English(EN) Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation

新的多任务学习模型增强了信号调制识别和SINR估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种不确定性感知多任务学习模型，旨在改进联合调制识别和信噪比（SINR）估计。该模型将归一化的同相/正交窗口处理成确定性统计量，并利用特定任务的适配器进行分类和回归。它结合了分类熵和预测回归方差的联合不确定性得分，以实现选择性推理。仿真表明，在各种信道条件下，与传统多任务学习相比，该模型显著提高了准确性，同时降低了SINR的平均绝对误差。 AI

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了一种新颖的机器学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Kosar Nourolahi, Vahid Ghasemi · 2026-09-01 04:00

面向联合调制识别和SINR估计的不确定性感知多任务学习

arXiv:2608.28865v1 Announce Type: cross Abstract: 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 propos…

本文如何被排名

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面向联合调制识别和SINR估计的不确定性感知多任务学习

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