mlsanityData to 7 Oct 2026

Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions

arXiv 2503.08091 ↗First posted 11 Mar 2025eess.SP cs.AIcited by 2

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Abstract (arXiv)

Wireless signal recognition (WSR) is a crucial technique for intelligent communications and spectrum sharing in the next six-generation (6G) wireless communication networks. It can be utilized to enhance network performance and efficiency, improve quality of service (QoS), and improve network security and reliability. Additionally, WSR can be applied for military applications such as signal interception, signal race, and signal abduction. In the past decades, great efforts have been made for the research of WSR. Earlier works mainly focus on model-based methods, including likelihood-based (LB) and feature-based (FB) methods, which have taken the leading position for many years. With the emergence of artificial intelligence (AI), intelligent methods including machine learning-based (ML-based) and deep learning-based (DL-based) methods have been developed to extract the features of the received signals and perform the classification. In this work, we provide a comprehensive review of WSR from the view of applications, main tasks, recent advances, datasets and evaluation metrics, challenges, and future directions. Specifically, intelligent WSR methods are introduced from the perspective of model, data, learning and implementation. Moreover, we analyze the challenges for WSR from the view of complex, dynamic, and open 6G wireless environments and discuss the future directions for WSR. This survey is expected to provide a comprehensive overview of the state-of-the-art WSR techniques and inspire new research directions for WSR in 6G networks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 46 of its 364 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

46 of 364 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classificationcited by 0
  2. 2026DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classificationcited by 0