Explainable Artificial Intelligence for Spatial-Temporal Wireless Traffic Prediction and Network Optimization (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The accelerating complexity of wireless networks, driven by dense heterogeneous deployments and the proliferation of bandwidth-intensive applications, demands predictive intelligence that can anticipate spatial-temporal traffic dynamics with high fidelity. Deep learning models have demonstrated remarkable accuracy in forecasting mobile traffic by capturing intricate non-linear dependencies across time and space. However, their adoption in operational network optimization remains constrained by the black-box nature of these models, which obscures the rationale behind predictions and hinders trust, debugging, and regulatory compliance. This paper provides a comprehensive system-level examination of explainable artificial intelligence (XAI) applied to spatial-temporal wireless traffic prediction and its integration into autonomous network optimization pipelines. We discuss the structural challenges of representing and interpreting complex spatio-temporal patterns, the architectural trade-offs involved in embedding explanation engines within existing network management systems, and the implications for resource allocation, load balancing, and energy efficiency. The analysis extends to governance and fairness, highlighting how XAI can support equitable service provisioning, bias detection, and accountability under emerging regulatory frameworks. Robustness, sustainability, and the lifecycle of explainable predictors are examined in the context of concept drift, adversarial manipulation, and the carbon footprint of AI operations. By synthesizing insights from machine learning, network architecture, and policy studies, the paper outlines a cohesive vision for responsible and transparent intelligence in next-generation wireless infrastructures, emphasizing that explainability is not merely an add-on but a foundational requirement for safe, efficient, and human-aligned network autonomy.
Keywords: explainable artificial intelligence, wireless traffic prediction, spatial-temporal modeling, network optimization, transparent machine learning, 5G and beyond, network governance, sustainable AI