Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation
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...
| Authors | Kosar Nourolahi, Vahid Ghasemi |
|---|---|
| Primary category | cs.NI |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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