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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Vydáno v:International Symposium on Wireless Communication Systems s. 1 - 6
Hlavní autoři: Danaee, Alireza, Hassanpour, Shayan, Wubben, Dirk, Dekorsy, Armin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 14.07.2024
Témata:
Cell-Free massive MIMO
Central Processing Unit
Compressors
distributed joint source-channel coding
Encoding
information bottleneck method
Information processing
Massive MIMO
mutual information
Numerical simulation
Wireless communication
ISSN:2154-0225
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Shrnutí: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.
ISSN:2154-0225
DOI:10.1109/ISWCS61526.2024.10639087