Dans les réseaux d'accès sans fil, l'un des défis les plus récents est la réduction de la consommation d'énergie du réseau, tout en préservant la qualité de service perçue par les utilisateurs finaux. Cette thèse propose des solutions à ce problème difficile considérant deux objectifs, l'économie d'énergie et la minimisation du délai de transmission. Comme ces objectifs sont contradictoires, un compromis devient inévitable. Par conséquent, nous formulons un problème d’optimisation multi-objectif dont le but est la minimisation conjointe de la puissance consommée et du délai de transmission dans les réseaux sans-fil. La minimisation de la puissance est réalisée en ajustant le mode de fonctionnement des stations de base (BS) du réseau d’un niveau élevé de puissance d’émission vers un niveau d'émission plus faible ou même en mode veille. La minimisation du délai de transmission est réalisée par le meilleur rattachement des utilisateurs avec les BS du réseau. Nous couvrons deux réseaux sans-fil différents en raison de leur pertinence : les réseaux locaux sans-fil (IEEE 802.11 WLAN) et les réseaux cellulaires dotés de la technologie LTE. / In wireless access networks, one of the most recent challenges is reducing the power consumption of the network, while preserving the quality of service perceived by the end users. The present thesis provides solutions to this challenging problem considering two objectives, namely, saving power and minimizing the transmission delay. Since these objectives are conflicting, a tradeoff becomes inevitable. Therefore, we formulate a multi-objective optimization problem with aims of minimizing the network power consumption and transmission delay. Power saving is achieved by adjusting the operation mode of the network Base Stations (BSs) from high transmit power levels to low transmit levels or even sleep mode. Minimizing the transmission delay is achieved by selecting the best user association with the network BSs. We cover two different wireless networks, namely IEEE 802.11 wireless local area networks and LTE cellular networks.

L'explosion de demande de bande passante est une conséquence de l'augmentation du volume de trafic. Il est important de proposer des mécanismes pour transférer le trafic entre les réseaux interconnectés de manière efficace. D'autre part, il est prévu que les réseaux d'accès (optiques et mobiles) constituent les plus grands consommateurs d'énergie dans les réseaux optiques pour les dix prochaines années. Cette situation et l'impact croissant des réseaux sur l'environnement ont fait devenir l'efficacité énergétique dans les réseaux de télécommunications un thème important de recherche. Cette thèse se concentre donc sur la proposition de nouvelles solutions aux problèmes liées à l'augmentation du volume de trafic dans différentes segments des réseaux. Tout d'abord, nous avons étudié différents schèmes de transfert du trafic entre les réseaux interconnectes en utilisant la synchronisation. Puis, nous avons exploré la possibilité d'offrir différents services dans les réseaux intégrés optiques-mobiles. Nous avons présenté une nouvelle architecture pour la conception de l'unité de réseau optique (ONU). Ensuite, nous nous sommes focalisés sur l'économie de l'énergie et des solutions efficaces pour l'allocation de bande passante ont été proposées. Nous avons également proposé un algorithme qui fournit l'efficacité énergétique pour les récepteurs sans fil dans les ONUs hybride. Une analyse des performances en utilisant modèles de files d'attente a été présentée. Finalement, nous avons analysé le trafic hétérogène dans l'ONU hybride, et nous avons proposé un cadre pour un algorithme d'ordonnancement qui puisse mettre à jour les règles de service de façon dynamique / A big growth in the number of subscribers is increasing the traffic volume passing through each sector in a telecommunication network. Mechanisms are required to solve the traffic shift problem between two sectors of the network in an efficient way. Additionally, it is expected that the access networks (optical and wireless) will constitute the largest energy consumers among the networks for the next ten years. This situation and the increasing impact of networks on the environment have made become the energy efficiency in telecommunication networks an important theme for researches. This dissertation hence focuses on the proposition of novel solutions for deal with the problems due to the growing of traffic in different segments of the network. Firstly, we have studied the traffic shift between interconnected networks by using the synchronization as technique to solve this problem. Secondly, we have explored the possibility of provisioning different services over the integration of optical-wireless technologies, which has been considered as a promising candidate for the deployment of high-speed access networks. Architecture of design for the Optical Network Unit (ONU) is presented. Then, energy efficiency has been focused and effective bandwidth management solutions have been proposed. We have also proposed an energy efficiency algorithm for wireless receiver at hybrid ONUS. A performance analysis by queuing models was presented for the implementation of proposed solutions. Finally, we have analyzed the heterogeneous traffic at hybrid ONU, and we have proposed a framework for a scheduling algorithm considering the characteristics of different traffic sources

The increasing demand for mobile data has placed significant strain on radio access networks (RANs), leading to a continuous need for increased network capacity. In keeping with that, a significant advancement in modern RANs is the ability to utilize several receivers and transmitters, to allow for beamforming. One way to increase the capacity of the network is therefore to optimize the resource allocation by preprocessing the transmitted signals, which involves several costly matrix multiplications (MMs). The aim of the project was to investigate the potential of accelerating Ericsson's RAN simulations by using AI based approximate matrix multiplication (AMM) algorithms. The main focus was on the multiply additionless (MADDNESS) algorithm, a product quantization technique that has achieved speedups of up to 100 times compared to exact MM, and 10 times faster than previous AMM methods. A complex matrix handling version of MADDNESS was implemented in Java and Python respectively, and its speed and accuracy were evaluated against Ericsson's current MM implementation. The proposed implementation did not beat the benchmark with respect to speed, instead resulting in a 4-10 times slowdown in runtime. However, this may largely be due to the fact that the used languages do not allow for complete control over memory resource allocation. As such, the implementations at hand do not incorporate all the crucial features of the algorithm. Particularly, the handicapped version does not fully leverage the vectorization potential, which is one of the key contributors to the speed of the algorithm. Consequently, further improvements are necessary before employing the techniques in an end-to-end implementation. / Den växande efterfrågan på mobildata har ökat belastningen på dagens radionätverk (RAN) och har medfört ett behov av att utvidga dess kapacitet. En betydande innovation inom RAN är beamforming, vilket är förmågan att fokusera digitala signaler mot mottagaren och på så vis öka singalstyrkan. En metod för att öka kapaciteten i ett nätverk är att optimera både kvaliteten av och resursallokeringen mellan nätverkets digitala kanaler, vilket medför tidskrävande matrismultiplikationer. Syftet med denna studie var att utforska om AI-baserade approximativa matrismultiplikationsalgoritmer har potentialen att accelerera Ericssons digitala tvilling-simuleringar. Studien fokuserade i huvudsak på produktkvantiseringsalgoritmen MADDNESS som påvisat potentialen att accelerera exakta matrismultiplikationer med en faktor 100, samt en faktor 10 snabbare än jämförbara approximativa metoder. En modifierad version av MADDNESS, som behandlar komplexa matriser, implementerades i Java samt Python, varefter precisionen och hastigheten utvärderades. Den föreslagna implementationen resulterade i en försämring med avseende på hastigheten med en faktor 4-10 jämfört med Ericssons nuvarande algoritmer. Den föreslagna implementationen saknar effektiv minnesallokering och misslyckas följaktligen att till fullo ta tillvara på vektoriseringspotentialen i MADDNESS. Detta indikerar att det är nödvändigt för ytterligare förbättringar innan algoritmen är användbar i den givna simuleringsmiljön.

Dynamic spectrum access enabled by cognitive radio networks are envisioned to drive the next generation wireless networks that can increase spectrum utility by opportunistically accessing unused spectrum. Due to the policy constraint that there could be no interference to the primary (licensed) users, secondary cognitive radios have to continuously sense for primary transmissions. Typically, sensing reports from multiple cognitive radios are fused as stand-alone observations are prone to errors due to wireless channel characteristics. Such dependence on cooperative spectrum sensing is vulnerable to attacks such as Secondary Spectrum Data Falsification (SSDF) attacks when multiple malicious or selfish radios falsify the spectrum reports. Hence, there is a need to quantify the trustworthiness of radios that share spectrum sensing reports and devise malicious node identification and robust fusion schemes that would lead to correct inference about spectrum usage. In this work, we propose an anomaly monitoring technique that can effectively capture anomalies in the spectrum sensing reports shared by individual cognitive radios during cooperative spectrum sensing in a multi-channel distributed network. Such anomalies are used as evidence to compute the trustworthiness of a radio by its neighbours. The proposed anomaly monitoring technique works for any density of malicious nodes and for any physical environment. We propose an optimistic trust heuristic for a system with a normal risk attitude and show that it can be approximated as a beta distribution. For a more conservative system, we propose a multinomial Dirichlet distribution based conservative trust framework, where Josang*s Belief model is used to resolve any uncertainty in information that might arise during anomaly monitoring. Using a machine learning approach, we identify malicious nodes with a high degree of certainty regardless of their aggressiveness and variations introduced by the pathloss environment. We also propose extensions to the anomaly monitoring technique that facilitate learning about strategies employed by malicious nodes and also utilize the misleading information they provide. We also devise strategies to defend against a collaborative SSDF attack that is launched by a coalition of selfish nodes. Since, defense against such collaborative attacks is difficult with popularly used voting based inference models or node centric isolation techniques, we propose a channel centric Bayesian inference approach that indicates how much the collective decision on a channels occupancy inference can be trusted. Based on the measured observations over time, we estimate the parameters of the hypothesis of anomalous and non-anomalous events using a multinomial Bayesian based inference. We quantitatively define the trustworthiness of a channel inference as the difference between the posterior beliefs associated with anomalous and non-anomalous events. The posterior beliefs are updated based on a weighted average of the prior information on the belief itself and the recently observed data. Subsequently, we propose robust fusion models which utilize the trusts of the nodes to improve the accuracy of the cooperative spectrum sensing decisions. In particular, we propose three fusion models: (i) optimistic trust based fusion, (ii) conservative trust based fusion, and (iii) inversion based fusion. The former two approaches exclude untrustworthy sensing reports for fusion, while the last approach utilizes misleading information. All schemes are analyzed under various attack strategies. We propose an asymmetric weighted moving average based trust management scheme that quickly identifies on-off SSDF attacks and prevents quick trust redemption when such nodes revert back to temporal honest behavior. We also provide insights on what attack strategies are more effective from the adversaries* perspective. Through extensive simulation experiments we show that the trust models are effective in identifying malicious nodes with a high degree of certainty under variety of network and radio conditions. We show high true negative detection rates even when multiple malicious nodes launch collaborative attacks which is an improvement over existing voting based exclusion and entropy divergence techniques. We also show that we are able to improve the accuracy of fusion decisions compared to other popular fusion techniques. Trust based fusion schemes show worst case decision error rates of 5% while inversion based fusion show 4% as opposed majority voting schemes that have 18% error rate. We also show that the proposed channel centric Bayesian inference based trust model is able to distinguish between attacked and non-attacked channels for both static and dynamic collaborative attacks. We are also able to show that attacked channels have significantly lower trust values than channels that are not– a metric that can be used by nodes to rank the quality of inference on channels.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increases energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices causes a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Driven by the surging need for seamless connectivity, research in the wireless communication area has dramatically evolved over the years to meet the increasing demand for data rate and seamless coverage. Such evolvement concurs with a notable increase in data traffic and the widespread of data-hungry devices, thereby inflicting stringent requirements on terrestrial networks. Despite the tremendous advances achieved through the past generations of wireless systems, almost half of the world's population remains unconnected, leading to an accentuated digital divide problem. Therefore, this work invigorates a new connectivity solution that integrates aerial and terrestrial communications with a high-altitude platform station (HAPS) to promote a sustainable connectivity landscape. The connectivity solution adopted in this thesis specifically integrates terrestrial base stations with hot-air balloons under the framework of a cloud-enabled HAPS via a data-sharing fronthauling strategy. The aerial (hot-air balloons) and terrestrial base stations, grouped into disjoint clusters, coordinate their mutual transmission to serve aerial (i.e., drones) and terrestrial users. This work studies the downlink communication from the cloud-enabled HAPS to the aerial and terrestrial users under practical system considerations, namely the limited transmit power and the limited-capacity fronthaul link, per-base station. To this end, the first part of the thesis devises a specific optimization problem that maximizes the network sum-rate while accounting for system design constraints to determine the user association strategy, i.e., user to terrestrial clusters or user to air clusters, and the associated beamforming vectors. The second part of the thesis, then, designs a different resource allocations optimization problem that accounts for the fairness among the users, thus adopting a proportionally fair scheduling scheme to assign users on frequency tones to maximize the log of the long-term average rate. On this account, the work solves a handful of non-convex intricate optimization problems using techniques from optimization theory, namely, fractional programming and $\ell_0$-norm approximation. The work consequently outlines the gains realized by providing on-demand coverage in crowded and unserved areas. Moreover, the thesis illustrates the benefits of coordinating the operations of aerial and terrestrial base stations for interference management, load-balancing, and fairness measures.

no / Multimedia applications in wireless networks are usually categorized into various classes according to their traffic patterns and differentiated Quality-of-Service (QoS) requirements. The traffic of heterogeneous multimedia applications often exhibits the Long-Range Dependent (LRD) and Short-Range Dependent (SRD) properties, respectively. The integrated scheduling scheme that combines Priority Queuing (PQ) and Generalized Processor Sharing (GPS) within a hierarchical structure, referred to as PQ-GPS, has been identified as an efficient mechanism for QoS differentiation in wireless networks and attracted significant research efforts. However, due to the high complexity and interdependent relationship among traffic flows, modelling of the integrated scheduling scheme poses great challenges. To address this challenging and important research problem, we develop an original analytical model for PQ-GPS systems under heterogeneous LRD and SRD traffic. A cost-effective flow decomposition approach is proposed to equivalently divide the integrated scheduling system into a group of Single-Server Single-Queue (SSSQ) systems. The expressions for calculating the queue length distribution and loss probability of individual traffic flows are further derived. After validating its accuracy, the developed model is adopted as an efficient performance tool to investigate the important issues of resource allocation and call admission control in the integrated scheduling system under QoS constraints.

In this work, a new multicast routing algorithm for wireless networks is presented. The algorithm, called SWIM (Source-initiated WIreless Multicast), is a depth-optimal multicast tree formation algorithm. SWIM is fully distributed and has an average computational complexity of O(N 2 ). SWIM forms a shared tree from the source(s) to destinations / yet, as a by-product, it creates a multicast mesh structure by maintaining alternative paths at every tree node. This makes SWIM suitable for both ad hoc networks and access networks with multiple gateways. An extension to the main algorithm is presented for the use in dynamic networks with mobility and/or dynamic destination group. Performance of SWIM is studied with simulations and is compared to other algorithms in the literature. Due to depth optimality, SWIM achieves a lower average and maximum delay than the compared algorithms. The throughput performance is found to be high. Working capability with rateless codes are also studied.

The main goal of this diploma thesis is to design of high-speed optical network. The first part deals with theoretical knowledge in the field of optical transmissions, especially principle of wavelength division multiplexing. Generally speaking, this part is dedicated to optical connections over long distances. It will concentrate on different types of wavelength division multiplexing, optical fiber amplifiers and other basic optical components. Then it discusses influence of negative effects acting on optical transmission and discusses how to reduce or suppress their influence. Subsequently, there is designed backbone network in the Czech Republic in OptiSystem 7.0 software and are verified some mentioned theoretical knowledge. One of chapters also presents results of measurements of real optical routes in our state. The second part of the diploma thesis moves its attention on that part of optical network, which provides data connectivity to end users, that means it is focused on optical access network. There are described the most common topologies, standards and components. Based on these findings, in the last chapter, there is worked out design of optical access network FTTH (more precisely FTTD) in the selected location. Afterwards, the design is transferred to the OptiSystem 7.0 software, where is verified its functionality.