Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to achieve spatial diversity and multiplexing gains. In a frequency division duplex (FDD) multiuser massive MIMO network, each user needs to compress and feedback its downlink CSI to the BS. The CSI overhead scales with the number of antennas, users and subcarriers, and becomes a major bottleneck for the overall spectral efficiency. In this paper, we propose a deep learning (DL)-based CSI compression scheme, called DeepCMC, composed of convolutional layers followed by quantization and entropy coding blocks. In comparison with previous DL-based CSI reduction structures, DeepCMC proposes a novel fully-convolutional neural network (NN) architecture, with residual layers at the decoder, and incorporates quantization and entropy coding blocks into its design. DeepCMC is trained to minimize a weighted rate-distortion cost, which enables a trade-off between the CSI quality and its feedback overhead. Simulation results demonstrate that DeepCMC outperforms the state of the art CSI compression schemes in terms of the reconstruction quality of CSI for the same compression rate. We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner. Distributed DeepCMC not only utilizes the inherent CSI structures of a single MIMO user for compression, but also benefits from the correlations among the channel matrices of nearby users to further improve the performance in comparison with DeepCMC. We also propose a reduced-complexity training method for distributed DeepCMC, allowing to scale it to multiple users, and suggest a cluster-based distributed DeepCMC approach for practical implementation.
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback / Mashhadi, M. B.; Yang, Q.; Gunduz, D.. - In: IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS. - ISSN 1536-1276. - 20:4(2021), pp. 2621-2633. [10.1109/TWC.2020.3043502]
|Data di pubblicazione:||2021|
|Titolo:||Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback|
|Autore/i:||Mashhadi, M. B.; Yang, Q.; Gunduz, D.|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TWC.2020.3043502|
|Codice identificativo ISI:||WOS:000639747400032|
|Codice identificativo Scopus:||2-s2.0-85098775409|
|Citazione:||Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback / Mashhadi, M. B.; Yang, Q.; Gunduz, D.. - In: IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS. - ISSN 1536-1276. - 20:4(2021), pp. 2621-2633. [10.1109/TWC.2020.3043502]|
|Tipologia||Articolo su rivista|
File in questo prodotto:
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris