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Intelligent Signal Processing Techniques for Wireless Communications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 30 December 2026 | Viewed by 418

Special Issue Editor


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Guest Editor
Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Consiglio Nazionale delle Ricerche (CNR), P.zza L. da Vinci 32, I-20133 Milan, Italy
Interests: signal processing aspects of wireless communications systems; antenna array processing; channel estimation and tracking; MIMO-OFDM systems; cooperative communication; ad-hoc networking and wireless sensor networks
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Special Issue Information

Dear Colleagues,

Emerging wireless networks are becoming increasingly intelligent, autonomous, and adaptive, requiring signal processing frameworks that can effectively exploit data, context, and computational intelligence. This Special Issue seeks original contributions that explore the convergence of signal processing, artificial intelligence, and communication theory to enable next-generation wireless technologies. Topics of interest include innovative learning-based distributed algorithms for estimation, detection, and coding; AI-driven transceiver and network optimization; and intelligent spectrum, interference, and energy management. Works highlighting cross-disciplinary approaches—such as joint sensing and communication, distributed consensus approaches for multi-agent systems, semantic information processing, and hardware-aware intelligent design—are particularly encouraged.

Dr. Stefano Savazzi
Guest Editor

Manuscript Submission Information

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Keywords

  • AI-driven signal processing
  • learning-based wireless networks
  • intelligent transceiver design
  • semantic communications
  • network optimization
  • consensus-based distributed algorithms for multi-agent systems

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Published Papers (1 paper)

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Research

15 pages, 2746 KB  
Article
DGrA: Lightweight Modulation Recognition Based on Hybrid Neural Networks
by Xu Chen, Rui Gao, Ding Xu and Hongbo Zhu
Sensors 2026, 26(10), 3259; https://doi.org/10.3390/s26103259 - 21 May 2026
Viewed by 145
Abstract
Automatic modulation recognition has been recognized as an effective technique for non-cooperative communication and intelligent transmission. In this paper, we propose a new lightweight method for automatic modulation recognition, aiming to extract crucial discriminative features of signals for higher recognition accuracy while reducing [...] Read more.
Automatic modulation recognition has been recognized as an effective technique for non-cooperative communication and intelligent transmission. In this paper, we propose a new lightweight method for automatic modulation recognition, aiming to extract crucial discriminative features of signals for higher recognition accuracy while reducing spatial costs. To enhance the dissimilarity between samples, this paper combines an improved attention block and convolutional operations with the recurrent neural network, focusing on key features during the training phase to efficiently differentiate signal sequences. By replacing standard convolutions with depthwise separable convolutions, the model’s computational complexity is reduced while enhancing its feature extraction capability. Furthermore, the method incorporates pruning to reduce ineffective features, decreasing the model size while maintaining performance. Experimental results on RadioML2016.10a demonstrate that the proposed method outperforms other comparative methods, exhibiting both higher recognition accuracy and smaller model size. To validate real-world applicability, the algorithm was implemented on a software-defined radio platform for signal transmission and reception under practical conditions, achieving an accuracy of 87.22% in the presence of environmental noise, thus confirming its effectiveness in real-world scenarios. Full article
(This article belongs to the Special Issue Intelligent Signal Processing Techniques for Wireless Communications)
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