- 综合性科技类中文核心期刊
- 中国科技论文统计源期刊
- 中国科学引文数据库来源期刊
- 中国学术期刊文摘数据库（核心版）来源期刊
- 中国学术期刊综合评价数据库来源期刊
| Citation: |
SUN Yanhua, QIAO Lan, YANG Ruizhe, SI Pengbo, ZHANG Yanhua. Hybrid Beamforming and Resource Allocation for mm-Wave Massive MIMO Based on Federated Learning[J]. Journal of Beijing University of Technology, 2023, 49(8): 851-861. DOI: 10.11936/bjutxb2021100008
|
Aiming at the problem that the hybrid beamforming in millimeter wave (mm-Wave) massive multiple input multiple output (MIMO) system leads to excessive communication overhead in centralized machine learning (CML), a hybrid beamforming and resource allocation optimization method based on contract theory under hierarchical federated learning (FL) was proposed. First, the system overhead was calculated for multiple users in a hierarchical system, and the maximum benefit of the system was realized by optimizing the allocation of resources. Then, the allocated resources were used to train the channel data and corresponding precoder data in the back propagation neural network (BPNN). The model was aggregated at the edge server (ES) by collecting weights and parameters from users and uploaded to the cloud server (CS) to aggregate after reaching a certain accuracy until obtaining an optimal model. The simulation results show that the resource optimization greatly reduces the communication overhead, and hybrid beamforming based on FL not only achieves almost the same sum rate as to CML, but also outperforms orthogonal matching pursuit (OMP) hybrid beamforming and fully digital beamforming.
| [1] |
LETAIEF K B, CHEN W, SHI Y M, et al. The roadmap to 6G∶AI empowered wireless networks[J]. IEEE Communications Magazine, 2019, 57(8): 84-90. doi: 10.1109/MCOM.2019.1900271
|
| [2] |
SUKHMANI S, SADEGHI M, EROL-KANTARCI M, et al. Edge caching and computing in 5G for mobile AR/VR and tactile Internet[J]. IEEE MultiMedia, 2019, 26(1): 21-30. doi: 10.1109/MMUL.2018.2879591
|
| [3] |
LIU Y, YUAN X L, XIONG Z H, et al. Federated learning for 6G communications: challenges, methods, and future directions[J]. China Communications, 2020, 17(9): 105-118. doi: 10.23919/JCC.2020.09.009
|
| [4] |
NIKNAM S, DHILLON H S, REED J H. Federated learning for wireless communications: motivation, opportunities and challenges[J]. IEEE Communications Magazine, 2020, 58(6): 46-51. doi: 10.1109/MCOM.001.1900461
|
| [5] |
MCMAHAN H B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[C]//Artificial Intelligence and Statistics. Cambridge: JMLR, 2017: 1273-1282.
|
| [6] |
SAMARAKOON S, BENNIS M, SAAD W, et al. Distributed federated learning for ultra-reliable low-latency vehicular communications[J]. IEEE Transactions on Communications, 2020, 68(2): 1146-1159. doi: 10.1109/TCOMM.2019.2956472
|
| [7] |
LU Y L, HUANG X H, ZHANG K, et al. Blockchain empowered asynchronous federated learning for secure data sharing in Internet of vehicles[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 4298-4311. doi: 10.1109/TVT.2020.2973651
|
| [8] |
YE D D, YU R, PAN M, et al. Federated learning in vehicular edge computing: a selective model aggregation approach[J]. IEEE Access, 2020, 8: 23920-23935. doi: 10.1109/ACCESS.2020.2968399
|
| [9] |
SAMIR M, EBRAHIMI D, ASSI C, et al. Trajectory planning of multiple dronecells in vehicular networks: a reinforcement learning approach[J]. IEEE Networking Letters, 2020, 2(1): 14-18. doi: 10.1109/LNET.2020.2966976
|
| [10] |
ZENG T C, SEMIARI O, MOZAFFARI M, et al. Federated learning in the sky: joint power allocation and scheduling with UAV swarms[C]//2020 IEEE International Conference on Communications. Piscataway: IEEE, 2020: 1-6.
|
| [11] |
SHIRI H, PARK J H, BENNIS M. Communication-efficient massive UAV online path control: federated learning meets mean-field game theory[J]. IEEE Transactions on Communications, 2020, 68(11): 6840-6857. doi: 10.1109/TCOMM.2020.3017281
|
| [12] |
NG J S, LIM W Y B, DAI H N, et al. Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4): 2326-2344. doi: 10.1109/TITS.2020.3041345
|
| [13] |
YANG H H, LIU Z Z, QUEK T Q S, et al. Scheduling policies for federated learning in wireless networks[J]. IEEE Transactions on Communications, 2019, 68(1): 317-333.
|
| [14] |
LUO S Q, CHEN X, WU Q, et al. HFEL: joint edge association and resource allocation for cost-efficient hierarchical federated edge learning[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6535-6548. doi: 10.1109/TWC.2020.3003744
|
| [15] |
ELBIR A M, COLERI S. Federated learning for hybrid beamforming in mm-Wave massive MIMO[J]. IEEE Communications Letters, 2020, 24(12): 2795-2799. doi: 10.1109/LCOMM.2020.3019312
|
| [16] |
ALKHATEEB A, AYACH O E, LEUS G, et al. Channel estimation and hybrid precoding for millimeter wave cellular systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5): 831-846. doi: 10.1109/JSTSP.2014.2334278
|
| [17] |
WANG P L, FANG J, DAI L L, et al. Joint transceiver and large intelligent surface design for massive MIMO mmWave systems[J]. IEEE Transactions on Wireless Communications, 2020, 20(2): 1052-1064.
|
| [18] |
YING K K, GAO Z, LYU S X, et al. GMD-based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter-wave massive MIMO[J]. IEEE Access, 2020, 8: 19530-19539. doi: 10.1109/ACCESS.2020.2968456
|
| [19] |
GAO X Y, DAI L L, HAN S F, et al. Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(4): 998-1009. doi: 10.1109/JSAC.2016.2549418
|
| [20] |
张钰, 赵雄文. 毫米波大规模MIMO系统中的预编码技术[J]. 中兴通讯技术, 2018, 24(140): 26-31. https://www.cnki.com.cn/Article/CJFDTOTAL-ZXTX201803006.htm
ZHANG Y, ZHAO X W. Precoding technology in mm-Wave massive MIMO system[J]. ZTE Technology, 2018, 24(140): 26-31. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZXTX201803006.htm
|
| [21] |
ALKHATEEB A, LEUS G, HEATH R W. Limited feedback hybrid precoding for multi-user millimeter wave systems[J]. IEEE Transactions on Wireless Communications, 2015, 14(11): 6481-6494. doi: 10.1109/TWC.2015.2455980
|
| [22] |
KONECNY J, QU Z, RICHTARIK P. Semi-stochastic coordinate descent[J]. Optimization Methods and Software, 2017, 32(5): 993-1005. doi: 10.1080/10556788.2017.1298596
|
| [23] |
PANDEY S R, TRAN N H, BENNIS M, et al. A crowdsourcing framework for on-device federated learning[J]. IEEE Transactions on Wireless Communications, 2020, 19(5): 3241-3256. doi: 10.1109/TWC.2020.2971981
|
| [24] |
VU T T, NGO D T, TRAN N H, et al. Cell-free massive MIMO for wireless federated learning[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6377-6392. doi: 10.1109/TWC.2020.3002988
|
| [25] |
MA C X, KONECNY J, JAGGI M, et al. Distributed optimization with arbitrary local solvers[J]. Optimization Methods and Software, 2017, 32(4): 813-848. doi: 10.1080/10556788.2016.1278445
|
| [26] |
CHEN Y C, HE S B, HOU F, et al. An efficient incentive mechanism for device-to-device multicast communication in cellular networks[J]. IEEE Transactions on Wireless Communications, 2018, 17(12): 7922-7935. doi: 10.1109/TWC.2018.2872981
|
| [27] |
REN J K, YU G D, DING G Y. Accelerating DNN training in wireless federated edge learning systems[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(1): 219-232. doi: 10.1109/JSAC.2020.3036971
|
| [28] |
LI C H, WANG S M, HUANG X M, et al. Parked vehicular computing for energy-efficient Internet of vehicles: a contract theoretic approach[J]. IEEE Internet of Things Journal, 2019, 6(4): 6079-6088. doi: 10.1109/JIOT.2018.2869892
|
| [29] |
AYACH O E, RAJAGOPAL S, ABU-SURRA S, et al. Spatially sparse precoding in millimeter wave MIMO systems[J]. IEEE Transactions on Wireless Communications, 2014, 13(3): 1499-1513. doi: 10.1109/TWC.2014.011714.130846
|
| [1] | SUN Enchang, QU Hanxing, YUAN Yongyi, ZHANG Yanhua. Survey of D2D Communication Resource Allocation Methods in Cellular Networks[J]. Journal of Beijing University of Technology, 2021, 47(10): 1188-1200. DOI: 10.11936/bjutxb2020040007 |
| [2] | LIU Jing-fang, HUANG Xiao-ou, YU Yue-qing. Constraint and Motion Analysis of a Dendriform-hybrid Mechanism[J]. Journal of Beijing University of Technology, 2014, 40(6): 809-813. |
| [3] | LAI Jian-hui, CHEN Yan-yan, ZHANG Wei-wei, WU De-cang. Subway and Public Transit Path Choice Model Based on Prospect Theory[J]. Journal of Beijing University of Technology, 2014, 40(4): 567-573. |
| [4] | QU Chong-nian, WU Liang-sheng, MA Jian-feng, ZHANG Shi-hai, XIAO Yi-chuan, XIA Qun. Research on Dynamic Stiffness of Porous Oily Media Interfaces Based on Fractal Theory[J]. Journal of Beijing University of Technology, 2013, 39(7): 976-980. |
| [5] | LIU Chao-feng, SU Jing-yu, WANG Wei, MA Dong-hui. Multi-criteria Assessment of Regional Seismic Hazard Based on GIS and Catastrophe Theory[J]. Journal of Beijing University of Technology, 2013, 39(3): 366-372. |
| [6] | FU Sheng, ZHANG Ya-bin. Development of the Monitoring and Fault Diagnosis System for Pump Based on Fuzzy Theory[J]. Journal of Beijing University of Technology, 2012, 38(7): 1008-1012. |
| [7] | ZHAO Xiao-hua, CHEN Yang-zhou. Traffic Light Control Method for a Single Intersection Based on Hybrid Systems Theory[J]. Journal of Beijing University of Technology, 2004, 30(4): 412-416. |
| [8] | LÜ Jian, YANG Xiang-ping, SHU Qing-nian, LIU Jing. Study on the Distribution of Storm Sewer System With Fractal Theory[J]. Journal of Beijing University of Technology, 2003, 29(4): 447-450. |
| [9] | WANG Dong-ping, CHEN Shu-jun, HUANG Ji-qiang, YIN Shu-yan. Implement of Harmonics Detection With DSP Based on Instantaneous Reactive Power Theory[J]. Journal of Beijing University of Technology, 2002, 28(4): 495-499. |
| [10] | Zhang Sanle, Xiao Qiusheng, Ren Futian. Applied Study of Car-following Theory[J]. Journal of Beijing University of Technology, 1992, 18(3): 20-27. |
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: