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A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites
Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty cooling controllers, idle radio equipment, and parasitic auxiliary loads often remain undetected because no ground-truth inefficiency labels exist
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