SINR Estimation under Limited Feedback via Online Convex Optimization
Information Theory
2026-03-16 v2 math.IT
Abstract
We introduce a novel online convex optimization (OCO) framework to estimate the user's signal-to-interference-plus-noise ratio (SINR) from ACK/NACK feedback, channel quality indicator (CQI) reports, and previously selected modulation and coding scheme (MCS) values. Specifically, the proposed approach minimizes a regularized binary cross-entropy loss using mirror descent enhanced with Nesterov momentum for accelerated SINR tracking. Its parameters are tuned online via an expert-advice algorithm, endowing the estimator with continual learning capabilities. Numerical experiments in ray-traced scenarios show that the proposed method outperforms state-of-the-art schemes in estimation accuracy and adapts robustly to time-varying SINR regimes.
Cite
@article{arxiv.2603.02061,
title = {SINR Estimation under Limited Feedback via Online Convex Optimization},
author = {Lorenzo Maggi and Boris Bonev and Reinhard Wiesmayr and Sebastian Cammerer and Alexander Keller},
journal= {arXiv preprint arXiv:2603.02061},
year = {2026}
}