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A Deep Generative Model for Synthesizing Labeled Wireless Signals (IIns-GAN)

Forum topic · 小凯 · 2026-09-08

Summary

This forum post shares the arXiv paper 'A Deep Generative Model for Synthesizing Labeled Wireless Signals' (arXiv:2609.05396) by Yuxiao Li, Keke Hu, Santiago Mazuelas, and Yuan Shen. Labeled wireless signals with position-related annotations are essential for performance evaluation and model training in wireless sensing, but real-world datasets are costly to measure and label. Traditional synthesis methods based on environmental models require extensive hyper-parameter tuning and lack realism for training purposes. The authors propose Inter-Instance Generative Adversarial Networks (IIns-GAN), a deep learning approach that generates realistic labeled wireless signals. The generated signals adapt to different environment scenarios and suit various model training tasks. The post was auto-collected on zhichai.net, a Chinese tech forum.

Paper Overview

  • Field: Machine Learning
  • Authors: Yuxiao Li, Keke Hu, Santiago Mazuelas, Yuan Shen
  • Published: 2026-09-04
  • arXiv: 2609.05396

Abstract (translated/condensed)

Wireless signals with position-related labels are pivotal for both performance evaluation and model training in the realm of wireless sensing. However, acquiring real-world datasets is often challenged by significant measurement and labeling costs. Traditional methods for synthesizing labeled wireless signals typically rely on environmental models, leading to extensive hyper-parameter tuning and inadequate realism for comprehensive model training purposes.

To address these limitations, the authors introduce a novel deep learning (DL)-based method, namely Inter-Instance Generative Adversarial Networks (IIns-GAN), to generate realistic labeled wireless signals. The generated signals are particularly adaptive to different environment scenarios and well-suited for various model training tasks.

> Note: The Chinese summary of this post was not yet translated (translation missing). The original abstract in the source is truncated; see the arXiv link above for the full paper.

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*Auto-collected on 2026-09-08.*

Tags

#machine-learning#generative-adversarial-networks#wireless-sensing#deep-learning#data-synthesis#arxiv-paper

This page is an English static mirror generated for search and AI citation. It may be a full translation or structured summary of the Chinese original. Canonical interactive discussion lives on the Chinese page: https://zhichai.net/topic/178634625