A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model
In urban scenarios, network planning requires awareness of the notoriously complex propagation environment by accounting for blocking, diffraction, and reflection on buildings. To this end, deep learning-based signal-strength prediction directly operating on environmental data has recently gained at...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9954403/ |
| _version_ | 1797986809122127872 |
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| author | Lukas Eller Philipp Svoboda Markus Rupp |
| author_facet | Lukas Eller Philipp Svoboda Markus Rupp |
| author_sort | Lukas Eller |
| collection | DOAJ |
| description | In urban scenarios, network planning requires awareness of the notoriously complex propagation environment by accounting for blocking, diffraction, and reflection on buildings. To this end, deep learning-based signal-strength prediction directly operating on environmental data has recently gained attention, mainly as a computationally efficient alternative to ray-tracing. Our work combines RSRP measurements from an extensive drive-test campaign in a live 4G network with a 3D city model for the largest real-world assessment of such data-driven schemes to date. We compare three different encodings of the propagation environment and find that a neural network operating on a full 3D representation of the surroundings performs best with an RMSE of <inline-formula> <tex-math notation="LaTeX">$7.06~dB$ </tex-math></inline-formula>. It is followed by a model using only the direct path profile with <inline-formula> <tex-math notation="LaTeX">$7.78~dB$ </tex-math></inline-formula> and a reference neural network utilizing a binary line of sight indicator achieving <inline-formula> <tex-math notation="LaTeX">$8.76~dB$ </tex-math></inline-formula>. The large size of our data set allows us to address several open questions regarding the inner workings of these black box approaches. In particular, we elaborate on different evaluation strategies, highlighting the importance of spatial separation of train and test areas, as the rich environmental data implicitly provides a spatial reference. Through model explainability, we further identify the area along the direct path between the user equipment and the transmitter as the input region with the highest feature importance — questioning the common practice of including large buffer areas. Evaluating the models in scenarios with artificially placed base stations reveals that the measurement campaign offers a sufficient basis for a prototypical network planner. The trained models, which we make publicly available, exhibit the dominant propagation mechanisms in urban areas and generate spatially consistent and physically sound signal-strength maps. |
| first_indexed | 2024-04-11T07:38:50Z |
| format | Article |
| id | doaj.art-ca5395f2edaa4bb2bed78cd1ae4b70f6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:38:50Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ca5395f2edaa4bb2bed78cd1ae4b70f62022-12-22T04:36:39ZengIEEEIEEE Access2169-35362022-01-011012218212219610.1109/ACCESS.2022.32230979954403A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City ModelLukas Eller0https://orcid.org/0000-0002-1087-0953Philipp Svoboda1https://orcid.org/0000-0002-2277-0378Markus Rupp2https://orcid.org/0000-0001-9003-7779Institute of Telecommunications, TU Wien, Vienna, AustriaInstitute of Telecommunications, TU Wien, Vienna, AustriaInstitute of Telecommunications, TU Wien, Vienna, AustriaIn urban scenarios, network planning requires awareness of the notoriously complex propagation environment by accounting for blocking, diffraction, and reflection on buildings. To this end, deep learning-based signal-strength prediction directly operating on environmental data has recently gained attention, mainly as a computationally efficient alternative to ray-tracing. Our work combines RSRP measurements from an extensive drive-test campaign in a live 4G network with a 3D city model for the largest real-world assessment of such data-driven schemes to date. We compare three different encodings of the propagation environment and find that a neural network operating on a full 3D representation of the surroundings performs best with an RMSE of <inline-formula> <tex-math notation="LaTeX">$7.06~dB$ </tex-math></inline-formula>. It is followed by a model using only the direct path profile with <inline-formula> <tex-math notation="LaTeX">$7.78~dB$ </tex-math></inline-formula> and a reference neural network utilizing a binary line of sight indicator achieving <inline-formula> <tex-math notation="LaTeX">$8.76~dB$ </tex-math></inline-formula>. The large size of our data set allows us to address several open questions regarding the inner workings of these black box approaches. In particular, we elaborate on different evaluation strategies, highlighting the importance of spatial separation of train and test areas, as the rich environmental data implicitly provides a spatial reference. Through model explainability, we further identify the area along the direct path between the user equipment and the transmitter as the input region with the highest feature importance — questioning the common practice of including large buffer areas. Evaluating the models in scenarios with artificially placed base stations reveals that the measurement campaign offers a sufficient basis for a prototypical network planner. The trained models, which we make publicly available, exhibit the dominant propagation mechanisms in urban areas and generate spatially consistent and physically sound signal-strength maps.https://ieeexplore.ieee.org/document/9954403/5G6Gcellular network planningdeep learningdrive-testLTE |
| spellingShingle | Lukas Eller Philipp Svoboda Markus Rupp A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model IEEE Access 5G 6G cellular network planning deep learning drive-test LTE |
| title | A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model |
| title_full | A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model |
| title_fullStr | A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model |
| title_full_unstemmed | A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model |
| title_short | A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model |
| title_sort | deep learning network planner propagation modeling using real world measurements and a 3d city model |
| topic | 5G 6G cellular network planning deep learning drive-test LTE |
| url | https://ieeexplore.ieee.org/document/9954403/ |
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