SINR Estimation under Limited Feedback via Online Convex Optimization

TL;DR

This paper presents an online convex optimization method for real-time SINR estimation using limited feedback, outperforming existing schemes in accuracy and adaptability in dynamic wireless environments.

Contribution

It introduces a novel OCO-based framework with online parameter tuning and accelerated mirror descent for improved SINR estimation from limited feedback.

Findings

Outperforms state-of-the-art schemes in accuracy

Adapts robustly to time-varying SINR regimes

Demonstrates effectiveness in ray-traced scenarios

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.