Real-World Wireless Network Modeling and Optimization: From Model/Data-Driven Perspective

LI Yang; ZHANG Shutao; REN Xiaohui; ZHU Jianhang; HUANG Jiajie; HE Pengcheng; SHEN Kaiming; YAO Zhiqiang; GONG Jie; CHANG Tsunghui; SHI Qingjiang; LUO Zhiquan

doi:10.1049/cje.2022.00.191

Volume 31 Issue 6

Nov. 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(6): 991-1012

LI Yang, ZHANG Shutao, REN Xiaohui, et al., “Real-World Wireless Network Modeling and Optimization: From Model/Data-Driven Perspective,” Chinese Journal of Electronics, vol. 31, no. 6, pp. 991-1012, 2022, doi: 10.1049/cje.2022.00.191

Citation:

LI Yang, ZHANG Shutao, REN Xiaohui, et al., “Real-World Wireless Network Modeling and Optimization: From Model/Data-Driven Perspective,” Chinese Journal of Electronics, vol. 31, no. 6, pp. 991-1012, 2022, doi: 10.1049/cje.2022.00.191

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Real-World Wireless Network Modeling and Optimization: From Model/Data-Driven Perspective

doi: 10.1049/cje.2022.00.191

LI Yang^{1, 2
,},
ZHANG Shutao^{3, 1
,},
REN Xiaohui^4
,,
ZHU Jianhang^5
,,
HUANG Jiajie^5
,,
HE Pengcheng^6
,,
SHEN Kaiming^3
,,
YAO Zhiqiang^4
,,
GONG Jie^5
,,
CHANG Tsunghui^{3, 1
,},
SHI Qingjiang^{6, 1
,},
LUO Zhiquan^{3, 1, 2
,}

1.
Shenzhen Research Institute of Big Data, Shenzhen 518172, China
2.
Pengcheng Lab, Shenzhen 518055, China
3.
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China
4.
College of Mathematics and Computational Sciences, Xiangtan University, Xiangtan 411105, China
5.
School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
6.
School of Software Engineering, Tongji University, Shanghai 201804, China

Funds: This work was supported by the National Key R&D Program of China (2022YFA1003900), the National Natural Science Foundation of China (62101349, 62001411, 62171481), the Special Support Program of Guangdong (2019TQ05X150), and the Natural Science Foundation of Guangdong Province (2021A1515011124)

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Author Bio:
Yang LI received the Ph.D. degree in Department of Electrical and Electronic Engineering from The University of Hong Kong in 2019. From 2019 to 2020, he has been a Senior Research Engineer in Huawei Noah’s Ark Lab. He is the winner of the 2020 Innovation Pioneer Award of Huawei. Currently, he is a Research Scientist with Shenzhen Research Institute of Big Data. His research interests include radio resource management, learning to optimize, and large-scale optimization. (Email: liyang@sribd.cn)

Shutao ZHANG received the B.E. degree in communication engineering and the M.E. degree in electronics and communication engineering from Sun Yat-sen University, Guangzhou, China, in 2018 and 2020, respectively. He is currently pursuing the Ph.D. degree with The Chinese University of Hong Kong, Shenzhen. He is also enrolled in the Joint Education Program of the Shenzhen Research Institute of Big Data (SRIBD). From 2022 to 2023, he join Peng Cheng Laboratory as a visiting student. His research interests include sparse signal processing, digital twin network and wireless channel modeling. He received the IEEE ISWCS Best Paper Award in 2022. (Email: shutaozhang@link.cuhk.edu.cn)

Xiaohui REN was born in 1996. She received the B.S. degree from School of Mathematics and Information Science, Hebei University, in 2022. She is now a Ph.D. candidate in School of Mathematics and Computational Sciences of Xiangtan University since 2022. Her research interests include network optimization, traffic prediction and modeling. (Email: 2654966935@qq.com)

Jianhang ZHU was born in Henan, China, in 1999. He received the B.E. degree in communication engineering from the School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China, in 2021, where he is currently pursuing the M.E. degree with the School of Computer Science and Engineering. His research interests include age of information, edge computing, and the Internet of Things. (Email: zhujh26@mail2.sysu.edu.cn)

Jiajie HUANG was born in Guangzhou, China, in 1999. He received the B.E. degree in communication engineering from the School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, China, in 2021, where he is currently pursuing the M.E. degree with the School of Computer Science and Engineering. His research interests include age of information, intelligent network, and the Internet of Things. (Email: huangjj7@mail2.sysu.edu.cn)

Pengcheng HE was born in 1998. He received the B.S. degree from School of Software Engineering, Tongji University, in 2020. He is now a Ph.D. candidate in the School of Software Engineering, Tongji University since 2020. His research interests include optimization, machine learning, and signal processing. (Email: steven_he@tongji.edu.cn)

Kaiming SHEN received the B.E. degree in information security and the B.S. degree in mathematics from Shanghai Jiao Tong University, Shanghai, China in 2011, then the M.S. and Ph.D. degrees in electrical and computer engineering from University of Toronto, Ontario, Canada in 2013 and 2020, respectively. Since 2020, he has been an Assistant Professor with the School of Science and Engineering at The Chinese University of Hong Kong (Shenzhen), China. His main research interests include optimization, wireless communications, and information theory. Dr. Shen received the IEEE Signal Processing Society Young Author Best Paper Award in 2021. (Email: shenkaiming@cuhk.edu.cn)

Zhiqiang YAO received the M.S. and Ph.D. degrees from the School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China, in 2004 and 2010, respectively. He was a Postdoctoral Fellow at the Chinese University of Hong Kong, Shenzhen, China, in Prof. Zhi-Quan Luo’s research group. Since 2010, he has been with Xiangtan University, where he is currently a Full Professor and the Dean in the College of Automation and Electronic Information. His research interests include signal processing, communication, localization, and optimization. He is Senior Member of the Chinese Institute of Electronics and IEEE. (Email: yaozhiqiang@xtu.edu.cn)

Jie GONG received the B.S. and Ph.D. degrees in the Department of Electronic Engineering in Tsinghua University, Beijing, China, in 2008 and 2013, respectively. From Jul. 2012 to Jan. 2013, he visited the Institute of Digital Communications, University of Edinburgh, Edinburgh, UK. From Jul. 2013 to Oct. 2015, he worked as a postdoctorial Scholar in Tsinghua University. He is currently an asSociate Professor in the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China. He served as Editor for IEEE Transactions on Green Communications and Networking, Workshop Co-chair for IEEE/CIC ICCC 2022 and Publicity Co-chair for IEEE WCNC workshop since 2018. He was a co-recipient of the Best Paper Award from IEEE Communications Society Asia-Pacific Board in 2013. His research interests include green communications and networking, energy harvesting technology, mobile edge computing, and age of information. (Email: gongj26@mail.sysu.edu.cn)

Tsunghui CHANG received the B.S. degree in electrical engineering and the Ph.D. degree in communications engineering from the Taiwan Tsing Hua University, Hsinchu, China, in 2003 and 2008, respectively. He currently is an Associate Professor with the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China. Prior to being a Faculty Member, he held research positions with Taiwan Tsing Hua University, from 2008 to 2011, and the University of California, Davis, CA, USA, from 2011 to 2012. His research interests include signal processing and optimization problems in data communications, machine learning, and big data analysis. Dr. Chang is an Elected Member of IEEE Signal Processing Society (SPS) Signal Processing for Communications and Networking Technical Committee (SPCOM TC), the Funding Chair of IEEE SPS Integrated Sensing and Communication Technical Working Group (ISAC TWG), and the IEEE SPS Regional Director-at-Large of Region 10. He was on the Editorial Board for main SP journals, including an Associate Editor (2014C2018) and Senior Area Editor since February 2021 of the IEEE Transactions on Signal Processing, and an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks (2015C2018) and IEEE Open Journal of Signal Processing since January 2020. Dr. Chang was the recipient of the Young Scholar Research Award of National Taiwan University of Science and Technology in 2014, IEEE ComSoc Asian-Pacific Outstanding Young Researcher Award in 2015, Outstanding Faculty Research Award of SSE, CUHKSZ, in 2021, and IEEE SPS Best Paper Award in 2018 and 2021. (Email: tsunghui.chang@ieee.org)

Qingjiang SHI received the Ph.D. degree in electronic engineering from Shanghai Jiao Tong University, Shanghai, China, in 2011. From Sept. 2009 to Sept. 2010, he visited Prof. Z.-Q. (Tom) Luo’s research group at the University of Minnesota, Twin Cities. In 2011, he worked as a Research Scientist at Bell Labs China. From 2012, He was with the School of Information and Science Technology at Zhejiang Sci-Tech University. From Feb. 2016 to Mar. 2017, he worked as a Research Fellow at Iowa State University, USA. From Mar. 2018, he is currently a Full Professor with the School of Software Engineering at Tongji University. He is also with the Shenzhen Research Institute of Big Data. His interests lie in algorithm design and analysis with applications in machine learning, signal processing, and wireless networks. So far he has published more than 80 IEEE journals and filed about 40 national patents. Dr. Shi was an Associate Editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING. He was the recipient of IEEE Signal Processing Society Best Paper Award in 2022, the Huawei Technical Cooperation Achievement Transformation Award (2nd Prize) in 2022, the Huawei Outstanding Technical Achievement Award in 2021, the Golden Medal at the 46th International Exhibition of Inventions of Geneva in 2018, the First Prize of Science and Technology Award from China Institute of Communications in 2017, the National Excellent Doctoral Dissertation Nomination Award in 2013, the Shanghai Excellent Doctorial Dissertation Award in 2012, and the Best Paper Award from the IEEE PIMRC’09 conference. (Email: shiqj@tongji.edu.cn)

Zhiquan LUO (corresponding author) received the B.S. degree in applied mathematics from Peking University, China, and the Ph.D. degree in operations research from MIT in 1989. From 1989 to 2003, he held a faculty position with the ECE Department of McMaster University, Canada. He held a tier-1 Canada Research Chair in information processing from 2001 to 2003. After that, he has been a Full Professor at the ECE Department, University of Minnesota and held an endowed ADC Chair in digital technology. Currently, he is the Vice President (Academic) of The Chinese University of Hong Kong (Shenzhen) and the Director of Shenzhen Research Institute of Big Data (SRIBD). Prof. Luo is a Fellow of IEEE and SIAM. He was elected to Fellow of Royal Society of Canada in 2014 and a Foreign Member of the Chinese Academy of Engineering (CAE) in 2021. He received four best paper awards from the IEEE Signal Processing Society, one best paper award from EUSIPCO, the Farkas Prize from INFORMS and the prize of Paul Y. Tseng Memorial Lectureship in Continuous Optimization as well as some best paper awards from international conferences. In 2021, he was awarded 2020 ICCM Best Paper Award by International Consortium of Chinese Mathematicians. He has published over 350 refereed papers, books and special issues. Prof. Luo has served as an Associate Editor for many internationally recognized journals and the Editor-in-Chief for IEEE Transactions on Signal Processing. His research mainly addresses mathematical issues in information sciences, with particular focus on the design, analysis and applications of large-scale optimization algorithms. (Email: luozq@cuhk.edu.cn)
Received Date: 2022-06-30
Accepted Date: 2022-11-03

Available Online: 2023-01-16

Publish Date: 2022-11-05

Abstract

Abstract

With the rapid development of the fifthgeneration wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of network parameters, and the prohibitively huge number of parameters to optimize. The conventional network modeling and optimization approaches rely on drive test, trial-and-error, and engineering experience, which are labor intensive, error-prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental limits of radio channels and developing physical layer techniques to operate close to it, very little is known about the performance limits of wireless networks, where millions of radio channels interact with one another in complex manners. This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real-world wireless networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.
- Mathematical methods,
- Learning based methods,
- Network performance modeling,
- Network performance optimization

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References(141)

References

[1]	Xu Wenwei, Zhang Gong, Bai Bo, et al., “Ten key ICT challenges in the post-Shannon era,” Scientia Sinica Mathematica, vol.51, no.7, pp.1095–1138, 2021. (in Chinese) doi: 10.1360/SSM-2021-0013
[2]	Huawei Technologies, “Huawei 5G wireless network planning solution white paper,” Available at: https://www.huawei.com/en/huaweitech/industry-insights/outlook/mobile-broadband/insights-reports/5g-wireless-network-plan-solution-whitepaper, 2018-11.
[3]	C. -X. Wang, J. Bian, J. Sun, et al., “A Survey of 5G channel measurements and models,” IEEE Communications Surveys & Tutorials, vol.20, no.4, pp.3142–3168, 2018. doi: 10.1109/COMST.2018.2862141
[4]	J. Bian, C. -X. Wang, X. Gao, et al., “A general 3D non-stationary wireless channel model for 5G and beyond,” IEEE Transactions on Wireless Communications, vol.20, no.5, pp.3211–3224, 2021. doi: 10.1109/TWC.2020.3047973
[5]	J. Huang, C. -X. Wang, L. Bai, et al., “A big data enabled channel model for 5G wireless communication systems,” IEEE Transactions on Big Data, vol.6, no.2, pp.211–222, 2020. doi: 10.1109/TBDATA.2018.2884489
[6]	Shutao Zhang, Xinzhi Ning, Xi Zheng, et al., “A physics-based and data-driven approach for localized statistical channel modeling,” presented in the 34th International Teletraffic Congress (ITC), Shenzhen, China, Available at: https://www.researchgate.net/publication/368350585, 2022
[7]	X. Ning, S. Zhang, X. Zheng, et al., “Multi-grid-based localized statistical channel modeling: A radio map approach,” IEEE International Symposium on Wireless Communication Systems, Hangzhou, China, pp.1–6, 2022.
[8]	ETSI TR 138 901 V16.1.0: 2020, Study on Channel Model for Frequencies from 0.5 to 100 GHz, Available at: https://standards.iteh.ai/catalog/standards/etsi/36c589cf-c0ee-4e2d-8079-d5efd4fa6f7b/etsi-tr-138-901-v16.1.0-2020-11.
[9]	J. Zhou, W. Zhao, and S. Chen, “Dynamic network slice scaling assisted by prediction in 5G network,” IEEE Access, vol.8, pp.133700–133712, 2020. doi: 10.1109/ACCESS.2020.3010623
[10]	L. N. Wang, C. R. Zang, and Y. Y. Cheng, “The short-term prediction of the mobile communication traffic based on the product seasonal model,” SN Applied Sciences, vol.2, no.3, pp.1–9, 2020. doi: https://doi.org/10.1007/s42452-020-2158-9
[11]	D. Clemente, G. Soares, D. Fernandes, et al., “Traffic forecast in mobile networks: Classification system using machine learning,” 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA, pp.1–5, 2019.
[12]	J. Huang and M. Xiao, “Mobile Network Traffic Prediction Based on Seasonal Adjacent Windows Sampling and Conditional Probability Estimation,” IEEE Transactions on Big Data, vol.8, no.5, pp.1155–1168, 2022. doi: 10.1109/TBDATA.2020.3014049
[13]	Q. T. Tran, L. Hao, and Q. K. Trinh, “A comprehensive research on exponential smoothing methods in modeling and forecasting cellular traffic,” Concurrency and Computation: Practice and Experience, vol.32, no.10, article no.e5602, 2020. doi: 10.1002/cpe.5602
[14]	A. Perveen, R. Abozariba, M. Patwary, et al., “Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks,” Neural Computing and Applications, DOI: 10.1007/s00521-021-06206-0, 2021.
[15]	Y. Yamada, R. Shinkuma, T. Sato, et al., “Feature-selection based data prioritization in mobile traffic prediction using machine learning,” 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, pp.1–6, 2018.
[16]	H. Xia, X. Wei, Y. Gao, et al., “Traffic prediction based on ensemble machine learning strategies with bagging and lightgbm,” 2019 IEEE International Conference on Communications Workshops, Shanghai, China, pp.1–6, 2019.
[17]	C. Liu, T. Wu, Z. Li, et al., “Individual traffic prediction in cellular networks based on tensor completion,” International Journal of Communication Systems, vol.34, no.16, article no.e4952, 2021. doi: 10.1002/dac.4952
[18]	T. N. Weerasinghe, I. A. M. Balapuwaduge, and F. Y. Li. “Supervised learning based arrival prediction and dynamic preamble allocation for bursty traffic,” 2019 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France, pp.1–6, 2019.
[19]	R. Abozariba, M. K. Naeem, M. Asaduzzaman, et al., “Uncertaintyaware RAN slicing via machine learning predictions in next-generation networks,” 2020 IEEE 92nd Vehicular Technology Conference, Victoria, BC, Canada, pp.1–6, 2020.
[20]	L. Ale, N.Zhang, S. A. King, et al., “Spatio-temporal Bayesian learning for mobile edge computing resource planning in smart cities,” ACM Transactions on Internet Technology (TOIT), vol.21, no.3, pp.1–21, 2021. doi: 10.1145/3448613
[21]	J. Zhang, Y. Zheng, J. Sun, et al., “Flow prediction in spatiotemporal networks based on multitask deep learning,” IEEE Transactions on Knowledge and Data Engineering, vol.32, no.3, pp.468–478, 2020. doi: 10.1109/TKDE.2019.2891537
[22]	K. Guo, Y. Hu, Z, Qian, et al., “Optimized graph convolution recurrent neural network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, vol.22, no.2, pp.1138–1149, 2020. doi: 10.1109/TITS.2019.2963722
[23]	S. Zhan, L.Yu, Z. Wang, et al., “Cell traffic prediction based on convolutional neural network for software-defined ultra-dense visible light communication networks,” Security and Communication Networks, vol.2021, DOI: 10.1155/2021/9223965, 2021.
[24]	C. W. Huang, C. T. Chiang, and Q. Li, “A study of deep learning networks on mobile traffic forecasting,” 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Montreal, QC, Canada, pp.1–6, 2017.
[25]	R. Huang, C. Huang, Y. Liu, G. Dai, et al., “LSGCN: Long short-term traffic prediction with graph convolutional networks,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan, pp.2355–2361, 2020.
[26]	X. Yin, G. Wu, J, Wei, et al., “Deep learning on traffic prediction: methods, analysis, and future directions,” IEEE Transactions on Intelligent Transportation Systems, vol.23, no.6, pp.4927–4943, 2022. doi: 10.1109/TITS.2021.3054840
[27]	C. Vinchoff, N. Chung, T. Gordan, et al., “Traffic prediction in optical networks using graph convolutional generative adversarial networks,” 2020 22nd International Conference on Transparent Optical Networks, Bari, Italy, pp.1–4, 2020.
[28]	A. Boukerche and J. Wang, “Machine learning-based traffic prediction models for intelligent transportation systems,” Computer Networks, vol.181, article no.107530, 2020. doi: 10.1016/j.comnet.2020.107530
[29]	X. Yin, G. Wu, J. Wei, et al., “Multi-stage attention spatial-temporal graph networks for traffic prediction,” Neurocomputing, vol.428, pp.42–53, 2021. doi: 10.1016/j.neucom.2020.11.038
[30]	L. Zhao, Y. Song, C. Zhang, P. Wang, et al., “T-GCN: A temporal graph convolutional network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, vol.21, no.9, pp.3848–3858, 2020. doi: 10.1109/TITS.2019.2935152
[31]	M. Aibin, N. Chung, T. Gordan, et al., “On short-and long-term traffic prediction in optical networks using machine learning,” The 25th International Conference on Optical Network Design and Modelling, Gothenburg, Sweden, pp.1–6, 2021.
[32]	L. Fang, X. Cheng, H. Wang, et al., “Mobile demand forecasting via deep graph-sequence spatiotemporal modeling in cellular networks,” IEEE Internet of Things Journal, vol.5, no.4, pp.3091–3101, 2018. doi: 10.1109/JIOT.2018.2832071
[33]	Z. Wang, X. Su, and Z. Ding, “Long-term traffic prediction based on LSTM encoder-decoder architecture,” IEEE Transactions on Intelligent Transportation Systems, vol.22, no.10, pp.6561–6571, 2021. doi: 10.1109/TITS.2020.2995546
[34]	M. Aibin, “Deep learning for cloud resources allocation: Long-short term memory in EONs,” The International Conference on Transparent Optical Networks, Angers, France, pp.1–4, 2019.
[35]	F. Morales, M, Ruiz, L. Gifre, et al., “Virtual network topology adaptability based on data analytics for traffic prediction,” Journal of Optical Communications and Networking, vol.9, no.1, pp.A35–A45, 2017. doi: 10.1364/JOCN.9.000A35
[36]	D. A. Tedjopurnomo, Z. Bao, B. Zheng, et al., “A survey on modern deep neural network for traffic prediction: trends, methods and challenges,” IEEE Transactions on Knowledge and Data Engineering, vol.34, no.4, pp.1544–1561, 2022. doi: 10.1109/TKDE.2020.3001195
[37]	X. Shi, H. Qi, Y. Shen, et al., “A spatial-temporal attention approach for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, vol.22, no.8, pp.4909–4918, 2021. doi: 10.1109/TITS.2020.2983651
[38]	Y. Zhu and S. Wang, “Joint traffic prediction and base station sleeping for energy saving in cellular networks,” IEEE International Conference on Communications, Montreal, QC, Canada, pp.1–6, 2021.
[39]	A. M. Nagib, H. Abou-Zeid, H. S. Hassanein, et al., “Deep learning-based forecasting of cellular network utilization at millisecond resolutions,” IEEE International Conference on Communications, Montreal, QC, Canada, pp.1–6, 2021.
[40]	T. Kuber, I. Seskar, and N. Mandayam, “Traffic prediction by augmenting cellular data with non-cellular attributes,” 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, pp.1–6, 2021.
[41]	G. L. Santos, P. Rosati, T. Lynn, et al., “Predicting short-term mobile Internet traffic from Internet activity using recurrent neural networks,” International Journal of Network Management, vol.32, no.3, article no.e2191, 2022. doi: 10.1002/nem.2191
[42]	T. H. H. Aldhyani, M. Alrasheedi, A. A. Alqarni, et al., “Intelligent hybrid model to enhance time series models for predicting network traffic,” IEEE Access, vol.8, pp.130431–130451, 2020. doi: 10.1109/ACCESS.2020.3009169
[43]	A. Azari, P. Papapetrou, S. Denic, et al., “Cellular traffic prediction and classification: A comparative evaluation of LSTM and ARIMA,” International Conference on Discovery Science, Springer, Cham, pp.129–144, 2019.
[44]	A. Azari, P. Papapetrou, S. Denic, et al., “User traffic prediction for proactive resource management: Learning-powered approaches,” 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, pp.1–6, 2019.
[45]	Y. Zang, F. Ni, Z. Feng, et al, “Wavelet transform processing for cellular traffic prediction in machine learning networks,” in Proceedings of the 2015 IEEE China Summit and International Conference on Signal and Information Processing, Chengdu, China, pp.458–462, 2015.
[46]	T. Li, J. Zhang, K. Bao, Y. Liang, et al., “AutoST: efficient neural architecture search for spatio-temporal prediction,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event CA USA, pp.794–802, 2020.
[47]	J. Niemela, T. Isotalo, and J. Lempiainen, “Optimum antenna downtilt angles for macro-cellular WCDMA network,” EURASIP Journal on Wireless Communications and Networking, vol.2005, article no.610942, 2005. doi: 10.1155/WCN.2005.816
[48]	J. Chen, H. Jiang, and M. Peng, “Adaptive configuration of antenna down tilt for optimizing coverages in IMT-Advanced systems,” in Proceedings of IET International Conference on Communication Technology and Application (ICCTA 2011), Beijing, China, pp.495–499, 2011.
[49]	U. Turke and M. Koonert, “Advanced site configuration techniques for automatic UMTS radio network design,” in Proceedings of 2005 IEEE 61st Vehicular Technology Conference, Stockholm, Sweden, pp.1960–1964, 2005.
[50]	A. Awada, B. Wegmann, I. Viering, et al., “Optimizing the radio network parameters of the long term evolution system using Taguchi’s method,” IEEE Transactions on Vehicular Technology, vol.60, no.8, pp.3825–3839, 2011. doi: 10.1109/TVT.2011.2163326
[51]	S. Berger, M. Soszka, A. Fehske, et al., “Joint throughput and coverage optimization under sparse system knowledge in LTE-A networks,” in Proceedings of 2013 International Conference on ICT Convergence (ICTC), Jeju, Korea (South), pp.105–111, 2013.
[52]	V. Buenestado, M. Toril, S. Luna-Ramirez, et al., “Self-tuning of remote electrical tilts based on call traces for coverage and capacity optimization in LTE,” IEEE Transactions on Vehicular Technology, vol.66, no.5, pp.4315–4326, 2017. doi: 10.1109/TVT.2016.2605380
[53]	W. An, Y. Liu, and W. Huangfu, “A fast coordinate descent algorithm improving both coverage and capacity in cellular networks,” in Proceedings of 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, pp.43–48, 2020.
[54]	S. Berger, A. Fehske, P. Zanier, et al., “Online antenna tilt-based capacity and coverage optimization,” IEEE Wireless Communications Letters, vol.3, no.4, pp.437–440, 2014. doi: 10.1109/LWC.2014.2327228
[55]	S. Berger, M. Simsek, A. Fehske, P. Zanier, et al., “Joint downlink and uplink tilt-based self-organization of coverage and capacity under sparse system knowledge,” IEEE Trans. Veh. Technol., vol.65, no.4, pp.2259–2273, 2016. doi: 10.1109/TVT.2015.2419079
[56]	Y. Liu, W. Huangfu, H. Zhang, et al., “An effcient stochastic gradient descent algorithm to maximize the coverage of cellular networks,” IEEE Transactions on Wireless Communications, vol.18, no.7, pp.3424–3436, 2019. doi: 10.1109/TWC.2019.2914040
[57]	H. Eckhardt, S. Klein, and M. Gruber, “Vertical antenna tilt optimization for LTE base stations,” 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), Budapest, Hungary, pp.1–5, 2011.
[58]	Pablo A. Sanchez Ordonez, Salvador Luna-Ramirez, and Matias Toril, “A computationally efficient method for QoE-driven self-planning of antenna tilts in a LTE network,” IEEE Access, vol.8, pp.197005–197016, 2020. doi: 10.1109/ACCESS.2020.3033325
[59]	A. Engels, M. Reyer, X. Xu, et al., “Autonomous self-optimization of coverage and capacity in LTE cellular networks,” IEEE Transactions on Vehicular Technology, vol.62, no.5, pp.1989–2004, 2013. doi: 10.1109/TVT.2013.2256441
[60]	B. Partov, D. J. Leith, and R. Razavi, “Utility fair optimization of antenna tilt angles in LTE networks,” IEEE/ACM Transactions on Networking, vol.23, no.1, pp.175–185, 2015. doi: 10.1109/TNET.2013.2294965
[61]	C. Dhahri and T. Ohtsuki, “Antenna parameters optimization in self-organizing networks: Multi-armed bandits with Pareto search,” IEEE 86th Vehicular Technology Conference (VTC Fall), Toronto, ON, Canada, pp.1–5, 2017.
[62]	C. Shen, R. Zhou, C. Tekin, et al., “Generalized global bandit and its application in cellular coverage optimization,” IEEE Journal of Selected Topics in Signal Processing, vol.12, no.1, pp.218–232, 2018. doi: 10.1109/JSTSP.2018.2798164
[63]	Z. Wang and C. Shen, “Small cell transmit power assignment based on correlated bandit learning,” IEEE Journal on Selected Areas in Communications, vol.35, no.5, pp.1030–1045, 2017. doi: 10.1109/JSAC.2017.2679660
[64]	N. Dandanov, H. Al-Shatri, A. Klein, et al., “Dynamic self-optimization of the antenna tilt for best trade-off between coverage and capacity in mobile networks,” Wireless Personal Communications, vol.92, pp.251–278, 2017. doi: 10.1007/s11277-016-3849-9
[65]	R. M. Dreifuerst, S. Daulton, Y. Qian, et al., “Optimizing coverage and capacity in cellular networks using machine learning,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, pp.8138–8142, 2021.
[66]	Maxime Bouton, Hasan Farooq, Julien Forgeat, Shruti Bothe, Meral Shirazipour, and Per Karlsson, “Coordinated reinforcement learning for capacity optimization in mobile networks,” ArXiv e-Print, arXiv:2109.15175, 2012.
[67]	N. Naderializadeh, J. J. Sydir, M. Simsek, and H. Nikopour, “Resource management in wireless networks via multi-agent deep reinforcement learning,” IEEE Transactions on Wireless Communications, vol.20, no.6, pp.3507–3523, 2021. doi: 10.1109/TWC.2021.3051163
[68]	F. Vannella, G. Iakovidis, E. A. Hakim, et al., ”Remote electrical tilt optimization via safe reinforcement learning,” 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, pp.1–7, 2021.
[69]	R. Razavi, S. Klein, and H. Claussen, “A fuzzy reinforcement learning approach for self optimization of coverage in LTE networks,” Bell Labs Technical Journal, vol.15, no.3, pp.153–175, 2010. doi: 10.1002/bltj.20463
[70]	R. Razavi, S. Klein, and H. Claussen, “Self-optimization of capacity and coverage in LTE networks using a fuzzy reinforcement learning approach,” in Proceedings of 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Istanbul, Turkey, pp.1865–1870, 2010.
[71]	F. Shaoshuai, H. Tian, and C. Sengul, “Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning,” EURASIP Journal on Wireless Communications and Networking, vol.2014, article no.57, 2014. doi: 10.1186/1687-1499-2014-57
[72]	A. Thampi, D. Kaleshi, P. Randall, et al., “A sparse sampling algorithm for self-optimisation of coverage in LTE networks,” in Proceedings of 2012 International Symposium on Wireless Communication Systems, Paris, France, pp.909–913, 2012.
[73]	Eren Balevi and Jeffrey G. Andrews, “Online antenna tuning in heterogeneous cellular networks with deep reinforcement learning,” IEEE Transactions on Cognitive Communications and Networking, vol.5, no.4, pp.1113–1124, 2019. doi: 10.1109/TCCN.2019.2933420
[74]	S. Berger, A. Fehske, and G. Fettweis, “Force field based joint optimization of strictly monotonic KPIs in wireless networks,” 2012 IFIP Wireless Days, Dublin, Ireland, pp.1–6, 2012.
[75]	W. Guo, S. Wang, Y. Wu, et al., “Spectral and energy-efficient antenna tilting in a hetnet using reinforcement learning,” in Proceedings of 2013 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, pp.767–772, 2013.
[76]	H. Zhang, J. Du, J. Cheng, et al., “Incomplete CSI based resource optimization in SWIPT enabled heterogeneous networks: A non-cooperative game theoretic approach,” IEEE Transactions on Wireless Communications, vol.17, no.3, pp.1882–1892, 2018. doi: 10.1109/TWC.2017.2786255
[77]	Y. Yoon and Y. -H. Kim, “An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks,” IEEE Transactions on Cybernetics, vol.23, no.5, pp.1473–1483, 2013. doi: 10.1109/TCYB.2013.2250955
[78]	Y. Mengjun, F. Lei, L. Wenjing, et al., “Cell outage compensation based on CoMP and optimization of tilt,” The Journal of China Universities of Posts and Telecommunications, vol.22, no.5, pp.71–79, 2015. doi: 10.1016/S1005-8885(15)60683-5
[79]	Y. Liu, W. Huangfu, H. Zhang, W. An, et al., “An efficient geometry-induced genetic algorithm for base station placement in cellular networks,” IEEE Access, vol.7, pp.108604–108616, 2019. doi: 10.1109/ACCESS.2019.2933284
[80]	N. Lakshminarasimman, S. Baskar, A. Alphones, et al., “Evolutionary multiobjective optimization of cellular base station locations using modified NSGA-Ⅱ,” Wireless Networks, vol.17, no.3, pp.597–609, 2011. doi: 10.1007/s11276-010-0299-2
[81]	J. Zhang, C. Sun, Y. Yi, et al., “A hybrid framework for capacity and coverage optimization in self-organizing LTE networks,” in Proceedings of 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), London, UK, pp.2919–2923, 2013.
[82]	M. Sousa, A. Martins, and P. Vieira, “Self-optimization of low coverage and high interference in real 3G/4G radio access networks,” ISEL Academic Journal of Electronics, Telecommunications and Computers, vol.3, no.1, article no.12, 2018. doi: 10.34629/ipl.isel.i-ETC.39
[83]	L. Huang, Y. Zhou, J. Hu, et al., “Coverage optimization for femtocell clusters using modified particle swarm optimization,” in Proceedings of 2012 IEEE International Conference on Communications (ICC), Ottawa, ON, Canada, pp.611–615, 2012.
[84]	Y. Qin, W. Huangfu, H. Zhang, et al., “Accelerated coverage optimization with particle swarm in the quotient space characterizing antenna azimuths of cellular networks,” IEEE Access, vol.7, pp.86252–86264, 2019. doi: 10.1109/ACCESS.2019.2925099
[85]	I. Siomina, P. Varbrand, and D. Yuan, “Automated optimization of service coverage and base station antenna configuration in UMTS networks,” IEEE Wireless Communications, vol.13, no.6, pp.16–25, 2006. doi: 10.1109/MWC.2006.275194
[86]	S. Hurley, S. Allen, D. Ryan, et al., “Modelling and planning fixed wireless networks,” Wireless Netw., vol.16, no.3, pp.577–592, 2010. doi: 10.1007/s11276-008-0155-9
[87]	W. Mai, H. -L. Liu, L. Chen, et al., “Multi-objective evolutionary algorithm for 4G base station planning,” in Proceedings of 2013 Ninth International Conference on Computational Intelligence and Security, Emeishan, China, pp.85–89, 2013.
[88]	R. Han, C. Feng, H. Xia, Y. We, et al., “Coverage optimization for dense deployment small cell based on ant colony algorithm,” in Proceedings of 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), Vancouver, BC, Canada, pp.1–5, 2014.
[89]	W. Bo, S. Yu, Z. Lv, et al., “A novel self-optimizing load balancing method based on ant colony in LTE network,” in Proceedings of 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, China, pp.1–4, 2012.
[90]	Z. -Q. Luo, W. -K. Ma, A. M. So, et al., “Semidefinite relaxation of quadratic optimization problems,” IEEE Signal Processing Magazine, vol.27, no.3, pp.20–34, 2010. doi: 10.1109/MSP.2010.936019
[91]	K. Shen and W. Yu, “Fractional programming for communication systems Part I: Power control and beamforming,” IEEE Transactions on Signal Processing, vol.66, no.10, pp.2616–2630, 2018. doi: 10.1109/TSP.2018.2812733
[92]	K. Shen and W. Yu, “Fractional programming for communication systems Part II: Uplink scheduling via matching,” IEEE Transactions on Signal Processing, vol.66, no.10, pp.2631–2644, 2018. doi: 10.1109/TSP.2018.2812748
[93]	Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Transactions on Wireless Communications, vol.18, no.11, pp.5394–5409, 2019. doi: 10.1109/TWC.2019.2936025
[94]	G. Zhou, C. Pan, H. Ren, et al., “Robust beamforming design for intelligent reflecting surface aided MISO communication systems,” IEEE Wireless Communications Letters, vol.9, no.10, pp.1658–1662, 2020. doi: 10.1109/LWC.2020.3000490
[95]	B. Zheng, C. You, and R. Zhang, “Double-IRS assisted multi-user MIMO: Cooperative passive beamforming design,” IEEE Transactions on Wireless Communications, vol.20, no.7, pp.4513–4526, 2021. doi: 10.1109/TWC.2021.3059945
[96]	H. Xie, J. Xu, and Y. -F. Liu, “Max-min fairness in IRS-aided multi-cell MISO systems with joint transmit and reflective beamforming,” IEEE Transactions on Wireless Communications, vol.20, no.2, pp.1379–1393, 2021. doi: 10.1109/TWC.2020.3033332
[97]	S. Huang, Y. Ye, M. Xiao, et al., “Decentralized beamforming design for intelligent reflecting surface-enhanced cell-free networks,” IEEE Wireless Communications Letters, vol.10, no.3, pp.673–677, 2021. doi: 10.1109/LWC.2020.3045884
[98]	W. Ni, X. Liu, Y. Liu, et al., “Resource allocation for multi-cell IRS-aided NOMA networks,” IEEE Transactions on Wireless Communications, vol.20, no.7, pp.4253–4268, 2021. doi: 10.1109/TWC.2021.3057232
[99]	M. Zeng, X. Li, G. Li, et al., “Sum rate maximization for IRS-assisted uplink NOMA,” IEEE Communications Letters, vol.25, no.1, pp.234–238, 2021. doi: 10.1109/LCOMM.2020.3025978
[100]	Z. Zhang, L. Dai, X. Chen, et al., “Active RIS vs. passive RIS: Which will prevail in 6G?,” IEEE Transactions on Communications, Early Access, DOI: 10.1109/TCOMM.2022.3231893, 2021.
[101]	Y. Cao, T. Lv, and W. Ni, “Intelligent reflecting surface aided multi-user mmWave communications for coverage enhancement,” 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London, UK, pp.1–6, 2020.
[102]	J. Hu, Y. -C. Liang, and Y. Pei, “Reconfigurable intelligent surface enhanced multi-user MISO symbiotic radio system,” IEEE Transactions on Communications, vol.69, no.4, pp.2359–2371, 2021. doi: 10.1109/TCOMM.2020.3047444
[103]	J. Zhu, Y. Huang, J. Wang, et al., “Power efficient IRS-assisted NOMA,” IEEE Transactions on Communications, vol.69, no.2, pp.900–913, 2021. doi: 10.1109/TCOMM.2020.3029617
[104]	Z. Zhang and L. Dai, “A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network,” IEEE Transactions on Signal Processing, vol.69, pp.4085–4101, 2021. doi: 10.1109/TSP.2021.3088755
[105]	Y. Cao, T. Lv, Z. Lin, et al., “Delay-constrained joint power control, user detection and passive beamforming in intelligent reflecting surface-assisted uplink mmWave system,” IEEE Transactions on Cognitive Communications and Networking, vol.7, no.2, pp.482–495, 2021. doi: 10.1109/TCCN.2021.3064973
[106]	K. Feng, X. Li, Y. Han, et al., “Physical layer security enhancement exploiting intelligent reflecting surface,” IEEE Communications Letters, vol.25, no.3, pp.734–738, 2021. doi: 10.1109/LCOMM.2020.3042344
[107]	T. Shafique, H. Tabassum, and E. Hossain, “Optimization of wireless relaying with flexible UAV-borne reflecting surfaces,” IEEE Transactions on Communications, vol.69, no.1, pp.309–325, 2021. doi: 10.1109/TCOMM.2020.3032700
[108]	X. Mu, Y. Liu, L. Guo, et al., “Exploiting intelligent reflecting surfaces in NOMA networks: Joint beamforming optimization,” IEEE Transactions on Wireless Communications, vol.19, no.10, pp.6884–6898, 2020. doi: 10.1109/TWC.2020.3006915
[109]	M. -M. Zhao, A. Liu, Y. Wan, et al., “Two-timescale beamforming optimization for intelligent reflecting surface aided multiuser communication with QoS constraints,” IEEE Transactions on Wireless Communications, vol.20, no.9, pp.6179–6194, 2021. doi: 10.1109/TWC.2021.3072382
[110]	B. Ning, Z. Chen, W. Chen, et al., “Beamforming optimization for intelligent reflecting surface assisted MIMO: A sum-path-gain maximization approach,” IEEE Wireless Communications Letters, vol.9, no.7, pp.1105–1109, 2020. doi: 10.1109/LWC.2020.2982140
[111]	H. Shen, W. Xu, S. Gong, et al., “Beamforming optimization for IRS-aided communications with transceiver hardware impairments,” IEEE Transactions on Communications, vol.69, no.2, pp.1214–1227, 2021. doi: 10.1109/TCOMM.2020.3033575
[112]	C. Pan, H. Ren, K. Wang, et al., “Multicell MIMO communications relying on intelligent reflecting surfaces,” IEEE Transactions on Wireless Communications, vol.19, no.8, pp.5218–5233, 2020. doi: 10.1109/TWC.2020.2990766
[113]	X. Yu, D. Xu, Y. Sun, et al., “Robust and secure wireless communications via intelligent reflecting surfaces,” IEEE Journal on Selected Areas in Communications, vol.38, no.11, pp.2637–2652, 2020. doi: 10.1109/JSAC.2020.3007043
[114]	V. Arun and H. Balakrishnan, “RFocus: Beamforming using thousands of passive antennas,” in Proceedings of the 17th Usenix Conference on Networked Systems Design and Implementation, Santa Clara, CA, USA, pp.1047–1061, 2020.
[115]	S. Ren, K. Shen, Y. Zhang, X. Li, X. Chen, and Z.-Q. Luo, “Configuring intelligent reflecting surface with performance guarantees: Blind beamforming,” Available at: https://kaimingshen.github.io/doc/BlindBeamforming.pdf, 2021.
[116]	X. Pei, H. Yin, L. Tan, et al., “RIS-aided wireless communications: Prototyping, adaptive beamforming, and indoor/outdoor field trials,” IEEE Transactions on Communications, vol.69, no.12, pp.8627–8640, 2021. doi: 10.1109/TCOMM.2021.3116151
[117]	N. M. Tran, M. M. Amri, D. S. Kang, et al., “Demo: Demonstration of reconfigurable metasurface for wireless communications,” 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Seoul, Korea (South), pp.1–2, 2020.
[118]	D. Kitayama, Y. Hama, K. Miyachi, and Y. Kishiyama, “Research of transparent RIS technology toward 5G evolution & 6G,” NTT Technical Review, vol.19, no.11, pp.26–34, 2021. doi: 10.53829/ntr202111fa2
[119]	P. Staat, S. Mulzer, S. Roth, et al., “IRShield: A countermeasure against adversarial physical-layer wireless sensing,” arXiv preprint, arXiv: 2112.01967, 2021.
[120]	W. Cai, M. Li, and Q. Liu, “Practical modeling and beamforming for intelligent reflecting surface aided wideband systems,” IEEE Communications Letters, vol.24, no.7, pp.1568–1571, 2020. doi: 10.1109/LCOMM.2020.2987322
[121]	X. Yu, D. Xu, and R. Schober, “Optimal beamforming for MISO communications via intelligent reflecting surfaces,” 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, pp.1–5, 2020.
[122]	B. Di, H. Zhang, L. Song, et al., “Hybrid beamforming for reconfigurable intelligent surface based multi-user communications: Achievable rates with limited discrete phase shifts,” IEEE Journal on Selected Areas in Communications, vol.38, no.8, pp.1809–1822, 2020. doi: 10.1109/JSAC.2020.3000813
[123]	Y. Zhang, K. Shen, S. Ren, et al., “Configuring intelligent reflecting surface with performance guarantees: Optimal beamforming,” IEEE Journal of Selected Topics in Signal Processing, vol.16, no.5, pp.967–979, 2022. doi: 10.1109/JSTSP.2022.3176479
[124]	J. Qiao and M. -S. Alouini, “Secure transmission for intelligent reflecting surface-assisted mmWave and terahertz systems,” IEEE Wireless Communications Letters, vol.9, no.10, pp.1743–1747, 2020. doi: 10.1109/LWC.2020.3003400
[125]	C. You, B. Zheng, and R. Zhang, “Channel estimation and passive beamforming for intelligent reflecting surface: Discrete phase shift and progressive refinement,” IEEE Journal on Selected Areas in Communications, vol.38, no.11, pp.2604–2620, 2020. doi: 10.1109/JSAC.2020.3007056
[126]	B. Zheng, Q. Wu, and R. Zhang, “Intelligent reflecting surface-assisted multiple access with user pairing: NOMA or OMA?,” IEEE Communications Letters, vol.24, no.4, pp.753–757, 2020. doi: 10.1109/LCOMM.2020.2969870
[127]	M. -M. Zhao, A. Liu, and R. Zhang, “Outage-constrained robust beamforming for intelligent reflecting surface aided wireless communication,” IEEE Transactions on Signal Processing, vol.69, pp.1301–1316, 2021. doi: 10.1109/TSP.2021.3056899
[128]	D. Li, “Fairness-aware multiuser scheduling for finite-resolution intelligent reflecting surface-assisted communication,” IEEE Communications Letters, vol.25, no.7, pp.2395–2399, 2021. doi: 10.1109/LCOMM.2021.3073353
[129]	Z. Feng, B. Clerckx, and Y. Zhao, “Waveform and beamforming design for intelligent reflecting surface aided wireless power transfer: Single-user and multi-user solutions,” IEEE Transactions on Wireless Communications, vol.21, no.7, pp.5346–5361, 2022. doi: 10.1109/TWC.2021.3139440
[130]	Q. Wu and R. Zhang, “Beamforming optimization for wireless networks aided by intelligent reflecting surface with discrete phase shifts,” IEEE Transactions on Communications, vol.68, no.3, pp.1838–1851, 2020. doi: 10.1109/TCOMM.2019.2958916
[131]	A. Al-Hilo, M. Samir, M. Assi, et al., “Reconfigurable intelligent surface enabled vehicular communication: Joint user scheduling and passive beamforming,” IEEE Transactions on Vehicular Technology, vol.71, no.3, pp.2333–2345, 2022. doi: 10.1109/TVT.2022.3141935
[132]	M.-M. Zhao, Q. Wu, M.-J. Zhao, et al., “Exploiting amplitude control in intelligent reflecting surface aided wireless communication with imperfect CSI,” IEEE Transactions on Communications, vol.69, no.6, pp.4216–4231, 2021. doi: 10.1109/TCOMM.2021.3064959
[133]	L. You, J. Xiong, D. W. K. Ng, et al., “Energy efficiency and spectral efficiency tradeoff in RIS-aided multiuser MIMO uplink transmission,” IEEE Transactions on Signal Processing, vol.69, pp.1407–1421, 2021. doi: 10.1109/TSP.2020.3047474
[134]	W. Zhang, J. Xu, W. Xu, et al., “Cascaded channel estimation for IRS-assisted mmWave multi-antenna with quantized beamforming,” IEEE Communications Letters, vol.25, no.2, pp.593–597, 2021. doi: 10.1109/LCOMM.2020.3028878
[135]	H. Wang, N. Shlezinger, Y. C. Eldar, et al., “Dynamic metasurface antennas for MIMO-OFDM receivers with bit-limited ADCs,” IEEE Transactions on Communications, vol.69, no.4, pp.2643–2659, 2021. doi: 10.1109/TCOMM.2020.3040761
[136]	S. Abeywickrama, R. Zhang, Q. Wu, et al., “Intelligentreflecting surface: Practical phase shift model and beamforming optimization,” IEEE Transactions on Communications, vol.68, no.9, pp.5849–5863, 2020. doi: 10.1109/TCOMM.2020.3001125
[137]	C. Huang, A. Zappone, G. C. Alexandropoulos, et al., “Reconfigurable intelligent surface for energy efficiency in wireless communication,” IEEE Transactions on Wireless Communications, vol.18, no.8, pp.4157–4170, 2019. doi: 10.1109/TWC.2019.2922609
[138]	J. Gao, C. Zhong, X. Chen, et al., “Unsupervised learning for passive beamforming,” IEEE Communications Letters, vol.24, no.5, pp.1052–1056, 2020. doi: 10.1109/LCOMM.2020.2965532
[139]	C. Liu, X. Liu, D. W. K. Ng, et al., “Deep residual network empowered channel estimation for IRS-assisted multi-user communication systems,” IEEE International Conference on Communications, Montreal, QC, Canada, pp.1–7, 2021.
[140]	G. T. de Araújo, A. L. F. de Almeida, and R. Boyer, “Channel estimation for intelligent reflecting surface assisted MIMO systems: A tensor modeling approach,” IEEE Journal of Selected Topics in Signal Processing, vol.15, no.3, pp.789–801, 2021. doi: 10.1109/JSTSP.2021.3061274
[141]	D. Mishra and H. Johansson, “Channel estimation and low-complexity beamforming design for passive intelligent surface assisted MISO wireless energy transfer,” in Proc. of 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, pp.4659–4663, 2019.