Neuro-Symbolic Digital Twin architecture for semantic trust, green beamforming, and autonomous GEO satellite communications

Abstract Future space-terrestrial networks demand autonomy, sustainability, and trust-aware intelligence. This paper proposes a Neuro-Symbolic Digital Twin (NSDT) architecture tailored for geostationary (GEO) satellites. By fusing neural inference with symbolic reasoning, NSDT delivers interpretable, resilient decision-making under uncertain or hostile conditions. Key innovations include a semantic trust layer, green beamforming and a novel energy metric ${\varepsilon _{NS}}$ . High-fidelity MATLAB simulations demonstrate 35–40% energy savings, 3–5 dB SINR gains, 0.8–0.9 resilience, and latency of 120–160 ms versus conventional approaches.