Interference Management of Inband Underlay Device-toDevice Communication in 5G Cellular Networks

The explosive growth of data traffic demands, emanating from smart mobile devices and bandwidth-consuming applications on the cellular network poses the need to drastically modify the cellular network architecture. A challenge faced by the network operators is the inability of the finite spectral resources to support the growing data traffic. The Next Generation Network (NGN) is expected to meet defined requirements such as massively connecting billions of devices with heterogeneous applications and services through enhanced mobile broadband networks, which provides higher data rates with improved network reliability and availability, lower end-to-end latency and increased energy efficiency. Device-to-Device (D2D) communication is one of the several emerging technologies that has been proposed to support NGN in meeting these aforementioned requirements. D2D communication leverages the proximity of users to provide direct communication with or without traversing the base station. Hence, the integration of D2D communication into cellular networks provides potential gains in terms of throughput, energy efficiency, network capacity and spectrum efficiency. D2D communication underlaying a cellular network provides efficient utilisation of the scarce spectral resources, however, there is an introduction of interference emanating from the reuse of cellular channels by D2D pairs. Hence, this dissertation focuses on the technical challenge with regards to interference management in underlay D2D communication. In order to tackle this challenge to be able to exploit the potentials of D2D communication, there is the need to answer some important research questions concerning the problem. Thus, the study aims to find out how cellular channels can be efficiently allocated to D2D pairs for reuse as an underlay to cellular network, and how mode selection and power control approaches influence the degree of interference caused by D2D pairs to cellular users. Also, the research study continues to determine how the quality of D2D communication can be maintained with factors such as bad channel quality or increased distance. In addressing these research questions, resource management techniques of mode selection, power control, relay selection and channel allocation are applied to minimise the interference caused by D2D pairs when reusing cellular channels to guarantee the Quality of Service (QoS) of cellular users, while optimally improving the number of permitted D2D pairs to reuse channels. The concept of Open loop power control scheme is examined in D2D communication underlaying cellular network. The performance of the fractional open loop power control components on SINR is studied. The simulation results portrayed that the conventional open loop power control method provides increased compensation for the path loss with higher D2D transmit power when compared with the fractional open loop power control method. Furthermore, the problem of channel allocation to minimise interference is modelled in two system model scenarios, consisting of cellular users coexisting with D2D pairs with or without relay assistance. The channel allocation problem is solved as an assignment problem by using a proposed heuristic channel allocation, random channel allocation, Kuhn-Munkres (KM) and Gale-Shapley (GS) algorithms. A comparative performance evaluation for the algorithms are carried out in the two system model scenarios, and the results indicated that D2D communication with relay assistance outperformed the conventional D2D communication without relay assistance. This concludes that the introduction of relay-assisted D2D communication can improve the quality of a network while utilising the available spectral resources without additional infrastructure deployment costs. The research work can be extended to apply an effective relay selection approach for a user mobility scenario

Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction

The vital role of analogical reasoning in human cognition allows us to grasp novel concepts by linking them with familiar ones through shared relational structures. Despite the attention previous research has given to word analogies, this work suggests that Large Language Models (LLMs) often overlook the structures that underpin these analogies, raising questions about the efficacy of word analogies as a measure of analogical reasoning skills akin to human cognition. In response to this, our paper introduces a task of analogical structure abduction, grounded in cognitive psychology, designed to abduce structures that form an analogy between two systems. In support of this task, we establish a benchmark called SCAR, containing 400 scientific analogies from 13 distinct fields, tailored for evaluating analogical reasoning with structure abduction. The empirical evidence underlines the continued challenges faced by LLMs, including ChatGPT and GPT-4, in mastering this task, signifying the need for future exploration to enhance their abilities.Comment: Accepted to EMNLP 2023 (Findings

Combining Outcome-Based and Preference-Based Matching: A Constrained Priority Mechanism

We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the predicted probability of employment, while in the student assignment context it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families, students) based on their preferences, but subject to meeting the planner's specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner's threshold.Comment: This manuscript has been accepted for publication by Political Analysis and will appear in a revised form subject to peer review and/or input from the journal's editor. End-users of this manuscript may only make use of it for private research and study and may not distribute it furthe

An Efficient Outpatient Scheduling Approach

Outpatient scheduling is considered as a complex problem. Efficient solutions to this problem are required by many health care facilities. Our previous work in Role-Based Collaboration (RBC) has shown that the group role assignment problems can be solved efficiently. Making connections between these two kinds of problems is meaningful. This paper proposes an efficient approach to outpatient scheduling by specifying a bidding method and converting it to a group role assignment problem. The proposed approach is validated by conducting simulations and experiments with randomly generated patient requests for available time slots. The major contribution of this paper is an efficient outpatient scheduling approach making automatic outpatient scheduling practical. The exciting result is due to the consideration of outpatient scheduling as a collaborative activity and the creation of a qualification matrix in order to apply the group role assignment algorithm. Note to practitioners -As the “Age Wave” approaches, health care facilities are becoming relatively scarce worldwide compared with what are demanded. The varying availability, requirements, and preferences of both facilities and outpatients make the problem of scheduling outpatient appointments on health care facilities extremely challenging. Traditional manually operated scheduling systems based on phone calls are out of date although they are still widely used due to lack of new effective scheduling systems. To solve such a problem requires an efficient Web-based system to schedule the appointments instantly in order to make full use of those expensive and critical facilities. It is able to optimize concerned performance objectives in a clinical environment. The proposed approach provides a technical foundation for efficient Web-based scheduling systems, which can be applied directly to not only outpatient scheduling in the health care sector, but also in some other real-world scheduling applications

Planning automated guided vehicle movements in a factory

This dissertation examines the problems of planning automated guided vehicle (AGV) movement schedules in an automated factory. AGVs are used mainly for material delivery and will have an important role in linking "islands of automation" in automated factories. Their employment in this context requires the plans to be generated in a manner which supports temporal projection so that further planning in other areas is possible. Planning also occurs in a dynamic scenario—while some plans are being executed, planning for new tasks and replanning failing plans occur. Expeditious planning is thus important so that deadlines can be met. Furthermore, dynamic replanning in a multi-agent environment has repercussions—changing one plan may require revision of other plans. Hence the issue of limiting the side effects of dynamic replanning is also considered. In dealing with these issues, the goals of this research are: (1) generate movement plans which can be executed efficiently; (2) develop fast algorithms for the recurrent subproblems viz. task assignment and route planning; and (3) generate robust plans which tolerate execution deviations; this helps to minimize disruptive dynamic replanning with its tendency to initiate a chain reaction of plan revisions. Efficient movement plans mean more productive utilization of the AGV fleet and this objective can be realized by three approaches. First, the tasks are assigned to AGVs optimally using an improved implementation of the Hungarian method. Second, the planner computes shortest routes for the AGVs using a bidirectional heuristic search algorithm which is amenable to parallel implementation for further computational time reduction. Third, whenever AGVs are fortuitously predisposed to assist each other in task execution, the planner will generate gainful collaborative plans. Efficient algorithms have been developed in these areas. The algorithms for task assignment and route planning are also designed to be fast, in keeping with the objective of expeditious planning. Robust plans can be generated using the approach of tolerant planning. Robustness is achieved in two ways: (1) by being tolerant of an AGV's own execution deviations; and (2) by being tolerant of other AGVs' deviant behaviour. Tolerant planning thus defers dynamic replanning until execution errors become excessive. The underlying strategy is to provide more than ample resources (time) for AGVs to achieve various subgoals. Such redundancies aggravate the resource contention problem. To solve this, an iterative negotiation model is proposed. During negotiations, AGVs yield in turn to help eliminate the conflict. The negotiation behaviour of each is governed by how much spare resources each has and tends towards intransigence as the bottom line is approached. In this way, no AGV will jeopardize its own plan while cooperating in the elimination of conflicts. By gradual yielding, an AGV is also able to influence the other party to yield more if it can, therein achieving some fairness. The model has many of the characteristics of negotiation acts in the real world (e.g. skilful negotiation, intransigence, selfishness, willingness to concede, nested negotiations)

Distributed management and coordination of UAV swarms based on infrastructureless wireless networks

[ES] Los Vehículos Aéreos no Tripulados (o drones) ya han demostrado su utilidad en una gran variedad de aplicaciones. Hoy en día, se utilizan para fotografía, cinematografía, inspecciones y vigilancia, entre otros. Sin embargo, en la mayoría de los casos todavía son controlados por un piloto, que como máximo suele estar volando un solo dron cada vez. En esta tesis, tratamos de avanzar en paso más allá en esta tecnología al permitir que múltiples drones con capacidad para despegue y aterrizaje vertical trabajen de forma sincronizada, como una sola entidad. La principal ventaja de realizar vuelos en grupo, comúnmente denominado enjambre, es que se pueden realizar tareas más complejas que utilizando un solo dron. De hecho, un enjambre permite cubrir más área en el mismo tiempo, ser más resistente, tener una capacidad de carga más alta, etc. Esto puede habilitar el uso de nuevas aplicaciones, o una mejor eficiencia para las aplicaciones existentes. Sin embargo, una parte clave es que los miembros del enjambre deben organizarse correctamente, ya que, durante el vuelo, diferentes perturbaciones pueden provocar que sea complicado mantener el enjambre como una unidad coherente. Una vez que se pierde esta coherencia, todos los beneficios previamente mencionados de un enjambre se pierden también. Incluso, aumenta el riesgo de colisiones entre los elementos del enjambre. Por lo tanto, esta tesis se centra en resolver algunos de estos problemas, proporcionando un conjunto de algoritmos que permitan a otros desarrolladores crear aplicaciones de enjambres de drones. Para desarrollar los algoritmos propuestos hemos incorporado mejoras al llamado ArduSim. Este simulador nos permite simular tanto la física de un dron como la comunicación entre drones con un alto grado de precisión. ArduSim nos permite implementar protocolos y algoritmos (bien probados) en drones reales con facilidad. Durante toda la tesis, ArduSim ha sido utilizado ampliamente. Su utilización ha permitido que las pruebas fueran seguras, y al mismo tiempo nos permitió ahorrar mucho tiempo, dinero y esfuerzo de investigación. Comenzamos nuestra investigación sobre enjambres asignando posiciones aéreas para cada dron en el suelo. Suponiendo que los drones están ubicados aleatoriamente en el suelo, y que necesitan alcanzar una formación aérea deseada, buscamos una solución que minimice la distancia total recorrida por todos los drones. Para ello se empezó con un método de fuerza bruta, pero rápidamente nos dimos cuenta de que, dada su alta complejidad, este método funciona mal cuando el número de drones aumenta. Por lo tanto, propusimos una heurística. Como en todas las heurísticas, se realizó un compromiso entre complejidad y precisión. Al simplificar el problema, encontramos que nuestra heurística era capaz de calcular una solución muy rápidamente sin aumentar sustancialmente la distancia total recorrida. Además, implementamos el algoritmo de Kuhn-Munkres (KMA), un algoritmo que ha demostrado proporcionar la respuesta exacta (es decir, reducir la distancia total recorrida) en el menor tiempo posible. Después de muchos experimentos, llegamos a la conclusión de que nuestra heurística es más rápida, pero que la solución proporcionada por el KMA es ligeramente más eficiente. En particular, aunque la diferencia en la distancia total recorrida es pequeña, el uso de KMA reduce el número de trayectorias de vuelo que se cruzan entre sí, lo cual es una métrica importante para las siguientes propuestas.[...][CA] Els vehicles aeris no tripulats (o drons) ja han demostrat la seua utilitat en una gran varietat d'aplicacions. Avui dia, s'utilitzen per a fotografia, cinematografia, inspeccions i vigilància, entre altres. No obstant això, en la majoria dels casos encara són controlats per un pilot, que com a màxim sol controlar el vol d'un sol dron cada vegada. En aquesta tesi, tractem d'avançar un pas més enllà en aquesta tecnologia, en permetre que múltiples drons amb capacitat per a l'enlairament i l'aterratge vertical treballen de forma sincronitzada, com una sola entitat. El principal avantatge de realitzar vols en grup, comunament denominats eixam, és que es poden fer tasques més complexes que utilitzant un sol dron. De fet, un eixam permet cobrir més àrea en el mateix temps, ser més resistent, tenir una capacitat de càrrega més alta, etc. Això pot habilitar l'ús de noves aplicacions, o una millor eficiència per a les aplicacions existents. No obstant això, una punt clau és que els membres de l'eixam han d'organitzar-se correctament, ja que, durant el vol, diferents pertorbacions poden provocar que siga complicat mantenir l'eixam com una unitat coherent. Una vegada que es perd aquesta coherència, tots els beneficis prèviament esmentats d'un eixam es perden també. Fins i tot, augmenta el risc de col·lisions entre els elements de l'eixam. Per tant, aquesta tesi se centra a resoldre alguns d'aquests problemes, proporcionant un conjunt d'algorismes que permeten a altres desenvolupadors crear aplicacions d'eixams de drons. Per a desenvolupar els algorismes proposats hem incorporat millores a l'anomenat ArduSim. Aquest simulador ens permet simular tant la física d'un dron com la comunicació entre drons amb un alt grau de precisió. ArduSim ens permet implementar protocols i algorismes (ben provats) en drons reals amb facilitat. Durant tota la tesi, ArduSim s'ha utilitzat àmpliament. El seu ús ha permès que les proves foren segures, i al mateix temps ens va permetre estalviar molt de temps, diners i esforç d'investigació. Per tant, es va utilitzar ArduSim per a cada bloc de construcció que vam desenvolupar. Comencem la nostra recerca sobre eixams assignant posicions aèries per a cada dron en terra. Suposant que els drons estan situats aleatòriament en terra i que necessiten assolir la formació aèria desitjada, cerquem una solució que minimitze la distància total recorreguda per tots els drons. Per a això, es va començar amb un mètode de força bruta, però ràpidament ens vam adonar que, atesa l'alta complexitat, aquest mètode funciona malament quan el nombre de drons augmenta. Per tant, vam proposar una heurística. Com en totes les heurístiques, es va fer un compromís entre complexitat i precisió. En simplificar el problema, trobem que la nostra heurística era capaç de calcular una solució molt ràpidament sense augmentar substancialment la distància total recorreguda. A més, vam implementar l'algorisme de Kuhn-Munkres (KMA), un algorisme que ha demostrat proporcionar la resposta exacta (és a dir, reduir la distància total recorreguda) en el menor temps possible. Després de molts experiments, arribem a la conclusió que la nostra heurística és més ràpida, però que la solució proporcionada pel KMA és lleugerament més eficient. En particular, encara que la diferència en la distància total recorreguda és xicoteta, l'ús de KMA redueix el nombre de trajectòries de vol que s'encreuen entre si, la qual cosa és una mètrica important per a les propostes següents.[...][EN] Unmanned Aerial Vehicles (UAVs) have already proven to be useful in many different applications. Nowadays, they are used for photography, cinematography, inspections, and surveillance. However, in most cases they are still controlled by a pilot, who at most is flying one UAV at a time. In this thesis, we try to take this technology one step further by allowing multiple Vertical Take-off and Landing (VTOL) UAVs to work together as one entity. The main advantage of this group, commonly referred to as a swarm, is that it can perform more complex tasks than a single UAV. When organized correctly, a swarm allows for: more area to be covered in the same time, more resilience, higher load capability, etc. A swarm can lead to new applications, or a better efficiency for existing applications. A key part, however, is that they should be organized correctly. During the flight, different disturbances will make it complicated to keep the swarm as one coherent unit. Once this coherency is lost, all the previously mentioned benefits of a swarm are lost as well. Even worse, the chance of a hazard increases. Therefore, this thesis focuses on solving some of these issues by providing a baseline of building blocks that enable other developers to create UAV swarm applications. In order to develop these building blocks, we improve a multi-UAV simulator called ArduSim. This simulator allows us to simulate both the physics of a UAV, and the communication between UAVs with a high degree of accuracy. This is a crucial part because it allows us to deploy (well tested) protocols and algorithms on real UAVs with ease. During the entirety of this thesis, ArduSim has been used extensively. It made testing safe, and allowed us to save a lot of time, money and research effort. We started by assigning airborne positions for each UAV on the ground. Assuming that the UAVs, are placed randomly on the ground, and that they need to reach a desired aerial formation, we searched for a solution that minimizes the total distance travelled by all the UAVs. We started with a brute-force method, but quickly realized that, given its high complexity, this method performs badly when the number of UAVs grows. Hence, we created a heuristic. As for all heuristics, a trade-off was made between complexity and accuracy. By simplifying the problem, we found that our heuristic was able to calculate a solution very quickly without increasing the total distance travelled substantially. Furthermore, we implemented the \ac{KMA}, an algorithm that has been proven to provide the exact answer (i.e. minimal total distance travelled) in the shortest time possible. After many experiments, we came to the conclusion that our heuristic is faster, but that the solution provided by the \ac{KMA} is slightly better. In particular, although the difference in total distance travelled is small, the \ac{KMA} reduces the numbers of flight paths crossing each other, which is an important metric in our next building block. Once we developed algorithms to assign airborne positions to each UAV on the ground, we started developing algorithms to take off all those UAVs. The objective of these algorithms is to reduce the time it takes for all the UAVs to reach their aerial position, while ensuring that all UAVs maintain a safe distance. The easiest solution is a sequential take-off procedure, but this is also the slowest approach. Hence, we improved it by first proposing a semi-sequential and later a semi-simultaneous take-off procedure. With this semi-simultaneous take-off procedure, we are able to reduce the takeoff time drastically without introducing any risk to the aircraft. [..]Wubben, J. (2023). Distributed management and coordination of UAV swarms based on infrastructureless wireless networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19888

Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID

Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing works mainly focus on alleviating the modality gap by aligning instance-level features of the unlabeled samples. However, the relationships between cross-modality clusters are not well explored. To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Specifically, we design a Many-to-many Bilateral Cross-Modality Cluster Matching (MBCCM) algorithm through optimizing the maximum matching problem in a bipartite graph. Then, the matched pairwise clusters utilize shared visible and infrared pseudo-labels during the model training. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Meanwhile, the cross-modality Consistency Constraint (CC) is proposed to explicitly reduce the large modality discrepancy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing state-of-the-art approaches by a large margin of 8.76% mAP on average