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 app...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/910 |
| _version_ | 1797437246026022912 |
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| author | Daniel Gaetano Riviello Riccardo Tuninato Elisa Zimaglia Roberto Fantini Roberto Garello |
| author_facet | Daniel Gaetano Riviello Riccardo Tuninato Elisa Zimaglia Roberto Fantini Roberto Garello |
| author_sort | Daniel Gaetano Riviello |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:16:04Z |
| format | Article |
| id | doaj.art-94d4720b0ea54497bfc6a045de25419d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:16:04Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-94d4720b0ea54497bfc6a045de25419d2023-12-01T00:29:39ZengMDPI AGSensors1424-82202023-01-0123291010.3390/s23020910Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level SimulatorDaniel Gaetano Riviello0Riccardo Tuninato1Elisa Zimaglia2Roberto Fantini3Roberto Garello4Department of Electrical, Electronic, and Information Engineering, University of Bologna, 40136 Bologna, ItalyDepartment of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Torino, ItalyTIM S.p.A., 10148 Torino, ItalyTIM S.p.A., 10148 Torino, ItalyDepartment of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Torino, ItalyAdvances 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.https://www.mdpi.com/1424-8220/23/2/9105GNew Radiodeep learningconvolutional neural networkCSI reporting |
| spellingShingle | Daniel Gaetano Riviello Riccardo Tuninato Elisa Zimaglia Roberto Fantini Roberto Garello Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator Sensors 5G New Radio deep learning convolutional neural network CSI reporting |
| title | Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator |
| title_full | Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator |
| title_fullStr | Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator |
| title_full_unstemmed | Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator |
| title_short | Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator |
| title_sort | implementation of deep learning based csi feedback reporting on 5g nr compliant link level simulator |
| topic | 5G New Radio deep learning convolutional neural network CSI reporting |
| url | https://www.mdpi.com/1424-8220/23/2/910 |
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