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New multi-task learning model enhances signal modulation recognition and SINR estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an uncertainty-aware multi-task learning model designed to improve joint modulation recognition and signal-to-interference-plus-noise ratio (SINR) estimation. This model processes normalized in-phase/quadrature windows into deterministic statistics, utilizing task-specific adapters for classification and regression. It incorporates a joint uncertainty score, combining classification entropy and predicted regression variance, to enable selective inference. Simulations demonstrated significant accuracy improvements over conventional multi-task learning in various channel conditions, while also reducing SINR mean absolute error. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a novel machine learning model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multi-task learning model enhances signal modulation recognition and SINR estimation

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The cluster contains a research paper published on arXiv detailing a novel machine learning model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kosar Nourolahi, Vahid Ghasemi ·

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

    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…

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