YONG LIAO, HAIMEI YAO, YUANXIAO HUA. Channel state information acquisition algorithm based on deep learning for IoT. [J]. Chinese journal on internet of things, 2019, 3(1): 8-13.
DOI:
YONG LIAO, HAIMEI YAO, YUANXIAO HUA. Channel state information acquisition algorithm based on deep learning for IoT. [J]. Chinese journal on internet of things, 2019, 3(1): 8-13. DOI: 10.11959/j.issn.2096-3750.2019.00085.
Channel state information acquisition algorithm based on deep learning for IoT
To solve the problem of high feedback overhead when the user sends channel state information (CSI) to the base station in massive multiple input multiple output (MIMO) based on Internet of things system
a CSI feedback network based on deep learning was proposed to feedback CSI.Firstly
the proposed network used convolutional neural network (CNN) to extract channel feature vectors and maxpooling to compress the data.Then the compressed CSI was decompressed by using full connection and CNN to restore the original channel.The simulation results show that compared with the existing CSI feedback methods
the CSI recovered by the proposed CSI feedback network is closer to the original channel
and the reconstruction quality is improved significantly.
关键词
大规模MIMO物联网CSI反馈深度学习
Keywords
massive MIMOInternet of thingsCSI feedbackdeep learning
HE H S . Application of mobile communication technology in Internet of things[J]. Information & Communications, 2017,5(173): 225-226.
ADAME T, BEL A, BELLALTA B ,et al. IEEE 802.11ah:the Wi-Fi approach for M2M communications[J]. IEEE Wireless Communications, 2015,21(6): 144-152.
LEE B M . Calibration for channel reciprocity in industrial massive MIMO antenna systems[J]. IEEE Transactions on Industrial Informatics, 2018,14(1): 221-230.
DING G, GAO X, XUE Z ,et al. Massive MIMO for distributed detection with transceiver impairments[J]. IEEE Transactions on Vehicular Technology, 2018,67(1): 604-617.
ZHANG Z, TEH K C, LI K H . Application of compressive sensing to limited feedback strategy in large-scale multiple-input single-output cellular networks[J]. IET Communications, 2014,8(6): 947-955.
GE A, ZHANG T, HU Z ,et al. Principal component analysis based limited feedback scheme for massive MIMO systems[C]// International Symposium on Personal,Indoor and Mobile Radio Communications. IEEE, 2016: 326-331.
GE A, ZHANG T, ZENG Z ,et al. PCA based limited feedback scheme for massive MIMO with kalman filter enhancing performance[C]// IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2015: 1-6.
NAGASHIMA R, OHTSUKI T, JIANG W ,et al. Channel prediction for massive MIMO with channel compression based on principal component analysis[C]// International Symposium on Personal,Indoor and Mobile Radio Communications. IEEE, 2016: 1-6.
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.
WANG T, WEN C K, JIN S ,et al. Deep learning-based CSI feedback approach for time-varying massive MIMO channels[J]. IEEE Wireless Communications Letters, 2018: 1-4.
SIM M S, PARK J, CHAE C ,et al. Compressed channel feedback for correlated massive MIMO systems[C]// 2014 IEEE Globecom Workshops (GC Wkshps). IEEE, 2014: 327-332.
TIAN R, LIANG Y, LI T . Overlapping user grouping in IoT oriented massive MIMO systems[C]// International Conference on Computing. IEEE, 2017: 255-259.