Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems
Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for a user equipment (UE) to achieve a high data rate. However, massive MIMO requires channel state information (CSI) at the transmitter and the CSI overhead fed back by UEs exponentially increases as the number...
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| Veröffentlicht in: | IEEE wireless communications letters 2021-12, Vol.10 (12), p.2776-2780 |
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| creator | Jo, Sanguk So, Jaewoo |
| description | Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for a user equipment (UE) to achieve a high data rate. However, massive MIMO requires channel state information (CSI) at the transmitter and the CSI overhead fed back by UEs exponentially increases as the number of antennas increases. In the last years, many studies have been conducted to solve the problem of enormous CSI feedback overhead by utilizing deep learning. In this letter, we propose an adaptive lightweight convolutional neural network (CNN) in the deep learning-based MIMO CSI feedback. The proposed network adaptively finds the compression ratio to be used in the network and reduces the computational complexity of the network. Simulation results show that the proposed lightweight CNN significantly reduces the computational complexity in comparison with the conventional CsiNet while achieving the equivalent performance; and moreover the proposed network converges faster. |
| doi_str_mv | 10.1109/LWC.2021.3117032 |
| format | Article |
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However, massive MIMO requires channel state information (CSI) at the transmitter and the CSI overhead fed back by UEs exponentially increases as the number of antennas increases. In the last years, many studies have been conducted to solve the problem of enormous CSI feedback overhead by utilizing deep learning. In this letter, we propose an adaptive lightweight convolutional neural network (CNN) in the deep learning-based MIMO CSI feedback. The proposed network adaptively finds the compression ratio to be used in the network and reduces the computational complexity of the network. Simulation results show that the proposed lightweight CNN significantly reduces the computational complexity in comparison with the conventional CsiNet while achieving the equivalent performance; and moreover the proposed network converges faster.</description><identifier>ISSN: 2162-2337</identifier><identifier>EISSN: 2162-2345</identifier><identifier>DOI: 10.1109/LWC.2021.3117032</identifier><identifier>CODEN: IWCLAF</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Artificial neural networks ; Codes ; Complexity ; Compression ratio ; Convolution ; convolutional neural network ; Convolutional neural networks ; Costs ; CSI feedback ; Decoding ; Deep learning ; Feedback ; learning-based feedback ; Lightweight ; Massive MIMO ; MIMO communication ; OFDM ; Wireless communication</subject><ispartof>IEEE wireless communications letters, 2021-12, Vol.10 (12), p.2776-2780</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-a5d083a95256dc5eed65d74fb4a60dc839ca84672c4e49c22d88a003719a72913</citedby><cites>FETCH-LOGICAL-c291t-a5d083a95256dc5eed65d74fb4a60dc839ca84672c4e49c22d88a003719a72913</cites><orcidid>0000-0001-7460-2491 ; 0000-0001-7868-7197</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9555820$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,794,27907,27908,54741</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9555820$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jo, Sanguk</creatorcontrib><creatorcontrib>So, Jaewoo</creatorcontrib><title>Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for a user equipment (UE) to achieve a high data rate. 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Simulation results show that the proposed lightweight CNN significantly reduces the computational complexity in comparison with the conventional CsiNet while achieving the equivalent performance; and moreover the proposed network converges faster.</description><subject>Artificial neural networks</subject><subject>Codes</subject><subject>Complexity</subject><subject>Compression ratio</subject><subject>Convolution</subject><subject>convolutional neural network</subject><subject>Convolutional neural networks</subject><subject>Costs</subject><subject>CSI feedback</subject><subject>Decoding</subject><subject>Deep learning</subject><subject>Feedback</subject><subject>learning-based feedback</subject><subject>Lightweight</subject><subject>Massive MIMO</subject><subject>MIMO communication</subject><subject>OFDM</subject><subject>Wireless communication</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kD1PwzAQhi0EElXpjsRiiTnFPscfGUugEClth4IYLdd2IIWSYqeg_nsSteoNdzc89570IHRNyZhSkt2Vb_kYCNAxo1QSBmdoAFRAAizl56edyUs0inFNuhKEAlUD9DBxZtvWvx6X9ftH--f7jvP5PLk30TucLws89d6tjP3EVRPwzMTY47NitsDLfWz9Jl6hi8p8RT86ziF6nT6-5M9JuXgq8kmZWMhomxjuiGIm48CFs7xLFdzJtFqlRhBnFcusUamQYFOfZhbAKWUIYZJmRnYJbIhuD7nb0PzsfGz1utmF7-6lBkGkklyKniIHyoYmxuArvQ31xoS9pkT3unSnS_e69FFXd3JzOKm99yc845wrIOwfZghiyQ</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Jo, Sanguk</creator><creator>So, Jaewoo</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-7460-2491</orcidid><orcidid>https://orcid.org/0000-0001-7868-7197</orcidid></search><sort><creationdate>20211201</creationdate><title>Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems</title><author>Jo, Sanguk ; So, Jaewoo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-a5d083a95256dc5eed65d74fb4a60dc839ca84672c4e49c22d88a003719a72913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Codes</topic><topic>Complexity</topic><topic>Compression ratio</topic><topic>Convolution</topic><topic>convolutional neural network</topic><topic>Convolutional neural networks</topic><topic>Costs</topic><topic>CSI feedback</topic><topic>Decoding</topic><topic>Deep learning</topic><topic>Feedback</topic><topic>learning-based feedback</topic><topic>Lightweight</topic><topic>Massive MIMO</topic><topic>MIMO communication</topic><topic>OFDM</topic><topic>Wireless communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jo, Sanguk</creatorcontrib><creatorcontrib>So, Jaewoo</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><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jo, Sanguk</au><au>So, Jaewoo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2021-12-01</date><risdate>2021</risdate><volume>10</volume><issue>12</issue><spage>2776</spage><epage>2780</epage><pages>2776-2780</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><abstract>Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for a user equipment (UE) to achieve a high data rate. However, massive MIMO requires channel state information (CSI) at the transmitter and the CSI overhead fed back by UEs exponentially increases as the number of antennas increases. In the last years, many studies have been conducted to solve the problem of enormous CSI feedback overhead by utilizing deep learning. In this letter, we propose an adaptive lightweight convolutional neural network (CNN) in the deep learning-based MIMO CSI feedback. The proposed network adaptively finds the compression ratio to be used in the network and reduces the computational complexity of the network. 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| subjects | Artificial neural networks Codes Complexity Compression ratio Convolution convolutional neural network Convolutional neural networks Costs CSI feedback Decoding Deep learning Feedback learning-based feedback Lightweight Massive MIMO MIMO communication OFDM Wireless communication |
| title | Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems |
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