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.

翻译：精确的信干噪比估计对于无线系统中的资源分配至关重要，但常受限于有限且间歇的反馈。本文提出一种在线凸优化框架，利用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SINR。具体而言，我们通过采用Nesterov动量加速的镜像下降法最小化正则化二元交叉熵损失，以实现快速SINR跟踪。超参数可通过专家建议算法在线自动调优。在多种射线追踪场景下的数值实验表明，相较于现有最优方案，本方法能持续获得更精确的SINR估计值，并展现出持续学习能力，这对于适应时变SINR环境至关重要。