SINR Estimation under Limited Feedback via Online Convex Optimization

2026-03-02

AI Summary

This work addresses the challenge of accurately estimating the signal-to-interference-plus-noise ratio (SINR) in wireless communication systems, where limited and intermittent feedback hampers efficient resource allocation. The authors propose an online convex optimization framework that dynamically estimates SINR by leveraging ACK/NACK feedback, channel quality indicators (CQI), and historical modulation and coding scheme (MCS) selections through a regularized binary cross-entropy loss. Innovatively integrating mirror descent with Nesterov momentum accelerates tracking of time-varying channel conditions, while an expert-advice algorithm enables online self-adaptation of hyperparameters, endowing the system with continual learning capability. Evaluated across diverse ray-tracing scenarios, the proposed method significantly outperforms existing approaches, achieving both high estimation accuracy and robust adaptability to non-stationary channels.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• SINR estimation
• limited feedback
• wireless systems
• resource allocation
• online learning

Innovation

• online convex optimization
• SINR estimation
• mirror descent
• Nesterov momentum
• expert-advice algorithm