
浏览全部资源
扫码关注微信
1. 中信科移动通信技术股份有限公司,北京 100083
2. 无线移动通信国家重点实验室(电信科学技术研究院有限公司),北京 100191
3. 中国信息通信研究院,北京 100191
Published Online:2022-03,
Published:20 March 2022
移动端阅览
Qiuping HUANG, Xiaofeng LIU, Qiubin GAO, et al. Study of channel state information feedback based on artificial intelligence[J]. Telecommunications science, 2022, 38(3): 74-83.
Qiuping HUANG, Xiaofeng LIU, Qiubin GAO, et al. Study of channel state information feedback based on artificial intelligence[J]. Telecommunications science, 2022, 38(3): 74-83. DOI: 10.11959/j.issn.1000-0801.2022051.
信道状态信息(channel state information,CSI)的精确获取是大规模天线发挥效能的关键。在现有的通信系统中,上下行链路互易性不理想时,基于码本进行下行链路的 CSI 反馈。随着天线规模的增大,码本CSI反馈所需要的开销也越来越大。给出了基于人工智能(artificial intelligence,AI)的CSI反馈压缩方法,分析了基于AI的CSI反馈的标准化影响、通信流程与面临的挑战,提供了评估结果。评估结果表明,相对于基于频域基向量压缩的码本CSI反馈,基于AI的CSI反馈在相同的反馈精度下可以显著地降低反馈开销。
Accurate acquisition of CSI (channel state information) is the key to the performance of massive MIMO.In current communication systems
when the reciprocity of uplink and downlink is not ideal
codebook-based CSI feedback is used for downlink CSI acquisition.With the increase of antenna scale
codebook-based CSI feedback needs more and more overhead.The CSI feedback compression method based on AI (artificial intelligence) was presented
and the standardization impact
communication process and challenges of CSI feedback based on AI were analyzed.Besides
evaluation results were provided.The evaluation results show that compared with codebook-based CSI feedback based on frequency domain basis vector compression
CSI feedback based on AI can significantly reduce the feedback cost at the same feedback accuracy.
CHATAUT R , AKL R . Massive MIMO systems for 5G and beyond networks-overview,recent trends,challenges,and future research direction [J ] . Sensors (Basel,Switzerland) , 2020 , 20 ( 10 ): 2753 .
BJORNSON E , VAN DER PERRE L , BUZZI S , et al . Massive MIMO in sub-6 GHz and mmWave:physical,practical,and use-case differences [J ] . IEEE Wireless Communications , 2019 , 26 ( 2 ): 100 - 108 .
陈山枝 , 孙韶辉 , 苏昕 , 等 . 大规模天线波束赋形技术原理与设计 [M ] . 北京:人民邮电出版社 , 2019 .
CHEN S Z , SUN S H , SU X , et al . Principles and design of massive beamforming technology [M ] . Beijing : Posts & Telecom Press , 2019 .
3GPP . NR; Physical layer procedures for data (V17.0.0):TS 38.214 [S ] . 2021 .
KUO P H , KUNG H T , TING P G . Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays [C ] // Proceedings of 2012 IEEE Wireless Communications and Networking Conference . Piscataway:IEEE Press , 2012 : 492 - 497 .
SIM M S , PARK J , CHAE C B , et al . Compressed channel feedback for correlated massive MIMO systems [J ] . Journal of Communications and Networks , 2016 , 18 ( 1 ): 95 - 104 .
GAO Z , DAI L L , WANG Z C , et al . Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO [J ] . IEEE Transactions on Signal Processing , 2015 , 63 ( 23 ): 6169 - 6183 .
CHEON H , PARK B , HONG D . Adaptive multicarrier system with reduced feedback information in wideband radio channels [C ] // Proceedings of Gateway to 21st Century Communications Village,IEEE VTS 50th Vehicular Technology Conference (Cat.No.99CH36324) . Piscataway:IEEE Press , 1999 : 2880 - 2884 .
YE H , LI G Y , JUANG B H . Power of deep learning for channel estimation and signal detection in OFDM systems [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 1 ): 114 - 117 .
YANG Y W , GAO F F , LI G Y , et al . Deep learning-based downlink channel prediction for FDD massive MIMO system [J ] . IEEE Communications Letters , 2019 , 23 ( 11 ): 1994 - 1998 .
KANG J M , CHUN C J , KIM I M . Deep-learning-based channel estimation for wireless energy transfer [J ] . IEEE Communications Letters , 2018 , 22 ( 11 ): 2310 - 2313 .
GUO J J , WEN C K , JIN S , et al . Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback:design,simulation,and analysis [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 4 ): 2827 - 2840 .
HSIEH C H , CHEN J Y , NIEN B H . Deep learning-based indoor localization using received signal strength and channel state information [J ] . IEEE Access , 2019 ( 17 ): 33256 - 33267 .
WANG T Q , WEN C K , WANG H Q , et al . Deep learning for wireless physical layer:opportunities and challenges [J ] . China Communications , 2017 , 14 ( 11 ): 92 - 111 .
LU Z L , WANG J T , SONG J . Multi-resolution CSI feedback with deep learning in massive MIMO system [C ] // Proceedings of ICC 2020 - 2020 IEEE International Conference on Communications . Piscataway:IEEE Press , 2020 : 1 - 6 .
LU Z L , WANG J T , SONG J . Binary neural network aided CSI feedback in massive MIMO system [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 6 ): 1305 - 1308 .
ZHANG Y Y , ZHANG X C , LIU Y . Deep learning based CSI compression and quantization with high compression ratios in FDD massive MIMO systems [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 10 ): 2101 - 2105 .
LIU Z Y , DEL ROSARIO M , DING Z . A Markovian model-driven deep learning framework for massive MIMO CSI feedback [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 2 ): 1214 - 1228 .
CAO Z , SHIH W T , GUO J J , et al . Lightweight convolutional neural networks for CSI feedback in massive MIMO [J ] . IEEE Communications Letters , 2021 , 25 ( 8 ): 2624 - 2628 .
GAO M , LIAO T M , LU Y B . Fully connected feed forward neural networks based CSI feedback algorithm [J ] . China Communications , 2021 , 18 ( 1 ): 43 - 48 .
3GPP . RP-213560:New SI:study on artificial intelligence (AI)/machine learning (ML) for NR air interface [S ] . 2021 .
WEN C K , SHIH W T , JIN S . Deep learning for massive MIMO CSI feedback [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 5 ): 748 - 751 .
LIU Z Y , ZHANG L , DING Z . Exploiting Bi-directional channel reciprocity in deep learning for low rate massive MIMO CSI feedback [J ] . IEEE Wireless Communications Letters , 2019 , 8 ( 3 ): 889 - 892 .
WANG T Q , WEN C K , JIN S , et al . Deep learning-based CSI feedback approach for time-varying massive MIMO channels [J ] . IEEE Wireless Communications Letters , 2019 , 8 ( 2 ): 416 - 419 .
LU C , XU W , SHEN H , et al . MIMO channel information feedback using deep recurrent network [J ] . IEEE Communications Letters , 2019 , 23 ( 1 ): 188 - 191 .
0
Views
612
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621