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Found 4 papers, 0 papers with code
Learning Decentralized Power Control in Cell-Free Massive MIMO Networks
This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination.
A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization
However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i. e., the number of antennas or users.
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies.
Deep Learning Methods for Universal MISO Beamforming
This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station.
