注:本文的发布,已得到作者授权。
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
谈晓思:移动通信国家重点实验室(东南大学信息科学与工程学院)博士后
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
参考文献:
1 尤肖虎,潘志文,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
10 Yu (You) X H. Can backpropagation error surface not have local minima. IEEE T Neural Networ, 1992, 3(6): 1019-1021
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
22 Xu W H, Be’ery Y, You X H, et al. Polar decoding on sparse graphs with deep learning. In: Proceedings of Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2018:1-6
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
26 Gesbert D, Hanly S, Huang H, et al. Multi-cell MIMO cooperative networks: A new look at interference. IEEE J Sel Area Comm, 2010, 28(9): 1380-1408
27 Jing S S, Yu A L, Liang X, et al. Uniform belief propagation processor for massive MIMO detection and GF (2n) LDPC decoding. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017: 961-964
28 Gandhi V S, Maheswaran B. A cross layer design for performance enhancements in LTE-A system. In: Proceedings of IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016: 905-909
29 Kuen J, Kong X F, Wang G, et al. DelugeNets: Deep networks with efficient and flexible cross-layer information inflows. In: Proceedings of IEEE International Conference on Computer Vision Workshop (ICCVW), 2017: 958-966
30 Farsad N, Rao M, Goldsmith A. Deep learning for joint source-channel coding of text. arXiv preprint, 2018.
31 Xu X W, Ding Y K, Hu S X, et al. Scaling for edge inference of deep neural networks. Nature Electronics, 2018, 1(4):216
32 Wang X F, Li X H, Leung V C M. Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges. IEEE Access, 2015, 3: 1379-1391
33 Klaine P V, Imran M A, Onireti O, et al. A Survey of machine learning techniques applied to self-organizing cellular networks. IEEE Commun Surv Tut, 2017, 19(4): 2392-2431
34 P`erez-Romero J, Sallent O, Ferr ú s R, et al. Knowledge-based 5G radio access network planning and optimization. In: Proceedings of IEEE International Symposium on Wireless Communication Systems (ISWCS), 2016: 359-365
35 G′omez-Andrades A, Munoz P, Serrano I, et al. Automatic root cause analysis for LTE networks based on unsupervised techniques. IEEE T Veh Technol, 2016, 65(4): 2369-2386
36 Wang J H, Guan W, Huang Y M, et al. Distributed optimization of hierarchical small cell networks: A GNEP framework. IEEE J Sel Area Comm, 2017, 35(2): 249-264
37 Ren Y R, Zhang C, Liu X, et al. Efficient early termination schemes for belief-propagation decoding of polar codes. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2015: 1-4
38 Fossorier M P C, Mihaljevic M, Imai H. Reduced complexity iterative decoding of low-density parity check codes based on belief propagation. IEEE T Commun, 1999, 47(5): 673-680
39 Yang J M, Song W Q, Zhang S Q, et al. Low-complexity belief propagation detection for correlated large-scale MIMO systems. J Signal Process Sys, 2018, 90(4): 585-599
40 Liu L, Yuen C, Guan Y L, et al. Gaussian message passing iterative detection for MIMO-NOMA systems with massive access. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), 2016: 1-6
41 Yang J M, Zhang C, Zhou H Y, et al. Pipelined belief propagation polar decoders. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2016: 413-416
42 Tan X S, Xu W H, Be’ery Y, et al. Improving massive MIMO belief propagation detector with deep neural network.
43 Liang F, Shen C, Wu F. An iterative BP-CNN architecture for channel decoding. IEEE J Sel Top Signa, 2018, 12(1): 144-159
44 Lv X Z, Wei P, Xiao X C. Automatic identification of digital modulation signals using high order cumulants. Electronic Warfare, 2004, 6:1
45 Wang T Q, Wen C K, Wang H Q, et al. Deep learning for wireless physical layer: Opportunities and challenges. China Commun, 2017, 14(11): 92-111
46 O’Shea T, Hoydis J. An introduction to deep learning for the physical layer. IEEE Trans on Cogn Commun Netw, 2017, 3(4): 563-575
47 O’Shea T J, Erpek T, Clancy T C. Deep learning based MIMO communications. arXiv preprint, 2017.
联系客服