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Optimal Training Design for Over-the-Air Polynomial Power Amplifier Model Estimation
The current evolution towards a massive number of antennas and a large variety of transceiver architectures forces to revisit the conventional techniques used to improve the fundamental power amplifier (PA) linearity-efficiency trade-off.
Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non-Linear PA Distortion
In the four user-case, for a fixed sum rate, the total consumed power (PA and processing) of the GNN precoder is 3. 24 and 1. 44 times lower compared to ZF and ZF plus DPD respectively.
Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model
Massive multiple-input multiple-output (MIMO) precoders are typically designed by minimizing the transmit power subject to a quality-of-service (QoS) constraint.
Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems
Massive multiple input multiple output (MIMO) systems are typically designed under the assumption of linear power amplifiers (PAs).
