Cascaded Deep Neural Network Based Adaptive Precoding for Distributed Massive MIMO Systems

Simple item page
dc.contributor.authorGe, L. J.
dc.contributor.authorNiu, S. X.
dc.contributor.authorShi, C. P.
dc.contributor.authorGuo, Y. C.
dc.contributor.authorChen, G. J.
dc.coverage.issue1cs
dc.coverage.volume33cs
dc.date.accessioned2024-05-28T12:43:34Z
dc.date.available2024-05-28T12:43:34Z
dc.date.issued2024-04cs
dc.description.abstractIn time-division duplex (TDD) distributed large-scale multiple input multiple output (DM-MIMO) systems, the traditional downlink channel precoding method is used to resist inter-user interference (IUI). However, when the Channel State Information (CSI) is incomplete, the performance loss is serious, not only the bit error rate is high, but also the complexity of the traditional precoding algorithm is high. In order to solve these problems, this paper proposes an adaptive precoding framework based on deep learning (DL) for joint training and split application deployment. First, we train a channel emulator deep neural network (CE-DNN) to learn and simulate the transmission process of the wireless communication channel. Then, we concatenate an untrained precoding DNN (P-DNN) with a trained CE-DNN and retrain the cascaded neural network to converge. The last step is to obtain the P-DNN, namely the adaptive precoding network, by dismantling the joint trained network. Simulation results show that, when CSI is imperfect, the proposed method is compared with Tomlinson-Harashima precoding (THP) and block diagonalization (BD) precoding. The proposed method has a lower mean square error (MSE) and higher spectrum efficiency, as well as a bit error rate (BER) performance close to the THP. The source codes and the neural network codes are available on request.en
dc.formattextcs
dc.format.extent34-44cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2024 vol. 33, iss. 1, s. 34-44. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2024.0034en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/245664
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2024/24_01_0034_0044.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectDistributed multiple-input multiple-output (D-MIMO)en
dc.subjectdeep neural networken
dc.subjectdownlink precodingen
dc.subjectchannel state information (CSI)en
dc.titleCascaded Deep Neural Network Based Adaptive Precoding for Distributed Massive MIMO Systemsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
24_01_0034_0044.pdf
Size:
635.63 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
2024/1