【一点资讯】中国指挥与控制学会

本文作者

尤肖虎：东南大学教授，移动通信国家重点实验室主任

张 川：东南大学信息科学与工程学院副教授

金 石：东南大学教授，博导，国家自然科学基金杰出青年科学基金获得者

邬贺铨：中国工程院院士

来源：5G

参考文献：

1 尤肖虎，潘志文尤肖虎 举报，高西奇，等. 5G移动通信发展趋势与若干关键技术. 中国科学：信息科学，2014, 44 (5):551-563

2 3GPP. RP-170741, Way Forward on the Overall 5G-NR eMBB. Workplan[s]. 2017

3 3GPP. Study on New Radio Access Technology: Radio Access Architecture and Interfaces (Release 14). TR38.801, v14.0[s]. 2017

4 ITU-R. Minimum Requirements Related to Technical Performance for IMT2020 Radio Interface(s). Report ITU-R M.2410-0. 2017

5 3GPP. Release-15: LTE Enhancements and 5G Normative Work. 2018

6 You X H, Wang D M, Sheng B, et al. Cooperative distributed antenna systems for mobile communications. IEEE Wirel Commun, 2010, 17(3): 35

7 Yang W J, Wang M, Zhang J J, et al. Narrowband wireless access for low-power massive Internet of things: A bandwidth perspective. IEEE Wirel Commun, 2017, 24(3): 138-145

8 Whitley D. A genetic algorithm tutorial. Stat Comput, 1994, 4(2): 65-85

9 Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw, 2015, 61: 85-117

[3] wang z尤肖虎 举报， bovik a c， sheikh h r， etal. image quality assessment： from error visibility to structural similarity [j]. ieee transactions on image processing， 2004 13（4）： 600-612.。error: plugin load for plugin 'decodeutfkeys' failed with return code: error: can't locate uri/escape.pm in @inc (@inc contains: /etc/perl /usr/local/lib/perl/5.10.1 /usr/local/share/perl/5.10.1 /usr/lib/perl5 /usr/share/perl5 /usr/lib/perl/5.10 /usr/share/perl/5.10 /usr/local/lib/site_perl . /usr/share/awstats/lib /usr/share/awstats/plugins) at (eval 3) line 1.。cellulosegold bullcomput-aided civ infj biomed mater res aj supercrit fluidieee t neural networ。

11 Yu (You) X H, Chen G A, Cheng S X. Dynamic learning rate optimization of the backpropagation algorithm. IEEE T Neural Networ, 1995, 6(3): 669-677

12 Yu (You) X H, Chen G A. Efficient backpropagation learning using optimal learning rate and momentum. Neural Netw, 1997, 10(3): 517-527

13 Kaelbling L P, Littman M L, Moore A W. Reinforcement learning: A survey. J Artif Intell Res, 1996, 4: 237-285

14 Watkins C J C H, Dayan P. Q-learning. Mach Learn, 1992, 8(3-4): 279-292

15 Gatherer A. Machine learning Modems: How ML will change how we specify and design next generation communication systems. IEEE ComSoc News, 2018.

16 Yang C, Xu W H, Zhang Z C, et al. A channel-blind detection for SCMA based on image processing techniques. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2018: 1-5

17 Zhang C, XuWH. Neural networks: Efficient implementations and applications. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017: 1029-1032

18 Xu W H, You X H, Zhang C. Efficient deep convolutional neural networks accelerator without multiplication and retraining. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018: 1-5

19 Xu W H, Wang Z F, You X H, et al. Efficient fast convolution architectures for convolutional neural network. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017: 904-907 20 Xu W H, Wu Z Z, Ueng Y L, et al. Improved polar decoder based on deep learning. In: Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS), 2017:1-6

21 Xu W H, Zhong Z W, Be’ery Y, et al. Joint neural network equalizer and decoder. In: Proceedings of IEEE International Symposium on Wireless Communication Systems (ISWCS), 2018:1-6

cao z, qin t, liu t y, et al. learning to rank: from pairwise approach to listwise approach[c]//proceedings of the 24th international conference on machine learning. acm, 2007: 129-136. 另外附上《tutorial-ltr by hang li》《tutorial-ltr by ty liu》。leng, j.,liao, j., huang, r. (2007). research on the application of content analysis to online learning,the proceedings of 10th iasted internationalconference on computers and advanced technology in education (cate 2007). calgary, ca: acta press.。zhuang y, chin w s, juan y c, et al. a fast parallel sgd for matrix factorization in shared memory systems[c]//proceedings of the 7th acm conference on recommender systems. acm, 2013: 249-256. recsys 2013 最佳论文，并行矩阵分解。

23 Xu W H, You X H, Zhang C. Using Fermat number transform to accelerate convolutional neural network. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017: 1033-1036

24 Gao X Q, Jiang B, Li X, et al. Statistical eigenmode transmission over jointly-correlated MIMO channels. IEEE T Inform Theory, 2009,55 (8): 3735-3750

25 Wang D M, Zhang Y, Wei H, et al. An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications. Sci China Inform Sci, 2016, 59(8): 081301

cooperative communication in multi-hop cognitive radio networks based on multicarrier modulation。（54）gloss: a social networks-based recommender system,gloss: a social networks-based recommender system,icpca2010,2010,第4作者。10.cairong zou, chengwei huang, dong han, li zhao. detecting practical speech emotion in a cognitive task, computer communications and networks , 2011 proceedings of 20th international conference on, maui, hi, usa, 2011。