Search Results for author: Marco Moretti

Found 7 papers, 1 papers with code

Joint OAM Radar-Communication Systems: Target Recognition and Beam Optimization

no code implementations11 May 2022 Wen-Xuan Long, Rui Chen, Marco Moretti, Wei zhang, Jiandong Li

In details, we first propose an OAM-based three-dimensional (3-D) super-resolution position estimation and rotation velocity detection method, which can accurately estimate the 3-D position and rotation velocity of multiple targets.

Position Super-Resolution

Radio-Frequency Multi-Mode OAM Detection Based on UCA Samples Learning

no code implementations29 Nov 2021 Jiabei Fan, Rui Chen, Wen-Xuan Long, Marco Moretti, Jiandong Li

Orbital angular momentum (OAM) at radio-frequency provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies.

Edge Computing and Communication for Energy-Efficient Earth Surveillance with LEO Satellites

no code implementations17 Nov 2021 Marc Martinez-Gost, Israel Leyva-Mayorga, Ana Pérez-Neira, Miguel Ángel Vázquez, Beatriz Soret, Marco Moretti

In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication.

Edge-computing Management

AoA Estimation for OAM Communication Systems With Mode-Frequency Multi-Time ESPRIT Method

no code implementations18 Oct 2021 Wen-Xuan Long, Rui Chen, Marco Moretti, Jiandong Li

Radio orbital angular momentum (OAM) communications require accurate alignment between the transmit and receive beam directions.

Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services

no code implementations18 Mar 2021 Sergio Martiradonna, Andrea Abrardo, Marco Moretti, Giuseppe Piro, Gennaro Boggia

The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming latency-sensitive applications.

Autonomous Driving Cloud Computing +3

Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

1 code implementation2 Mar 2021 Fabio Saggese, Luca Pasqualini, Marco Moretti, Andrea Abrardo

Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes.

Management reinforcement-learning +1

WMMSE resource allocation for NOMA-FD

no code implementations16 May 2019 Andrea Abrardo, Marco Moretti, Fabio Saggese

Power and channel allocation in interference-limited systems is a key enabler for beyond 5G (B5G) technologies, such as multi-carrier full duplex non-orthogonal multiple access (FD-NOMA).

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