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. Deep Reinforcement Learning-based Sum-Rate Maximization in Hybrid Beamforming Multi-User Massive MIMO Systems;2024 Tenth International Conference on Communications and Electronics (ICCE);2024-07-31
2. Deep Learning-Based Hybrid Precoding Approach in the Massive Multiple-Input Multiple-Output System;IETE Journal of Research;2024-07-07
3. Resource allocation in heterogeneous network with node and edge enhanced graph attention network;Applied Intelligence;2024-03
4. Exploring Deep Learning for Improved MIMO Performance: A Design Research Methodology Approach;2023 International Conference on Electrical Engineering and Informatics (ICEEI);2023-10-10
5. Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders;IEEE Transactions on Cognitive Communications and Networking;2023-08
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