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DUET-Net: A Physics-Aware Deep Learning Framework for Joint Sensing and Communication Channel Estimation in ISAC Systems
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DOI：10.1109/tvt.2026.3666305.png)
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Abstract
En 中文
Joint sensing and communication channel estimation (JSCCE) is a critical problem in integrated sensing and communication (ISAC) systems, where two types of channels inherently share similar physical propagation mechanisms while exhibit distinct task-specific characteristics. Leveraging this property, this work proposes a multi-objective unified framework that combines the feature extraction capacity of deep learning with the principled exploitation of ISAC channel commonalities and differences. Specifically, we design a novel dual-task unified estimation Transformer network (DUET-Net) comprising a shared encoder backbone for capturing correlated sparse channel features, alongside dual-head decoders with task-specific branches for accommodating the individual statistical characteristics and structural properties of the two channels. To further address the limited angular resolution in communication channels, we incorporate an angle-aware attention (AAA) module that selectively emphasizes informative angular components. Moreover, an uncertainty-weighted multi-task loss is introduced to adaptively balance the training process of both tasks in accordance to their different learning dynamics, thereby improving convergence stability. Extensive numerical simulations demonstrate that the proposed DUET-Net consistently outperforms existing methods over a wide range of transmit power levels and across various scenarios, with an efficient performance-complexity trade-off. The effectiveness of the designed modules is further confirmed by ablation studies.
Keywords:
Integrated sensing and communication
joint channel estimation
deep learning
Transformer
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7.1
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1.7W
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