Deep Learning based Multi-User Power Allocation and Hybrid Precoding in Massive MIMO Systems
Author:
Affiliation:
1. McGill University,Department of Electrical and Computer Engineering,Montreal,QC,Canada
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837954/9838246/09839162.pdf?arnumber=9839162
Reference20 articles.
1. Swarm Intelligence based Power Allocation in Hybrid Millimeter-Wave Massive MIMO Systems
2. Deep learning;lecun;Nature,2015
3. Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
4. Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
5. A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems
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1. Exploring Deep Learning for Improved MIMO Performance: A Design Research Methodology Approach;2023 International Conference on Electrical Engineering and Informatics (ICEEI);2023-10-10
2. Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders;IEEE Transactions on Cognitive Communications and Networking;2023-08
3. Intelligent Subcarrier Allocation in Hybrid Beamforming Multi-User mMIMO-OFDM Systems;2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring);2023-06
4. Uplink Scheduling for MIMO-OFDMA Systems with Rate Constraints by Deep Learning;2023 IEEE Wireless Communications and Networking Conference (WCNC);2023-03
5. Resource Allocation in Heterogeneous Network with Supervised GNNs;Lecture Notes in Computer Science;2023
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