Accurate signal-to-interference-plus-noise ratio (SINR) estimation is essential for resource allocation in wireless systems, yet it is often hindered by limited and intermittent feedback. We propose an online convex optimization framework to estimate the SINR from ACK/NACK feedback, channel quality indicator (CQI) reports, and previously selected modulation and coding scheme (MCS) values. Specifically, we minimize a regularized binary cross entropy loss using a mirror descent method enhanced by Nesterov momentum for accelerated SINR tracking. The hyperparameters can be automatically tuned online via an expert-advice algorithm. Numerical experiments across multiple ray-traced scenarios show that our method consistently yields more accurate SINR estimates than state-of-the-art schemes and exhibits continual learning capabilities, which are essential for adapting to time-varying SINR regimes.

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