Schynol, Lukas ; Pesavento, Marius (2022)
Deep Unfolding in Multicell MU-MIMO.
30th European Signal Processing Conference (EUSIPCO 2022). Belgrad, Serbia (29.08.2022-02.09.2022)
doi: 10.23919/EUSIPCO55093.2022.9909892
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The weighted sum-rate maximization in coordinated multicell MIMO networks with intra- and intercell interference and local channel state at the base stations is considered. Based on the concept of unrolling applied to the classical weighted minimum mean squared error (WMMSE) algorithm and ideas from graph signal processing, we present the GCN-WMMSE deep network architecture for transceiver design in multicell MU-MIMO interference channels with local channel state information. Similar to the original WMMSE algorithm it facilitates a distributed implementation in multicell networks. However, GCN-WMMSE significantly accelerates the convergence and con-sequently alleviates the communication overhead in a distributed deployment. Additionally, the architecture is agnostic to different wireless network topologies while exhibiting a low number of trainable parameters and high efficiency w.r.t. training data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Schynol, Lukas ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | Deep Unfolding in Multicell MU-MIMO |
| Sprache: | Englisch |
| Publikationsjahr: | 18 Oktober 2022 |
| Verlag: | IEEE |
| Buchtitel: | 30th European Signal Processing Conference (EUSIPCO 2022): Proceedings |
| Veranstaltungstitel: | 30th European Signal Processing Conference (EUSIPCO 2022) |
| Veranstaltungsort: | Belgrad, Serbia |
| Veranstaltungsdatum: | 29.08.2022-02.09.2022 |
| DOI: | 10.23919/EUSIPCO55093.2022.9909892 |
| Kurzbeschreibung (Abstract): | The weighted sum-rate maximization in coordinated multicell MIMO networks with intra- and intercell interference and local channel state at the base stations is considered. Based on the concept of unrolling applied to the classical weighted minimum mean squared error (WMMSE) algorithm and ideas from graph signal processing, we present the GCN-WMMSE deep network architecture for transceiver design in multicell MU-MIMO interference channels with local channel state information. Similar to the original WMMSE algorithm it facilitates a distributed implementation in multicell networks. However, GCN-WMMSE significantly accelerates the convergence and con-sequently alleviates the communication overhead in a distributed deployment. Additionally, the architecture is agnostic to different wireless network topologies while exhibiting a low number of trainable parameters and high efficiency w.r.t. training data. |
| Freie Schlagworte: | Wireless networks, Signal processing algorithms, Training data, Signal processing, Network architecture, Transceivers, Topology |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme |
| Hinterlegungsdatum: | 16 Jan 2025 12:19 |
| Letzte Änderung: | 16 Jan 2025 12:19 |
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