Deep Reinforcement Learning for Multi-User Massive MIMO With Channel Aging
Author:
Affiliation:
1. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9882533/9895461/10286867.pdf?arnumber=10286867
Reference47 articles.
1. Addressing the Curse of Mobility in Massive MIMO With Prony-Based Angular-Delay Domain Channel Predictions
2. Beamforming in Multi-User MISO Cellular Networks with Deep Reinforcement Learning
3. Distributed Space–Time Interference Alignment With Moderately Delayed CSIT
4. Joint Coordinated Beamforming and Power Splitting Ratio Optimization in MU-MISO SWIPT-Enabled HetNets: A Multi-Agent DDQN-Based Approach
5. Sum-Rate and Power Scaling of Massive MIMO Systems With Channel Aging
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