Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator

Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel

ENERGY RECOVERY FROM BIOMASS IN THE LANGHE AND ROERO DISTRICT

The decision to consider the biogas from the anaerobic digestion was taken considering that the use of other engineering solutions (i.e. incinerators, pyrolysis plants, large photovoltaic plants) could hardly find a positive response in the Langhe and Roero district since this area has strong agricultural and food traditions and a constant attention to the environmental preservation. In this frame it was decided not to consider other possible scenarios coming both from the use of so-called "energy crops" and from changes in land exploitation. Also livestock manure has not been taken into account to avoid resorting to alternative solutions for field fertilization. In such conditions, it is possible to recover 81,864 MWhe/y coming from landfilled MSW (11.0%), the organic fraction of the separated collection (0.4%), livestock effluents (43.2%), agricultural wastes (42.4%) and unused agricultural area exploitation (3.0%). Considering the local population (166,065 inhabitants) this amount becomes 0.49 MWhe/inhabitant·y that represents, almost the 50% of the domestic consumption

A deep learning-based approach to 5G-new radio channel estimation

In this paper, we present a deep learning-based technique for channel estimation. By treating the time-frequency grid of the channel response as a low-resolution 2D-image, we propose a 5G-New Radio Convolutional Neural Network, called NR-ChannelNet, which can be properly trained to predict the channel coefficients. Our study employs a 3GPP-compliant 5G-New Radio simulator that can reproduce a realistic scenario by including multiple transmitting/receiving antenna schemes and clustered delay line channel model. Simulation results show that our deep learning approach can achieve competitive performance with respect to traditional techniques such as 2D-MMSE: indeed, under certain conditions, our new NR-ChannelNet approach achieves remarkable gains in terms of throughput