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Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation [article]

Yi Yuan, Gan Zheng, Kai-Kit Wong, Björn Ottersten, Zhi-Quan Luo
2020 arXiv   pre-print
To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and meta-learning, which are able to achieve fast adaptation with the limited new labelled  ...  The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance.  ...  To be specific, we propose the offline and online fast adaptive algorithms using transfer learning and meta learning techniques to solve the mismatch issue of beamforming design in dynamic wireless environments  ... 
arXiv:2011.00903v1 fatcat:trt22neppzaljdidr6bzwkul74

Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation

Yi Yuan, Gan Zheng, Kai-Kit Wong, Bjorn Ottersten, Zhi-Quan Luo
2020 IEEE Transactions on Wireless Communications  
To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and metalearning, which are able to achieve fast adaptation with the limited new labelled  ...  The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance.  ...  Comparison of transfer learning and meta leaning: Transfer learning and meta learning both have the training and adaption stages.  ... 
doi:10.1109/twc.2020.3035843 fatcat:z6d5muulcbcsromz2y3scnnzte

Embedding Model Based Fast Meta Learning for Downlink Beamforming Adaptation

Juping Zhang, Yi Yuan, Gan Zheng, Ioannis Krikidis, Kai-Kit Wong
2021 IEEE Transactions on Wireless Communications  
This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink.  ...  Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation.  ...  Transfer learning and meta learning have been recognized as two emerging techniques to design adaptive beamforming.  ... 
doi:10.1109/twc.2021.3094162 fatcat:4b7rrlb2obfyrhfi6ezhnedtaa

Fast Meta Learning for Adaptive Beamforming

Juping Zhang, Yi Yuan, Gan Zheng, Ioannis Krikidis, Kai-Kit Wong
2021 ICC 2021 - IEEE International Conference on Communications  
We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning.  ...  This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interferenceplus-noise ratio balancing problem.  ...  In the following, we will present the proposed fast meta learning algorithm and its application to design adaptive beamforming. A.  ... 
doi:10.1109/icc42927.2021.9500589 fatcat:4rleerxwd5cr5p4jypyxnjyw5e

Table of contents

2021 IEEE Wireless Communications Letters  
Seichiroh Osaki and Shinya Sugiura 1567 Adaptive Network Pruning for Wireless Federated Learning . . .  ...  Xuanhan Zhou, Jun Xiong, Xiaochen Zhang, Xiaoran Liu, and Jibo Wei 1552 Meta Distribution of Downlink SIR for Binomial Point Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lwc.2021.3087376 fatcat:z36xqldx25d35ntal2gpbkonke

Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems [article]

Yuwen Yang, Feifei Gao, Zhimeng Zhong, Bo Ai, Ahmed Alkhateeb
2020 arXiv   pre-print
To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a  ...  Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm in terms of both prediction accuracy and stability, which validates its effectiveness and superiority.  ...  algorithm as the gradsteps G Ad increases, which demonstrates that the meta-learning algorithm can find a better initialization for fast adaption than the direct-transfer algorithm.  ... 
arXiv:1912.12265v4 fatcat:mhyhgqbvlvbsbk6vyfrfgvi7se

Scanning the Literature

2021 IEEE wireless communications  
The core of the data processing is a deep-learning based multivariate long short term memory model that captures and predicts the spatiotemporal patterns and mobility.  ...  Cellular operators are facing fast growing mobile subscribers and surging traffic demand. More base stations and more powerful processing units are deployed to increase the network capacity.  ...  Downlink CSI Feedback Algorithm with Deep Transfer Learning for FDD Massive MIMO Systems Jun Zeng, Jinlong Sun, Guan Gui, Bamidele Adebisi, Tomoaki Ohtsuki, Haris Gacanin, and Hikmet Sari, IEEE Transactions  ... 
doi:10.1109/mwc.2021.9535469 fatcat:wno2m5nibbbshb7o2mlmkxeutm

2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
C., see Li, M., TWC Jan. 2020 650-664 Huang, A., see He, H., TWC Dec. 2020 7881-7896 Huang, C., Molisch, A.F., He, R., Wang, R., Tang, P., Ai, B., and Zhong, Z., Machine Learning-Enabled LOS/NLOS  ...  ., and Saad, W., Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach; TWC June 2020 3908-3923 Hu, Y.H., see Xia, M., TWC June 2020 3769-3781 Hua,  ...  ., +, TWC Oct. 2020 6948-6959 Fast Fourier transforms Fast Beam Search and Refinement for Millimeter-Wave Massive MIMO Based on Two-Level Phased Arrays.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

Table of contents

2021 IEEE Transactions on Wireless Communications  
Lars Grannemann, Aleksandar Ichkov, Petri Mähönen, and Ljiljana Simić Transfer Learning and Meta Learning-Based Fast Downlink Beamforming Adaptation .................................... ...............  ...  Thai-Chien Bui, Ravinder Singh, Timothy O'Farrell, and Mauro Biagi DeepWiPHY: Deep Learning-Based Receiver Design and Dataset for IEEE 802.11ax Systems .............  ... 
doi:10.1109/twc.2021.3056921 fatcat:jomcadm3izdixj5kmqyjmdbg3a

2021 Index IEEE Transactions on Wireless Communications Vol. 20

2021 IEEE Transactions on Wireless Communications  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TWC Jan. 2021 228-242 Transfer Learning and Meta Learning-Based Fast Downlink Beamforming Adaptation.  ... 
doi:10.1109/twc.2021.3135649 fatcat:bgd3vzb7pbee7jp75dnbucihmq

Artificial Intelligence Enabled NOMA Towards Next Generation Multiple Access [article]

Xiaoxia Xu, Yuanwei Liu, Xidong Mu, Qimei Chen, Hao Jiang, Zhiguo Ding
2022 arXiv   pre-print
Then, to achieve the vision of NGMA, a novel cluster-free NOMA framework is proposed for providing scenario-adaptive NOMA communications, and several promising machine learning solutions are identified  ...  To elaborate further, novel centralized and distributed machine learning paradigms are conceived for efficiently employing the proposed cluster-free NOMA framework in single-cell and multi-cell networks  ...  On the other hand, meta-learning, also known as learning to learn, aims to learn a general initial model that can be fast-adaptive to newly unseen communication scenarios.  ... 
arXiv:2206.04992v1 fatcat:zmfk3f5nvndnlpjoqbtour6uo4

Fast Data-Driven Adaptation of Radar Detection via Meta-Learning [article]

Wei Jiang, Alexander M. Haimovich, Mark Govoni, Timothy Garner, Osvaldo Simeone
2021 arXiv   pre-print
Furthermore, the meta-learning-based detector outperforms the transfer learning-based detector when the clutter is Gaussian.  ...  One approach is based on transfer learning: it first pre-trains a detector such that it works well on data collected in previously observed environments, and then it adapts the pre-trained detector to  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.  ... 
arXiv:2112.01780v1 fatcat:vmodofc73beejcdnggn7mmdweq

2019 Index IEEE Wireless Communications Letters Vol. 8

2019 IEEE Wireless Communications Letters  
., +, LWC June 2019 969-972 Meta Distribution of Downlink Non-Orthogonal Multiple Access (NOMA) in Poisson Networks.  ...  ., +, LWC June 2019 961-964 Wireless Power Transfer via mmWave Power Beacons With Directional Beamforming.  ... 
doi:10.1109/lwc.2019.2961756 fatcat:bwxehcl4ejew7a6m66prb6s4z4

2020 Index IEEE Transactions on Vehicular Technology Vol. 69

2020 IEEE Transactions on Vehicular Technology  
T., R., TVT Dec. 2020 16218-16223 Hoon-Kim, T., see Kumar, G., TVT July 2020 7707-7722 Horlin, F., see Monfared, S., TVT Oct. 2020 11369-11382 Horng, S., Lu, C., and Zhou, W., An Identity-Based and  ...  Yu, X., A Joint Design of Platoon Communication and Control Based on LTE-V2V; 15893-15907 Hong, C.S., see Nguyen, M.N.H., TVT May 2020 5618-5633 Hong, C.S., see Chen, D., TVT May 2020 5634-5646 Hong  ...  ., +, TVT Jan. 2020 1065-1069 Fast Specific Absorption Rate Aware Beamforming for Downlink SWIPT via Deep Learning.  ... 
doi:10.1109/tvt.2021.3055470 fatcat:536l4pgnufhixneoa3a3dibdma

2021 Index IEEE Transactions on Vehicular Technology Vol. 70

2021 IEEE Transactions on Vehicular Technology  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TVT July 2021 7176-7181 Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications.  ... 
doi:10.1109/tvt.2022.3151213 fatcat:vzuzqu54irebpibzp3ykgy5nca
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