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.

翻译：本文提出了一种新颖的在线凸优化框架，用于通过ACK/NACK反馈、信道质量指示报告以及先前选定的调制编码方案值来估计用户的信号干扰噪声比。具体而言，所提方法通过采用Nesterov动量增强的镜像下降算法最小化正则化二元交叉熵损失，以实现加速的SINR跟踪。其参数通过专家建议算法在线调整，使估计器具备持续学习能力。在射线追踪场景中的数值实验表明，所提方法在估计精度上优于现有最优方案，并能稳健地适应时变的SINR环境。