SINR Estimation under Limited Feedback via Online Convex Optimization
This work addresses SINR estimation for wireless communication systems, offering incremental improvements over existing methods.
The paper tackles the problem of estimating signal-to-interference-plus-noise ratio (SINR) with limited feedback by introducing an online convex optimization framework, achieving improved estimation accuracy and robust adaptation in time-varying scenarios as shown in numerical experiments.
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.