Deep Learning for Massive MIMO CSI Feedback
In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead....
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| Published in | IEEE wireless communications letters Vol. 7; no. 5; pp. 748 - 751 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain. |
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| AbstractList | In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain. |
| Author | Shih, Wan-Ting Wen, Chao-Kai Jin, Shi |
| Author_xml | – sequence: 1 givenname: Chao-Kai orcidid: 0000-0001-5952-232X surname: Wen fullname: Wen, Chao-Kai email: chaokai.wen@mail.nsysu.edu.tw organization: Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan – sequence: 2 givenname: Wan-Ting orcidid: 0000-0001-5411-5223 surname: Shih fullname: Shih, Wan-Ting email: sydney2317076@gmail.com organization: Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan – sequence: 3 givenname: Shi orcidid: 0000-0003-0271-6021 surname: Jin fullname: Jin, Shi email: jinshi@seu.edu.cn organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, China |
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| CODEN | IWCLAF |
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| Cites_doi | 10.1109/CVPR.2016.90 10.1002/cpa.20042 10.1109/TSP.2014.2324991 10.1109/TIT.2016.2556683 10.1109/JSAC.2013.130203 10.1109/JSTSP.2014.2313020 10.1109/TWC.2010.092810.091092 10.1109/MWC.2012.6393523 10.1109/ALLERTON.2017.8262812 10.1109/WCNC.2012.6214417 10.1073/pnas.0909892106 |
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| SubjectTerms | Beamforming Codes compressed sensing conventional neural network Decoding Deep learning Discrete Fourier transforms FDD Feedback Frequency division duplexing Image reconstruction Machine learning Massive MIMO MIMO communication Training Transformations Wireless communication |
| Title | Deep Learning for Massive MIMO CSI Feedback |
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