Robust Hybrid Beamforming with Quantized Deep Neural Networks
| dc.contributor.author | Elbir, Ahmet Musab | |
| dc.contributor.author | Mishra, Kumar Vijay | |
| dc.date.accessioned | 2020-04-30T13:33:18Z | |
| dc.date.available | 2020-04-30T13:33:18Z | |
| dc.date.issued | 2019 | |
| dc.department | DÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| dc.description | 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 -- 13 October 2019 through 16 October 2019 -- 155874 | en_US |
| dc.description.abstract | Hybrid beamforming is integral to massive multiple-input multiple-output (MIMO) communications in reducing the training overhead and hardware cost associated with large antenna arrays. Prior works have employed optimization and greedy search to jointly estimate the precoder and combiner weights. High computational complexity of these methods apart, their performance strongly relies on accurate channel information. In this paper, we propose a computationally efficient, deep learning approach that also provides robust performance against the deviations in the channel characteristics. Further, we employ a convolutional neural network with quantized weights (Q-CNN) so that it is deployable in mobile devices that have less memory resources and low overhead requirements. We show that the proposed Q-CNN, saved in at least 6 bits, yields superior performance over conventional massive MIMO hybrid beamforming. © 2019 IEEE. | en_US |
| dc.identifier.doi | 10.1109/MLSP.2019.8918866 | en_US |
| dc.identifier.isbn | 9781728108247 | |
| dc.identifier.issn | 2161-0363 | |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://dx.doi.org/10.1109/MLSP.2019.8918866 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12684/604 | |
| dc.identifier.volume | 2019-October | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE Computer Society | en_US |
| dc.relation.ispartof | IEEE International Workshop on Machine Learning for Signal Processing, MLSP | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Convolutional neural networks; deep learning; hybrid beamforming; massive MIMO; quantization | en_US |
| dc.title | Robust Hybrid Beamforming with Quantized Deep Neural Networks | en_US |
| dc.type | Conference Object | en_US |
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