File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Hybrid Precoding with a Fully-Parallel Large-Scale Analog RRAM Array for 5G/6G MIMO Communication System

TitleHybrid Precoding with a Fully-Parallel Large-Scale Analog RRAM Array for 5G/6G MIMO Communication System
Authors
Issue Date2022
Citation
Technical Digest - International Electron Devices Meeting, IEDM, 2022, v. 2022-December, p. 3321-3324 How to Cite?
AbstractFor the first time, an energy-efficient hybrid precoding with computing-in-memory technology for 5G/6G MIMO communication system is demonstrated. To meet the requirement of massive matrix multiplication for MIMO signal processing, we realize the first fully-parallel large-scale (128K) analog resistive switching RRAM array. In order to address the IR-drop issue caused by both interconnect resistance and peripheral circuit in the fully-parallel large-scale arrays, a compact model and corresponding compensation scheme are proposed. To address the computation complexity challenge of hybrid precoding, we design a new CIM-compatible analog precoding scheme and mapping strategy. The demonstrated CIM-based hybrid precoding system achieves FPGA-comparable sum rates (13.17 bps/Hz @ 0dB SNR) and 20.2 × higher energy efficiency than FPGA. This work explores the feasibility of in-MIMO hybrid precoding with analog RRAM for future 5G/6G high-speed communication systems.
Persistent Identifierhttp://hdl.handle.net/10722/334897
ISSN
2023 SCImago Journal Rankings: 1.047
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQin, Qi-
dc.contributor.authorGao, Bin-
dc.contributor.authorLiu, Qi-
dc.contributor.authorLiu, Zhengwu-
dc.contributor.authorLin, Yudeng-
dc.contributor.authorYao, Peng-
dc.contributor.authorZhou, Ying-
dc.contributor.authorYu, Ruihua-
dc.contributor.authorHao, Zhenqi-
dc.contributor.authorTang, Jianshi-
dc.contributor.authorZhang, Qingtian-
dc.contributor.authorDai, Linglong-
dc.contributor.authorSu, Zhiqiang-
dc.contributor.authorXu, Qingqing-
dc.contributor.authorYou, Shujuan-
dc.contributor.authorWu, Huaqiang-
dc.contributor.authorQian, He-
dc.date.accessioned2023-10-20T06:51:34Z-
dc.date.available2023-10-20T06:51:34Z-
dc.date.issued2022-
dc.identifier.citationTechnical Digest - International Electron Devices Meeting, IEDM, 2022, v. 2022-December, p. 3321-3324-
dc.identifier.issn0163-1918-
dc.identifier.urihttp://hdl.handle.net/10722/334897-
dc.description.abstractFor the first time, an energy-efficient hybrid precoding with computing-in-memory technology for 5G/6G MIMO communication system is demonstrated. To meet the requirement of massive matrix multiplication for MIMO signal processing, we realize the first fully-parallel large-scale (128K) analog resistive switching RRAM array. In order to address the IR-drop issue caused by both interconnect resistance and peripheral circuit in the fully-parallel large-scale arrays, a compact model and corresponding compensation scheme are proposed. To address the computation complexity challenge of hybrid precoding, we design a new CIM-compatible analog precoding scheme and mapping strategy. The demonstrated CIM-based hybrid precoding system achieves FPGA-comparable sum rates (13.17 bps/Hz @ 0dB SNR) and 20.2 × higher energy efficiency than FPGA. This work explores the feasibility of in-MIMO hybrid precoding with analog RRAM for future 5G/6G high-speed communication systems.-
dc.languageeng-
dc.relation.ispartofTechnical Digest - International Electron Devices Meeting, IEDM-
dc.titleHybrid Precoding with a Fully-Parallel Large-Scale Analog RRAM Array for 5G/6G MIMO Communication System-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IEDM45625.2022.10019426-
dc.identifier.scopuseid_2-s2.0-85147520267-
dc.identifier.volume2022-December-
dc.identifier.spage3321-
dc.identifier.epage3324-
dc.identifier.isiWOS:000968800700083-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats