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dc.contributor.authorJeon, Charles
Li, Kaipeng
Cavallaro, Joseph R.
Studer, Christoph
dc.date.accessioned 2018-06-27T15:44:48Z
dc.date.available 2018-06-27T15:44:48Z
dc.date.issued 2017
dc.identifier.citation Jeon, Charles, Li, Kaipeng, Cavallaro, Joseph R., et al.. "On the achievable rates of decentralized equalization in massive MU-MIMO systems." 2017 IEEE International Symposium on Information Theory (ISIT), (2017) https://doi.org/10.1109/ISIT.2017.8006699.
dc.identifier.urihttps://hdl.handle.net/1911/102306
dc.description.abstract Massive multi-user (MU) multiple-input multiple-output (MIMO) promises significant gains in spectral efficiency compared to traditional, small-scale MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or minimum mean-square error (MMSE)-based methods, typically rely on centralized processing at the base station (BS), which results in (i) excessively high interconnect and chip input/output data rates, and (ii) high computational complexity. In this paper, we investigate the achievable rates of decentralized equalization that mitigates both of these issues. We consider two distinct BS architectures that partition the antenna array into clusters, each associated with independent radio-frequency chains and signal processing hardware, and the results of each cluster are fused in a feed forward network. For both architectures, we consider ZF, MMSE, and a novel, non-linear equalization algorithm that builds upon approximate message passing (AMP), and we theoretically analyze the achievable rates of these methods. Our results demonstrate that decentralized equalization with our AMP-based methods incurs no or only a negligible loss in terms of achievable rates compared to that of centralized solutions.
dc.language.iso eng
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE.
dc.title On the achievable rates of decentralized equalization in massive MU-MIMO systems
dc.type Journal article
dc.citation.journalTitle 2017 IEEE International Symposium on Information Theory (ISIT)
dc.contributor.publisher IEEE
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1109/ISIT.2017.8006699
dc.type.publication post-print


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