Cross-Layer Diagnosis of Performance Anomalies in Integrated Wireless-Optical-Ethernet Networks

Recent works (e.g., Lai et al., 2014; Dhaini et al., 2010; Ahmed & Shami, 2012) explore integrating Ethernet with wireless and optical domains, but diagnosing end-to-end performance anomalies—especially across heterogeneous segments—remains unsolved. Building on Raca et al. (2024)’s lessons about real-world throughput prediction and Yuan et al. (2022)’s findings on 5G performance dependencies, this idea proposes a novel diagnostic engine that correlates metrics and events from all layers (from physical to application) and across all domains (wireless, optical, Ethernet). It uses AI for root-cause analysis and provides actionable insights for operators. This approach addresses the challenge of “where is the bottleneck?” in complex, multi-technology networks—crucial for future smart city and industrial deployments.

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