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Particular WLAN pathologies experienced in realistic scenarios are hard to detect, due to the complex nature of the wireless medium. Prior work has employed sophisticated equipment, driver modifications,or even application-layer techniques, towards diagnosing such pathologies. The key novelty of our approach lies in the identification of metrics able to characterize the root causes of individual pathologies, while also being directly extractable from MAC-layer statistics available in today’s wireless equipment. Through the development of the proposed framework as application-layer software on top of commercial hardware and its experimental evaluation, we validate the efficiency and applicability of our approach.