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

arXiv: https://arxiv.org/abs/2608.28865 · HTML: https://mlsanity.com/paper/2608.28865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 2 of 2)

- [Blind Modulation Classification via Combined Machine Learning and Signal Feature Extraction](https://mlsanity.com/paper/2101.04337/) (2021)
- [A Machine Learning Based Algorithm for Joint Improvement of Power Control, link adaptation, and Capacity in Beyond 5G Communication systems](https://mlsanity.com/paper/2201.07090/) (2022)

## Abstract (arXiv)

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.