发表机构
Kermanshah University of Technology（克尔曼沙赫理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对联合调制识别与SINR估计的任务不确定性差异，提出不确定性感知多任务模型，通过任务适配器等设计提升了不同信道下的准确率并降低了SINR估计误差，还可通过置信度弃权机制优化性能。
AI 中文摘要
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，但两个任务具有不同的不确定性特性。本文提出一种不确定性感知多任务模型，该模型将每个短的归一化同相/正交（I/Q）窗口转换为36个确定性、无标签的统计量，学习共享表示，并使用任务特定适配器分别完成调制分类和异方差SINR回归。联合不确定性得分结合分类熵和预测的回归方差，以支持选择性推理。仿真覆盖了QPSK、8PSK、16QAM和64QAM在匹配的加性高斯白噪声（AWGN）/瑞利信道以及未见过的频率选择性莱斯信道下的情况。在5个独立随机种子的实验中，所提模型在匹配信道和未见过信道上的准确率相比传统多任务学习分别提升了14.86和8.61个百分点，同时将SINR的平均绝对误差分别降低了1.60和1.61 dB。基于置信度的弃权（不执行）机制进一步降低了信道失配下的调制误差。
英文摘要
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.
Comments4 pages, 6 figures
