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Downlink Pilots are Essential for Cell-Free Massive MIMO with Multi-Antenna Users
We consider a cell-free massive MIMO system with multiple antennas on the users and access points.
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.
Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks
Fog radio access networks (F-RANs), which consist of a cloud and multiple edge nodes (ENs) connected via fronthaul links, have been regarded as promising network architectures.
Learning Robust Beamforming for MISO Downlink Systems
This paper investigates a learning solution for robust beamforming optimization in downlink multi-user systems.
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.
