Abstract--Integrated Satellite-Aerial-Terrestrial Networks (ISATNs) are envisioned as key enablers of 6G, providing global connectivity for applications such as autonomous transportation, Industrial IoT, and disaster response. ISATN-specific control knobs, including carbon-aware handovers, UA V duty-cycling, and renewable-aware edge placement, are exploited to reduce emissions. I. Introduction The rapid evolution of next-generation communication systems is driving the integration of Satellite, Aerial, and Terrestrial Networks (ISATNs) into a unified infrastructure capable of delivering seamless global connectivity. This convergence is critical for enabling emerging applications such as autonomous transportation, Industrial Internet of Things (IIoT), remote healthcare, and disaster response, where reliable, low-latency, and high-capacity communication is essential [ 1 ]. However, the energy consumption associated with operating dense terrestrial base stations, satellite constellations, and aerial platforms introduces significant carbon emissions, posing new challenges for designing energy-efficient and environmentally sustainable integrated networks. As communication networks scale toward 6G and beyond, addressing carbon emissions and energy optimization has become a priority. The increasing reliance on renewable energy sources and fluctuating carbon intensity in power grids demand intelligent orchestration mechanisms capable of balancing Quality of Service (QoS) with environmental impact. S. Javaid is with the College of Electronics and Information Engineering, Tongji University, Shanghai 201804, and the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 201210, China N. Saeed is with the Department of Electrical and Communication Engineering, College of Engineering, UAE University, Al-Ain 15551, UAE (e-mail: mr.nasir.saeed@ieee.org).

Multi-agent systems (MAS) are foundational in simulating complex real-world scenarios involving autonomous, interacting entities. However, traditional MAS architectures often suffer from rigid coordination mechanisms and difficulty adapting to dynamic tasks. We propose MetaOrch, a neural orchestration framework for optimal agent selection in multi-domain task environments. Our system implements a supervised learning approach that models task context, agent histories, and expected response quality to select the most appropriate agent for each task. A novel fuzzy evaluation module scores agent responses along completeness, relevance, and confidence dimensions, generating soft supervision labels for training the orchestrator. Unlike previous methods that hard-code agent-task mappings, MetaOrch dynamically predicts the most suitable agent while estimating selection confidence. Experiments in simulated environments with heterogeneous agents demonstrate that our approach achieves 86.3% selection accuracy, significantly outperforming baseline strategies including random selection and round-robin scheduling. The modular architecture emphasizes extensibility, allowing agents to be registered, updated, and queried independently. Results suggest that neural orchestration offers a powerful approach to enhancing the autonomy, interpretability, and adaptability of multi-agent systems across diverse task domains.

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