Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation

• URL: http://arxiv.org/abs/2608.28865v1
• Date: Fri, 28 Aug 2026 21:06:06 GMT
• Title: Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation
• Abstract summary: Joint modulation recognition and signal-to-interference-plus-noise ratio estimation can reduce duplicated processing in intelligent receivers.<n>This letter proposes an uncertainty-aware multi-task model that transforms each short normalized in-phase/quadrature window into 36 deterministic, label-free statistics.<n>A joint uncertainty score combines classification entropy and predicted regression variance to support selective inference.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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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