlicensed users (secondary users, SUs) can share the spectrum with licensed users (primary users, PUs), provided they respect PUs rights to use spectrum exclusively. Cognitive technology presents a revolutionary wireless communication where users can exploit the spectrum dynamically. The integration of cognitive technology capability into the conventional wireless networks is perhaps the significant promising architectural upgrade in the next generation of wireless network that helps to solve spectrum scarcity problem. In this work, we propose integrating cognitive technology with wireless mesh network to serve the maximum number of SUs by utilizing the limited bandwidth efficiently. The architecture for this network is selected first. In particular, we introduce the cluster-based architecture, signaling protocols, spectrum management scheme and detailed algorithms for the cognitive cycle. This new architecture is shown to be promising for the cognitive network. In order to manage the transmission power for the SUs in the cognitive wireless mesh network, a dynamic power management framework is developed based on machine learning to improve spectrum utilization while satisfying users requirements. Reinforcement learning (RL) is used to extract the optimal control policy that allocates spectrum and transmission powers for the SUs dynamically. RL is used to help users to adapt their resources to the changing network conditions. RL model considers the spectrum request arrival rate of the SUs, the interference constraint for the PUs, the physical properties of the channel that is selected for the SUs, PUs activities, and the QoS for SUs. In our work, PUs trade the unused spectrum to the SUs. For this sharing paradigm, maximizing the revenue is the key objective of the PUs, while that of the SUs is to meet their requirements and obtain service from the rented spectrum. However, PUs have to maintain their QoS when trading their spectrum. These complex conflicting objectives are embedded in our machine learning model. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. We use a machine learning to help the PUs to make a decision about the optimal size and price of the offered spectrum for trading. The trading model considers the QoS for PUs and SUs, traffic load at the PUs, the changes in the network conditions, and the revenues of the PUs. Finally, we integrate all the mechanisms described above to build a new cognitive network where users can interact intelligently with network conditions

    Packet Scheduling Algorithms in LTE/LTE-A cellular Networks: Multi-agent Q-learning Approach

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    Spectrum utilization is vital for mobile operators. It ensures an efficient use of spectrum bands, especially when obtaining their license is highly expensive. Long Term Evolution (LTE), and LTE-Advanced (LTE-A) spectrum bands license were auctioned by the Federal Communication Commission (FCC) to mobile operators with hundreds of millions of dollars. In the first part of this dissertation, we study, analyze, and compare the QoS performance of QoS-aware/Channel-aware packet scheduling algorithms while using CA over LTE, and LTE-A heterogeneous cellular networks. This included a detailed study of the LTE/LTE-A cellular network and its features, and the modification of an open source LTE simulator in order to perform these QoS performance tests. In the second part of this dissertation, we aim to solve spectrum underutilization by proposing, implementing, and testing two novel multi-agent Q-learning-based packet scheduling algorithms for LTE cellular network. The Collaborative Competitive scheduling algorithm, and the Competitive Competitive scheduling algorithm. These algorithms schedule licensed users over the available radio resources and un-licensed users over spectrum holes. In conclusion, our results show that the spectrum band could be utilized by deploying efficient packet scheduling algorithms for licensed users, and can be further utilized by allowing unlicensed users to be scheduled on spectrum holes whenever they occur

    Optimal spectrum utilisation in cognitive network using combined spectrum sharing approach: overlay, underlay and trading

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    Cognitive radio technology enables unlicensed users (secondary users, SUs) to access the unused spectrum. In the literature, there are three spectrum sharing paradigms that enable SUs to access the licensed spectrum. These access techniques include underlay, overlay and spectrum trading, and have their own drawbacks. To combat these drawbacks, we propose a new approach for each of them and merge them into one combined system. Our overlay scheme provides quick access to the unused spectrum. We propose a new cooperative sensing protocol to reduce the likelihood of interfering with PUs. In order to enable SUs for transmitting simultaneously with PUs, we suggest using our underlay scheme. Our trading scheme allows PUs to trade the unused spectrum for the SUs that require better quality of service. The new combined scheme increases the size of spectrum in the cognitive network. Simulation results show the ability of the new scheme to serve extra traffic

    A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

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    This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

    Optimization and Learning in Energy Efficient Cognitive Radio System

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    Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network
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