Deep-learning Based Adaptive Beam Management Technique for Mobile High-speed 5G mmWave Networks

被引：0

作者：

Na, Woongsoo ^{[1
]}

Bae, Byungjun ^{[1
,2
]}

Cho, Sukhee ^{[1
]}

Kim, Nayeon ^{[1
,2
]}

机构：

[1] Elect & Telecommun Res Inst ETRI, Daejeon, South Korea

[2] Univ Sci & Technol, Daejeon, South Korea

来源：

2019 IEEE 9TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE-BERLIN) | 2019年

关键词：

Beam management; 5G mmWave; Deep-learning; Mobile networks;

D O I：

10.1109/icce-berlin47944.2019.8966183

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

5G core frequency band, mmWave, is about ten times wider than the existing commercial frequency band, enabling various services to be created. However, due to the characteristics of the mmWave frequency, it has various limitations. In the mobile environment, the misaligned beam problem, in which the SNR is degraded because the beam align between the sender and the receiver does not match, is one of the biggest problems to be solved. In this paper, we propose a adaptive beam management scheme based on deep-learning to solve misaligned beam problem. In the proposed scheme, 5G base-station (gNB) learns the mobility information, SNR, and current beam information of the associated user equipment (UE) by the deep-learning agent, and predicts whether the beam is aligned or not. From the prediction result, gNB and UE perform beam hands-off in advance before loss of connectivity.

引用

页码：149 / 151

页数：3

共 50 条

[1] Deep Learning-based Predictive Beam Management for 5G mmWave Systems
Kaya, Aliye Ozge
Viswanathan, Harish
2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2021,
[2] Beam Management in Operational 5G mmWave Networks
Feng, Yufei
Wei, Jin
Dinh, Phuc
Ghoshal, Moinak
Koutsonikolas, Dimitrios
PROCEEDINGS OF THE 2023 THE 7TH ACM WORKSHOP ON MILLIMETER-WAVE AND TERAHERTZ NETWORKS AND SENSING SYSTEMS, MMNETS 2023, 2023, : 13 - 18
[3] Learning-Aided Beam Management for mmWave High-Speed Railway Networks
Zhang, Yuqi
Liu, Shengheng
Lu, Zhaohua
Meng, Fan
Huang, Yongming
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
[4] Deep Learning based Speed Profiling for Mobile Users in 5G Cellular Networks
Saffar, Illyyne
Morel, Marie Line Alberi
Singh, Kamal Deep
Viho, Cesar
2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,
[5] Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks
Cousik, Tarun S.
Shah, Vijay K.
Reed, Jeffrey H.
Erpek, Tugba
Sagduyu, Yalin E.
2021 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM 2021), 2021,
[6] Deep Learning Based Hotspot Prediction and Beam Management for Adaptive Virtual Small Cell in 5G Networks
Liu, Yanan
Wang, Xianbin
Boudreau, Gary
Bin Sediq, Akram
Abou-zeid, Hatem
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2020, 4 (01): : 83 - 94
[7] ADVERSARIAL ATTACKS ON DEEP LEARNING BASED MMWAVE BEAM PREDICTION IN 5G AND BEYOND
Kim, B.
Sagduyu, Y.
Erpek, T.
Ulukus, S.
2021 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2021, : 590 - 594
[8] Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and beyond
Kim, B.
Sagduyu, Y.
Erpek, T.
Ulukus, S.
IEEE Workshop on Statistical Signal Processing Proceedings, 2021, 2021-July : 590 - 594
[9] Cooperative Deep-Learning Positioning in mmWave 5G-Advanced Networks
Tedeschini, Bernardo Camajori
Nicoli, Monica
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2023, 41 (12) : 3799 - 3815
[10] Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks
Yao, Yujie
Zhou, Hao
Erol-Kantarci, Melike
2022 27TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2022), 2022,

← 1 2 3 4 5 →