Explainable AI-Driven Geospatial Intelligence for Resilient Distributed 6G Communication Systems

Emerging sixth-generation (6G) networks require intelligent, geographically aware communication systems capable of adapting to dynamic traffic, mobility, interference, and infrastructure failures. However, conventional network optimization often lacks spatial context, transparent decision-making, and coordinated resilience across distributed edge-cloud environments. This study proposes an Explainable AI-Driven Geospatial Intelligence Framework (EAGI-6G) to improve network reliability, resource allocation, and communication performance. The framework integrates geospatial information systems, distributed machine learning, digital network representations, and explainable artificial intelligence (XAI). Geospatial features, including terrain, population density, mobility patterns, signal strength, and infrastructure locations, are fused with network telemetry to predict congestion, coverage degradation, and communication failures. A distributed learning architecture coordinates edge nodes and cloud services while minimizing centralized data dependence. Explainability methods, including SHAP and feature attribution, identify the spatial and network factors influencing decisions. Evaluation compares EAGI-6G against conventional geographic routing, centralized machine learning, distributed learning without geospatial features, and distributed learning without XAI. Key metrics include prediction accuracy, end-to-end latency, packet delivery ratio, network availability, recovery time, energy consumption, communication overhead, and explanation fidelity. The proposed evaluation targets measurable improvements over baseline methods: at least 10% higher failure-prediction F1-score, 15% lower end-to-end latency, 20% faster recovery, and 10% higher packet delivery ratio, alongside reduced energy consumption and communication overhead. These figures are research hypotheses, not experimentally established findings. Ablation studies will quantify the individual contributions of geospatial intelligence, distributed learning, and explainability under varying mobility, traffic, and failure scenarios. EAGI-6G establishes a testable framework for interpretable, spatially adaptive, and resilient 6G communication. Its comparative evaluation will clarify trade-offs among predictive performance, scalability, energy efficiency, transparency, and fault tolerance, supporting trustworthy network management for smart cities, connected infrastructure, and intelligent transportation. Keywords: distributed systems; Explainable Artificial Intelligence; Geospatial Intelligence; 6G communication; network resilience.