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. 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.

翻译：根据无线链路质量自适应调制与编码方案（MCS）对于在保证可靠性的同时最大化频谱效率至关重要。本文提出SALAD（自适应链路适配）算法，该算法仅利用ACK/NACK反馈即可可靠跟踪信干噪比（SINR）的动态变化，在将长期误块率（BLER）维持在期望目标附近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BLER值之间的交叉熵损失来推断SINR。基于此推断，SALAD通过假设检验选择MCS：若SINR可能存在低估，则选择更高阶MCS以在信道条件改善时加速链路适配。为防止BLER偏离其长期目标，SALAD引入反馈控制环路以调整瞬时BLER目标。在5G测试平台上进行的空口实验表明，SALAD持续优于行业标准的外环链路适配（OLLA）方案。在单一参数配置下，SALAD在不同业务场景中相比多种OLLA变体可实现高达15%的吞吐量与频谱效率提升，同时满足BLER目标要求。