1,720,996 research outputs found
Joint management of compute and radio resources in mobile edge computing: a market equilibrium approach
Full text linkEdge computing has been recently introduced to bring computational capabilities closer to end-users of modern network-based services, supporting existing and future delay-sensitive applications by effectively addressing the high propagation delay issue that affects cloud computing. However, the problem of efficiently and fairly managing the system resources presents particular challenges due to the limited capacity of both edge nodes and wireless access networks and the heterogeneity of resources and services' requirements. To this end, we propose a techno-economic market where service providers act as buyers, securing both radio and computing resources to execute their associated end-users' jobs while being constrained by a budget limit. We design an allocation mechanism that employs convex programming to find the unique market equilibrium point that maximizes fairness while ensuring that all buyers receive their preferred resource bundle. Additionally, we derive theoretical properties that confirm how the market equilibrium approach strikes a balance between fairness and efficiency. We also propose alternative allocation mechanisms and give a comparison with the market-based mechanism. Finally, we conduct simulations to numerically analyze and compare the performance of the mechanisms and confirm the market model's theoretical properties
5G Ranging: Towards Flexible Positioning Services
No full textIn a world where positioning is becoming increasingly important and emerging applications have progressively tighter requirements, the current satellite-based positioning solutions can not address their needs. We propose a new network-based positioning approach based on O-RAN 5G. The solution is easily deployable, transparent to the users, and flexible to the application needs, thanks to the programmability of the network. This is the first step towards a positioning network service targeting a flexible accuracy level, minimizing the number of parameters, and optimizing the resource and computation utilization
RL-based Resource Allocation in mmWave 5G IAB Networks
Full text link5G standardization has envisioned mmWave communications as a promising direction to expand the capacity of current mobile radio networks. However, communications at high frequency are characterized by extremely harsh propagation conditions, thus requiring a high base station deployment density. To solve this issue, from both technical and economic perspective, 3GPP has proposed mmWave access networks based on an Integrated Access and Backhaul (IAB) multi-hop architecture.IAB networks require fine-tuning of the available resources in a complex setting, due to directional transmissions, device heterogeneity, and harsh propagation conditions. The latter, in particular, characterize the operations of such networks, resulting in links with very different levels of availability. For this reason, traditional optimization techniques do not provide the best performance in these conditions. We believe, instead, Reinforcement Learning (RL) techniques can implicitly consider the dynamics of the network links and learn the best resource allocation strategy in networks with intermittent links. In this paper, we propose an RL-based resource allocation approach that shows the advantages of these techniques in dynamic environmental conditions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Clustered robust routing for traffic engineering in software-defined networks
No full textOne of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate Traffic Engineering modules able to optimize network configuration according to traffic. Ideally, the network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in multiple domains and not only in traffic values. Moreover, to provide more flexibility to the decisions on when to apply a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing even in case of smooth transitions. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration. Moreover, we present an experimental platform where our solution is implemented in a production-ready SDN controller
Resource allocation in mmWave 5G IAB networks: A reinforcement learning approach based on column generation
Full text linkMillimeter wave (mmWave) communications have been introduced in the 5G standardization process due to their attractive potential to provide a huge capacity extension to traditional sub-6 GHz technologies. However, such high-frequency communications are characterized by harsh propagation conditions, thus requiring base stations to be densely deployed. Integrated access and backhaul (IAB) network architecture proposed by 3GPP is gaining momentum as the most promising and cost-effective solution to this need of network densification. IAB networks’ available resources need to be carefully tuned in a complex setting, including directional transmissions, device heterogeneity, and intermittent links with different levels of availability that quickly change over time. It is hard for traditional optimization techniques to provide alone the best performance in these conditions. We believe that Deep Reinforcement Learning (DRL) techniques, especially assisted with Long Short-Term Memory (LSTM), can implicitly capture the regularities of environment dynamics and learn the best resource allocation strategy in networks affected by obstacle blockages. In this article, we propose a DRL based framework based on the Column Generation (CG) that shows remarkable effectiveness in addressing routing and link scheduling in mmWawe 5G IAB networks in realistic scenarios
Planning Mm-Wave Access Networks with Reconfigurable Intelligent Surfaces
Full text linkWith the capability to support gigabit data rates, millimetre-wave (mm-Wave) communication is unanimously considered a key technology of future cellular networks. However, the harsh propagation at such high frequencies makes these networks quite susceptible to failures due to obstacle blockages. Recently introduced Reconfigurable Intelligent Surfaces (RISs) can enhance the coverage of mm-Wave communications by improving the received signal power and offering an alternative radio path when the direct link is interrupted. While several works have addressed this possibility from a communication standpoint, none of these has yet investigated the impact of RISs on large-scale mm-Wave networks. Aiming to fill this literature gap, we propose a new mathematical formulation of the coverage planning problem that includes RISs. Using well-established planning methods, we have developed a new optimization model where RISs can be installed alongside base stations to assist the communications, creating what we have defined as Smart Radio Connections. Our simulation campaigns show that RISs effectively increase both throughput and coverage of access networks, while further numerical results highlight additional benefits that the simplified scenarios analyzed by previous works could not reveal
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