Relevance-Based Information Processing for Fronthaul Rate Reduction in Cell-Free MIMO Systems
Consider a user equipment in a Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) system that is served by several Radio Access Points (RAPs). In the uplink of this setup, these RAPs receive noisy observations of the user/source signal and must locally compress their signals before forwardi...
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| Veröffentlicht in: | International Symposium on Wireless Communication Systems S. 1 - 6 |
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| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
14.07.2024
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| Schlagworte: | |
| ISSN: | 2154-0225 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Consider a user equipment in a Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) system that is served by several Radio Access Points (RAPs). In the uplink of this setup, these RAPs receive noisy observations of the user/source signal and must locally compress their signals before forwarding them to the Central Processing Unit (CPU) through multiple rate-limited fronthaul channels. To retrieve the source signal at CPU, we are interested in maximizing the Mutual Information (MI) between the received signals at CPU and the user/source signal, and purposefully choose the Information Bottleneck (IB)-based compression techniques to design the quantizers at RAPs. We consider both separate and joint designs of the local compressors by establishing basic trade-offs between the informativity and compactness of the outcomes. For the joint design, two different schemes are presented, based on whether to leverage the side-information at CPU. Finally, the effectiveness of both compression schemes will be shown as well by means of numerical investigations over typical digital data transmission scenarios. |
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| ISSN: | 2154-0225 |
| DOI: | 10.1109/ISWCS61526.2024.10639087 |