SALAD: Self-Adaptive Link Adaptation

2025-10-07

AI Summary

In wireless link adaptation, it is challenging to jointly optimize spectral efficiency and block error rate (BLER) reliability. Method: This paper proposes SALAD—a novel adaptive algorithm that (i) estimates SINR online via cross-entropy loss minimization using ACK/NACK feedback; (ii) employs knowledge distillation for adaptive learning-rate tuning; (iii) integrates hypothesis testing to accelerate MCS selection under abrupt channel variations; and (iv) establishes a closed-loop feedback mechanism to dynamically adjust the instantaneous BLER target, thereby stabilizing long-term BLER. Contribution/Results: Unlike conventional outer-loop adaptation, SALAD eliminates manual parameter tuning. Evaluated on real 5G deployments, it achieves up to 15% gains in spectral efficiency and throughput while precisely maintaining the target BLER of 10⁻². The approach significantly enhances robustness and generalization capability of link adaptation.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Adapting the modulation and coding scheme (MCS) to the wireless link quality is critical for maximizing spectral efficiency while ensuring reliability. We propose SALAD (self-adaptive link adaptation), an algorithm that exclusively leverages ACK/NACK feedback to reliably track the evolution of the signal-to-interference-plus-noise ratio (SINR), achieving high spectral efficiency while keeping the long-term block error rate (BLER) near a desired target. SALAD infers the SINR by minimizing the cross-entropy loss between received ACK/NACKs and predicted BLER values, with a learning rate that self-adapts online through knowledge distillation. Based on this inference, SALAD selects the MCS via hypothesis testing: if the SINR is likely underestimated, a higher MCS is selected to accelerate link adaptation under improving channel conditions. To prevent BLER drift from its long-term target, SALAD incorporates a feedback control loop that adjusts the instantaneous BLER target. Over-the-air experiments on a 5G testbed demonstrate that SALAD consistently outperforms the industry-standard outer-loop link adaptation (OLLA). With a single set of parameters, SALAD achieves up to 15% higher throughput and spectral efficiency than multiple OLLA variants across different traffic regimes, while meeting the BLER target.

Problem

• Adapting modulation and coding to wireless link quality
• Tracking SINR evolution using only ACK/NACK feedback
• Maintaining long-term BLER target while maximizing spectral efficiency

Innovation

• Uses ACK/NACK feedback for SINR tracking
• Employs hypothesis testing for MCS selection
• Incorporates feedback control for BLER stabilization