References of "Tarchi, Daniele"
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See detailMachine Learning for Radio Resource Management in Multibeam GEO Satellite Systems
Ortiz Gomez, Flor de Guadalupe UL; Lei, Lei UL; Lagunas, Eva UL et al

in Electronics (2022), 11(7), 992

Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and ... [more ▼]

Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and changes in traffic demand diurnal. This problem is addressed by using flexible payload architectures, which allow payload resources to be flexibly allocated to meet the traffic demand of each beam. While optimization-based radio resource management (RRM) has shown significant performance gains, its intense computational complexity limits its practical implementation in real systems. In this paper, we discuss the architecture, implementation and applications of Machine Learning (ML) for resource management in multibeam GEO satellite systems. We mainly focus on two systems, one with power, bandwidth, and/or beamwidth flexibility, and the second with time flexibility, i.e., beam hopping. We analyze and compare different ML techniques that have been proposed for these architectures, emphasizing the use of Supervised Learning (SL) and Reinforcement Learning (RL). To this end, we define whether training should be conducted online or offline based on the characteristics and requirements of each proposed ML technique and discuss the most appropriate system architecture and the advantages and disadvantages of each approach. [less ▲]

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See detailCooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems
Ortiz Gomez, Flor de Guadalupe UL; Tarchi, Daniele; Martinez, Ramon et al

in IEEE Transactions on Cognitive Communications and Networking (2022), 8(1),

Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand in the near future. Nevertheless, this increase will not be uniform over the entire service area due to ... [more ▼]

Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand in the near future. Nevertheless, this increase will not be uniform over the entire service area due to the non-uniform distribution of users and changes in traffic demand during the day. This problem is addressed by using flexible payload architectures, which allow the allocation of payload resources flexibly to meet the traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to VHTS systems, so in this paper we discuss the use of one reinforcement learning (RL) algorithm and two deep reinforcement learning (DRL) algorithms to manage the resources available in flexible payload architectures for DRM. These algorithms are Q-Learning (QL), Deep Q-Learning (DQL) and Double Deep Q-Learning (DDQL) which are compared based on their performance, complexity and added latency. On the other hand, this work demonstrates the superiority a cooperative multiagent (CMA) decentralized distribution has over a single agent (SA). [less ▲]

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See detailConvolutional Neural Networks for Flexible Payload Management in VHTS Systems
Ortiz Gomez, Flor de Guadalupe UL; Tarchi, Daniele; Martinez, Ramon et al

in IEEE Systems Journal (2021), 15(3), 4675-4686

Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the ... [more ▼]

Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved. [less ▲]

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See detailSupervised machine learning for power and bandwidth management in very high throughput satellite systems
Ortiz Gomez, Flor de Guadalupe UL; Tarchi, Daniele; Martinez, Ramon et al

in International Journal of Satellite Communications and Networking (2021)

In the near future, very high throughput satellite (VHTS) systems are expected to have a high increase in traffic demand. However, this increase will not be uniform over the service area and will be also ... [more ▼]

In the near future, very high throughput satellite (VHTS) systems are expected to have a high increase in traffic demand. However, this increase will not be uniform over the service area and will be also dynamic. A solution to this problem is given by flexible payload architectures; however, they require that resource management is performed autonomously and with low latency. In this paper, we propose the use of supervised machine learning, in particular a classification algorithm using a neural network, to manage the resources available in flexible payload architectures. Use cases are presented to demonstrate the effectiveness of the proposed approach, and a discussion is made on all the challenges that are presented. [less ▲]

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See detailCognitive approaches to enhance spectrum availability for satellite systems
Chatzinotas, Symeon UL; Evans, Barry; Guidotti, Alessandro et al

in International Journal of Satellite Communications and Networking (2016)

Cognitive radio technologies have achieved in the recent years an increasing interest for the possible gain in terms of spectrum usage with respect to unshared approaches. While most of the attention has ... [more ▼]

Cognitive radio technologies have achieved in the recent years an increasing interest for the possible gain in terms of spectrum usage with respect to unshared approaches. While most of the attention has been devoted to the cognitive coexistence between terrestrial systems, the coexistence between terrestrial and satellite communications is also seen as a viable option. Cognitive radio for satellite communications (CoRaSat) has been a European Commission seventh Framework Program project funded under the ICT Call 8. CoRaSat aimed at investigating, developing, and demonstrating cognitive radio techniques in satellite communication systems for flexible and dynamic spectrum access. In this paper, the CoRaSat cognitive approaches and techniques, investigated, developed, and demonstrated as most relevant to satellite communications, are described. In particular, the focus is on spectrum awareness, that is, database and spectrum sensing approaches, and on spectrum exploitation algorithms, that is, resource allocation and beamforming algorithms, to enable the use of spectrum for satellite communications using shared bands. [less ▲]

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See detailCognitive Radio for Ka Band Satellite Communications
Maleki, Sina UL; Chatzinotas, Symeon UL; Sharma, Shree Krishna UL et al

in 32nd AIAA International Communications Satellite Systems Conference, August 2014 (2014, August)

The satellite communication data traffic is increasing dramatically over the coming years. High throughput multibeam satellite networks in Ka band are potentially able to accommodate the upcoming high ... [more ▼]

The satellite communication data traffic is increasing dramatically over the coming years. High throughput multibeam satellite networks in Ka band are potentially able to accommodate the upcoming high data rate demands. However, there is only 500 MHz of exclusive band for download and the same amount for upload. This spectrum shortage impose a barrier in order to satisfy the increasing demands. Cognitive satellite communication in Ka band is considered in this paper in order to potentially provide an additional 4.4 GHz bandwidth for downlink and uplink fixed-satellite-services. In this way, it is expected that the problem of spectrum scarcity for future generation of satellite networks is alleviated to a great extent. The underlying scenarios and enabling techniques are discussed in detail, and finally we investigate the implementation issues related to the considered techniques. [less ▲]

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See detailTechnical Challenges for Cognitive Radio Application in Satellite Communications
Tarchi, Daniele; Guidotti, Alessandro; Icolari, Vincenzo et al

in 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2014 (2014, June)

During the last years, spectrum scarcity has become one of the major issues for the development of new communication systems. Cognitive Radio (CR) approaches have gained an ever increasing attention from ... [more ▼]

During the last years, spectrum scarcity has become one of the major issues for the development of new communication systems. Cognitive Radio (CR) approaches have gained an ever increasing attention from system designers and operators, as they promise a more efficient utilization of the available spectral resources. In this context, while the application of CRs in terrestrial scenarios has been widely considered from both theoretical and practical viewpoints, their exploitation in satellite communications is still a rather unexplored area. In this paper, we address the definition of several satellite communications scenarios, where cognitive radio techniques promise to introduce significant benefits, and we discuss the major enablers and the associated challenges [less ▲]

Detailed reference viewed: 205 (7 UL)