プライマリシステムの干渉制限を考慮した周波数共用のためのリソース割り当てに関する研究

In wireless communications, the improvement of spectral efficiency isrequired due to the shortage of frequency resource. As an effectivesolution, spectrum sharing has been attracted attention. A cognitiveradio is promising technology for realization of spectrum sharing. Inthe spectrum sharing, cognitive user (secondary user) has to protectlicensed user (primary user) according to the interference constraint.However, conventional metric of interference constraint cannot avoidlarge performance degradation in primary system with widely rangeof Signal to Noise Ratio (SNR) such as a cellular system. Additionally,conventional interference constraints do not considers schedulingbehavior in cellular system. In order to solve these problems, thispaper proposes novel metric of the interference constraint whichsupports the widely SNR region of the primary system, so calledcapacity conservation ratio (CCR). The CCR is defined as the ratio ofthe capacity of the Primary receiver without interference from thesecondary transmitter, to the decreased primary capacity due tointerference. Proposed interference constraint based on CCR canprotect primary capacities over the widely SNR region. In addition,scheduling behavior of the primary system can be protected by usingproposed interference constraint. In addition, we propose transmitpower control schemes: exact and simplified power control. The exactpower control can satisfy requirement of interference constraintwithout large margin; however, transmit power cannot be derivewithout numerical analysis. In contrast, transmit power isclosed-form solution in the simplified power control with satisfyingthe interference constraint. Finally, this thesis proposes the resourcescheduling under the interference constraint. Proposed schedulingachieves the high throughput and high user fairness in the secondarysystem without increasing feedback information compared withconventional algorithm.現在、無線通信において周波数リソース不足が深刻な問題となっており、抜本的な対策技術としてコグニティブ周波数共用が注目されている。本論文では、周波数共用において既存システムの周波数帯を他システム（2 次システム）が二次利用するために干渉制限指標及びリソース割り当てに関する研究を行った。一つ目の研究では、既存システムに与える与干渉状態の評価指標について提案を行い，幅広い通信品質の既存システムを保護可能な干渉制限について評価を行った．評価ではシステムのリンクが静的モデルおよび動的なリソース配分で変更される動的モデルを用いた．二つ目の研究では，その干渉制限達成可能な送信電力制御の検討を行った。送信電力制御を行う際に，外部からチャネル情報の一部のみが得られると仮定し，確率的に変動するフェージング要素について所望のアウテージ確率を満足できるように数値解析を行い，厳密設計および簡易設計について提案を行った．三つ目の研究では、既存システムが複数端末に対して無線リソースをスケジューリングするモデルへと拡張し，2 次システムが干渉を回避しつつ，効率的リソース割り当てに関する検討を行った。電気通信大学201

Probabilistic Neural Networks for Special Tasks in Electromagnetics

Tato práce pojednává o technikách behaviorálního modelování pro speciální úlohy v elektromagnetismu, které je možno formulovat jako problém aproximace, klasifikace, odhadu hustoty pravděpodobnosti nebo kombinatorické optimalizace. Zkoumané methody se dotýkají dvou základních problémů ze strojového učení a combinatorické optimalizace: ”bias vs. variance dilema” a NP výpočetní komplexity. Boltzmanův stroj je v práci navržen ke zjednodušování komplexních impedančních sítí. Bayesovský přístup ke strojovému učení je upraven pro regularizaci Parzenova okna se snahou o vytvoření obecného kritéria pro regularizaci pravděpodobnostní a regresní neuronové sítě.The thesis deals with behavioural modelling techniques capable solving special tasks in electromagnetics which can be formulated as approximation, classification, probability estimation, and combinatorial optimization problems. Concept of the work lies in applying a probabilistic approach to behavioural modelling. Examined methods address two general problems in machine learning and combinatorial optimization: ”bias vs. variance dilemma” and NP computational complexity. The Boltzmann machine is employed to simplify a complex impedance network. The Parzen window is regularized using the Bayesian strategy for obtaining a model selection criterion for probabilistic and general regression neural networks.

Optimal investment, financing and dividends: A Stackelberg differential game

Financing

A tutorial on the characterisation and modelling of low layer functional splits for flexible radio access networks in 5G and beyond

The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy.Peer ReviewedPostprint (author's final draft

Continuous-Time Quantum Walks: Models for Coherent Transport on Complex Networks

This paper reviews recent advances in continuous-time quantum walks (CTQW) and their application to transport in various systems. The introduction gives a brief survey of the historical background of CTQW. After a short outline of the theoretical ideas behind CTQW and of its relation to classical continuous-time random walks (CTRW) in Sec.~2, implications for the efficiency of the transport are presented in Sec.~3. The fourth section gives an overview of different types of networks on which CTQW have been studied so far. Extensions of CTQW to systems with long-range interactions and with static disorder are discussed in section V. Systems with traps, i.e., systems in which the walker's probability to remain inside the system is not conserved, are presented in section IV. Relations to similar approaches to the transport are studied in section VII. The paper closes with an outlook on possible future directions.Comment: review article to appear in Physics Reports, 39 pages, 44 figure

Planification globale des réseaux mobiles de la quatrième génération (4G)

RÉSUMÉ Dans le contexte actuel où l’information est la clé du succès, peu importe le domaine où l’on se place, les réseaux de télécommunications sont de plus en plus sollicités. D’énormes quantités d’informations circulent sur les réseaux à chaque seconde. Il est primordial d’assurer la disponibilité de ces réseaux afin de garantir la transmission de ces données en toutes circonstances. Le problème de la planification des réseaux de télécommunications consiste à déterminer, parmi un ensemble de sites potentiels, ceux à utiliser afin de couvrir une zone géographique donnée. Il convient également de choisir les équipements à installer sur ces sites et de faire le lien entre eux en fonction de certaines contraintes bien définies. Depuis des dizaines d’années, plusieurs auteurs se sont penchés sur la résolution de ce problème dans le but de minimiser le coût d’installation du réseau. Ces auteurs se sont intéressés à divers aspects du problème sans le considérer dans sa globalité. Certaines études ont été effectuées récemment sur la planification globale des réseaux mobiles. Les auteurs se sont intéressés aux réseaux de la troisième génération et ont proposé un modèle pour résoudre le problème de façon globale. Cependant, ils n’ont pas pris en compte la tolérance du réseau aux pannes qui pourraient survenir. Cette thèse propose un cadre de planification globale pour les réseaux de la quatrième génération (la nouvelle génération des réseaux mobiles). La survivabilité du réseau est prise en compte dans cette étude. Le travail a été effectué en trois phases. Dans la première phase, un modèle global incluant la tolérance aux pannes a été conçu pour la planification des réseaux 4G (WiMAX) et résolu de manière optimale avec un solveur mathématique, en utilisant la programmation linéaire en nombres entiers. L’objectif du modèle consiste à minimiser le coût du réseau, tout en maximisant sa survivabilité. Afin de montrer la pertinence de la résolution globale, le modèle a été comparé à un modèle séquentiel avec les mêmes contraintes. Le modèle séquentiel consiste à subdiviser le problème en trois sous-problèmes et à les résoudre successivement. Un modèle global qui n’intègre pas les contraintes de fiabilité a également été conçu afin de vérifier l’effet des pannes sur le réseau. Les résultats obtenus par le modèle global proposé sont, en moyenne, 25% meilleurs que ceux des deux autres modèles. Le problème de planification globale des réseaux et le problème de survivabilité des réseaux de télécommunications sont deux problèmes NP-difficiles. La combinaison de ces deux problèmes donne un problème encore plus difficile à résoudre que chacun des problèmes pris séparément. La méthode exacte utilisée dans la première phase ne peut résoudre que des instances de petite taille. Dans la deuxième phase, nous proposons une métaheuristique hybride afin trouver de "bonnes solutions" en un temps "raisonnable" pour des instances de plus grande taille. La métaheuristique proposée est une nouvelle forme d’hybridation entre l’algorithme de recherche locale itérée et la méthode de programmation linéaire en nombres entiers. L’hybridation de ces deux méthodes permet de bénéficier de leurs avantages respectifs, à savoir l’exploration efficace de l’espace de recherche et l’intensification des solutions obtenues. L’intensification est effectuée par la méthode exacte qui calcule la meilleure solution possible à partir d’une configuration donnée tandis que l’exploration de l’espace est faite à travers l’algorithme de recherche locale itérée. Les performances de l’algorithme ont été évaluées par rapport à la méthode exacte proposée lors de la première phase. Les résultats montrent que l’algorithme proposé génère des solutions qui sont, en moyenne à 0,06% des solutions optimales. Pour les instances de plus grande taille, des bornes inférieures ont été calculées en utilisant une relaxation du modèle. La comparaison des résultats obtenus par l’algorithme proposé avec ces bornes inférieures montrent que la métaheuristique obtient des solutions qui sont, en moyenne à 2,43% des bornes inférieures pour les instances qui ne peuvent pas être résolues de manière optimale, avec un temps de calcul beaucoup plus faible. La troisième phase a consisté à la conception d’une métaheuristique multi-objectifs pour résoudre le problème. En effet, nous essayons d’optimiser deux objectifs contradictoires qui sont le coût du réseau et sa survivabilité. L’algorithme proposé permet d’offrir plus d’alternatives au planificateur, lui donnant ainsi plus de flexibilité dans la prise de décision.----------ABSTRACT In the current context where information is the key to success in any field where one stands, telecommunications networks are increasingly in demand. Huge amounts of information circulates on the networks every second. It is essential to ensure the availability of these networks to ensure the transmission of these data at any time. The problem of planning of telecommunication networks is to determine, from a set of potential sites, those to be used to cover a given geographical area. One should also choose the equipment to be installed on these sites and to link them according to certain well-defined constraints. For decades, several authors have focused on solving this problem in order to minimize the cost of network installation. These authors were interested in various aspects of the problem without considering it in its entirety. Some studies have recently been performed on the global planning of mobile networks. The authors were interested in the third generation networks. They proposed a model to solve the problem entirely, without breaking it down into sub-problems. However, they did not take into account the fault tolerance of network. This thesis proposes a global planning framework for the fourth generation (4G) networks (the new generation of mobile networks). The survivability of the network is taken into account in this study. The work was conducted in three phases. In the first phase, a global model including survivability has been designed for the planning of 4G (WiMAX) networks and solved optimally with a mathematical solver using the integer linear programing method. The objective of the model is to minimize the network cost while maximizing its survivability. To show the relevance of the global resolution, the model was compared to a sequential model with the same constraints. The sequential model is to divide the problem into three sub-problems and solve them successively. A global model which does not include survivability constraints has also been designed to test the effect of failures on the network. The results show that the proposed model performs on average 25% better than the two other models. The problem of global network planning and the problem of survivability of telecommunications networks are two NP-hard problems. The combination of these two problems provides a problem even more difficult to solve than each problem taken separately. The exact method used in the first phase can only solve small instances. In the second phase, we propose a hybrid metaheuristic to find `good solutions' in a `reasonable time' for instances of larger size. The proposed metaheuristic is a new form of hybridization between the iterated local search algorithm and the integer linear programing method. The hybridization of these two methods can benefit from their respective advantages, namely the efficient exploration of the search space and the intensification of the solutions obtained. The intensification is performed by the exact method that calculates the best possible solution from a given configuration while the exploration of the search space is made through the iterated local search algorithm. The performance of the algorithm have been evaluated with respect to the exact method given in the first phase. The results show that the proposed algorithm generates solutions that are on average 0,06% of the optimal solutions. For the larger instances, the lower bounds are calculated using a relaxation of the model. The comparison of the results obtained by the proposed algorithm with the lower bounds show that the metaheuristic obtains solutions that are on average 2,43% from the lower bounds, for the instances that cannot be solved optimally, within a much less computation time. The third phase involved the design of a multi-objective metaheuristics to solve the problem. Indeed, we try to optimize two conflicting objectives which are the cost of network and its survivability. The proposed algorithm allows us to offer more alternatives to the planner, giving him (her) more exibility in the decision making process

Automatiserad behandlingsplanering inom högintensivt fokuserat ultraljud guidat av magnetresonanstermometri

Högintensivt fokuserat ultraljud guidat av magnetresonanstermometri (MR-HIFU) är en ickeinvasiv medicinsk metod för att åtstadkomma lokal uppvärmning i vävnad, vilket tillämpas främst för behandling av tumörer. Tekniken utnyttjar fokuserat ultraljud för att lokalt höja temperaturen i tumörvävnaden vilket resulterar nekros. För att orsaka ablation i hela tumören krävs det att flera av dessa celler sonikeras. Att manuellt planera hur dessa celler skall placeras, medan behandlingens samtliga säkerhetsaspekter tas i beaktande, är en tidskrävande och monoton process som samtidigt kräver expertis och precision. Dessutom, på grund av behandlingens mångfacetterade karaktär är den svår att optimera manuellt. Syftet med detta arbete var att utforma en algoritm för automatisk behandlingsplanering för MR-HIFU för att förbättra arbetsflödet i planeringsprocessen, samt att producera en prototyp av en dylik algoritm. Den presenterade algoritmen är en stegvis process. Först producerar algoritmen en grupp av positioner som kan sonikeras på ett säkert sätt. Därefter finner algoritmen den optimala undergruppen av dessa positioner. Slutligen optimerar algoritmen resten av de relevanta behandlingsparametrarna. Behandlingen kan optimeras antingen genom att maximera volymen som utsätts för ablation eller genom att minimera tiden som behandlingen kräver. Den presenterade algoritmen är tillräckligt generell för att kunna anpassas till samtliga ablationstillämpningar av MR-HIFU. Den har en modulstruktur vilket förenklar uppgradering, och den kan använda information om hur behandlingen framskrider för att reglera och uppdatera planen. Detta är den första publicerade algoritmen för behandlingsplanering inom MRHIFU som kan optimera behandlingen samt använda återkoppling för att reglera planen. Prototypen testades i två konstgjorda fall samt i ett äkta kliniskt fall vilket dess genomförbarhet.Magnetic Resonance guided High Intensity Focused Ultrasound (MR-HIFU) is a noninvasive medical procedure for localized tissue heating, used mostly in treatment of tumours. The modality utilizes focused ultrasound to raise the temperature of the tumour tissue in small localized volumes, resulting in necrosis. To ablate the whole tumour, several of these sonication cells are need. Planning the positions of the cells, while taking into consideration all safety aspects of the treatment, is a time consuming and monotonous task, but requires at the same time expertise and precision. Furthermore, due to the complex characteristics of a MR-HIFU treatment, it is difficult to optimize manually. The aim of the thesis was to design an outline for an automated treatment planning algorithm for MR-HIFU, and to produce a prototype of such an algorithm. The presented algorithm relies on a step-wise process. First, a set of positions is produced that can be sonicated safely. Then, an optimal subset of those positions is selected. Finally, the remaining treatment parameters are optimized. The treatment can either be optimized for maximum coverage or minimum total treatment time. The proposed algorithm is general enough to be adaptable to all ablation applications of MR-HIFU. It has a modular structure for easy updating, and it is able to improve on the plan during the treatment based on feedback from already delivered cells. This is the first published treatment planning algorithm for MR-HIFU that optimizes the treatment and has the ability to update the plan based on feedback. The prototype was tested in two artificial test cases and one real clinical case, proving its feasibility