Title: SALAD: Self-Adaptive Link Adaptation Show Chinese title

Title: SALAD：自适应链路适配

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. Show Chinese abstract

Abstract: 适应调制和编码方案（MCS）以满足无线链路质量对于在确保可靠性的同时最大化频谱效率至关重要。 我们提出SALAD（自适应链路适配），一种仅利用ACK/NACK反馈来可靠跟踪信噪干扰比（SINR）演化的算法，在保持长期块错误率（BLER）接近期望目标的同时实现高频谱效率。 SALAD通过最小化接收到的ACK/NACK与预测的BLER值之间的交叉熵损失来推断SINR，其学习率通过知识蒸馏在线自适应调整。 基于此推断，SALAD通过假设检验选择MCS：如果SINR可能被低估，则在信道条件改善的情况下选择更高的MCS以加速链路适配。 为防止BLER偏离其长期目标，SALAD引入一个反馈控制环路来调整瞬时BLER目标。 在5G测试平台上的空中实验表明，SALAD始终优于行业标准的外环链路适配（OLLA）。 使用一组参数，SALAD在不同业务模式下比多个OLLA变体在吞吐量和频谱效率方面高出高达15%，同时满足BLER目标。