同时识别调制方式并估计信干噪比，提升智能接收机性能。
Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation
- 用36个无标签统计量表示信号，共享特征并分任务适配
- 在匹配与陌生信道上准确率分别提升14.86和8.61个百分点
- 通过置信度筛选降低信道不匹配时的误判率
联合调制识别与信干噪比（SINR）估计可减少智能接收机中的重复处理，但两项任务具有不同的不确定性特征。本文提出一种不确定性感知的多任务模型，将每个短时归一化I/Q窗口转换为36个确定性、无标签统计量，学习共享表示，并使用任务特定适配器完成调制分类与异方差SINR回归。通过结合分类熵与预测回归方差构建联合不确定性评分，支持选择性推理。仿真涵盖QPSK、8PSK、16QAM和64QAM，在匹配的加性高斯白噪声/瑞利信道及未见过的频率选择性瑞利信道下测试。在五个独立种子下，该模型在匹配与未知信道上的准确率分别较传统多任务学习提升14.86和8.61个百分点，同时将SINR平均绝对误差降低1.60和1.61 dB。基于置信度的拒绝策略进一步降低了信道失配下的调制错误率。
原文摘要 · Abstract (English)
Joint modulation recognition and signal-to-interference-plus-noise ratio (SINR) estimation can reduce duplicated processing in intelligent receivers, but the two tasks have different uncertainty characteristics. This letter proposes an uncertainty-aware multi-task model that transforms each short normalized in-phase/quadrature window into 36 deterministic, label-free statistics, learns a shared representation, and uses task-specific adapters for modulation classification and heteroscedastic SINR regression. A joint uncertainty score combines classification entropy and predicted regression variance to support selective inference. Simulations cover QPSK, 8PSK, 16QAM, and 64QAM under matched additive white Gaussian noise/Rayleigh channels and an unseen frequency-selective Rician channel. Over five independent seeds, the proposed model improves matched and unseen-channel accuracy over conventional multi-task learning by 14.86 and 8.61 percentage points, respectively, while reducing SINR mean absolute error by 1.60 and 1.61 dB. Confidence-based rejection further lowers modulation error under channel mismatch.
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