Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming
Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization....
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Veröffentlicht in: | IEEE wireless communications letters 2024-09, p.1-1 |
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creator | Zhang, Cheng Xiong, Shuangbo He, Mengqing Wei, Lan Huang, Yongming Zhang, Wei |
description | Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework. |
doi_str_mv | 10.1109/LWC.2024.3466117 |
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In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. 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In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework.</description><subject>Array signal processing</subject><subject>conditional variational auto-encoder (CVAE)</subject><subject>hybrid beamforming</subject><subject>MIMO communication</subject><subject>mixed density network (MDN)</subject><subject>Numerical models</subject><subject>Optimization</subject><subject>probing beam</subject><subject>Radio frequency</subject><subject>Training</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1Lw0AQhhdRsNTePXjYP5C4s5tMkqMG2wqB9lD0GDabiazko2yKUn-9m7aIc5kZ3o_Dw9g9iBBAZI_Fex5KIaNQRYgAyRWbSUAZSBXF13-3Sm7ZYhw_hR8UICGdsbcV9eT0wX4RL0i73vYffDt8k6Oab91QTf8z6Y5v9gfb2R9vHXreDI7n1LbB0hHx9bFytj7ZvND5yB27aXQ70uKy52y3fNnl66DYrF7zpyIwKCHQRpjaZBlEUgqMjaA41aYWmKm0qVDoGBDR600cQZ3KKkkSqhuZxBUqXaVqzsS51rhhHB015d7ZTrtjCaKcyJSeTDmRKS9kfOThHLFE9M-OaaYiUL8nDF7q</recordid><startdate>20240921</startdate><enddate>20240921</enddate><creator>Zhang, Cheng</creator><creator>Xiong, Shuangbo</creator><creator>He, Mengqing</creator><creator>Wei, Lan</creator><creator>Huang, Yongming</creator><creator>Zhang, Wei</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-3616-4616</orcidid><orcidid>https://orcid.org/0009-0005-4707-8504</orcidid><orcidid>https://orcid.org/0000-0002-1059-3642</orcidid><orcidid>https://orcid.org/0000-0003-2663-4207</orcidid></search><sort><creationdate>20240921</creationdate><title>Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming</title><author>Zhang, Cheng ; Xiong, Shuangbo ; He, Mengqing ; Wei, Lan ; Huang, Yongming ; Zhang, Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c621-ac0cdc991422065c0e58acd06938fb60a51666991f541d82b777edf275b63ab83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Array signal processing</topic><topic>conditional variational auto-encoder (CVAE)</topic><topic>hybrid beamforming</topic><topic>MIMO communication</topic><topic>mixed density network (MDN)</topic><topic>Numerical models</topic><topic>Optimization</topic><topic>probing beam</topic><topic>Radio frequency</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Cheng</creatorcontrib><creatorcontrib>Xiong, Shuangbo</creatorcontrib><creatorcontrib>He, Mengqing</creatorcontrib><creatorcontrib>Wei, Lan</creatorcontrib><creatorcontrib>Huang, Yongming</creatorcontrib><creatorcontrib>Zhang, Wei</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Cheng</au><au>Xiong, Shuangbo</au><au>He, Mengqing</au><au>Wei, Lan</au><au>Huang, Yongming</au><au>Zhang, Wei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2024-09-21</date><risdate>2024</risdate><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><abstract>Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework.</abstract><pub>IEEE</pub><doi>10.1109/LWC.2024.3466117</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-3616-4616</orcidid><orcidid>https://orcid.org/0009-0005-4707-8504</orcidid><orcidid>https://orcid.org/0000-0002-1059-3642</orcidid><orcidid>https://orcid.org/0000-0003-2663-4207</orcidid></addata></record> |
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subjects | Array signal processing conditional variational auto-encoder (CVAE) hybrid beamforming MIMO communication mixed density network (MDN) Numerical models Optimization probing beam Radio frequency Training |
title | Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming |
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