Physics-Driven Framework for QoS-Constrained Satellite System with Non-Linear PA Distortion
This work develops a physics-driven self-supervised graph learning framework that directly optimizes the distortion-aware precoder under nonlinear correlated power amplifier (PA) distortion, without relying on expensive optimizer-generated labels for a user-centric satellite system. We minimize nonlinear PA consumption subject to user quality-of-service (QoS) constraints under PA nonlinearity for a downlink multi-input-single-output (MISO) satellite system. Our pipeline jointly designs the precoder and admission control that minimize nonlinear PA consumed power. In particular, a graph neural network (GNN) for the design of a distortion-aware precoder is optimized end-to-end in a self-supe...
| Authors | Hamza Ahmed Qureshi, Abuzar Babikir Mohammad Adam, Vu Nguyen Ha et al. |
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| Primary category | eess.SP |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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