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Electronics
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AI-Driven Signal Processing in Communications
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AI-Driven Signal Processing in Communications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 3086

Special Issue Editors

Dr. Tao Guo

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
Interests: network information theory; information theoretic security; privacy protection; signal processing for communications
Dr. Huihui Wu

E-Mail Website
Guest Editor
Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo 315100, China
Interests: data compression; signal quantization; channel coding; semantic communications; deep learning; AI-empowered communications
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoyu Zhao

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
Interests: ultra-reliable and low-latency communications; wireless extended reality; cross-layer optimization; physical layer security
Dr. Mingjie Shao

E-Mail Website
Guest Editor
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Interests: signal processing; convex and non-convex optimization; model-based machine learning

Special Issue Information

Dear Colleagues,

The relentless evolution of communication systems, spurred by the demands of 5G, Beyond 5G, and the emerging 6G era, necessitates a paradigm shift in how we approach signal processing. Artificial intelligence (AI), encompassing machine learning, deep learning, and reinforcement learning, has emerged as a transformative force, offering powerful tools to overcome traditional limitations and unlock unprecedented performance in communication signal processing. From the physical layer intricacies to network-wide optimizations, AI is enabling intelligent adaptation, robust operation in complex environments, and the efficient handling of massive data streams inherent in modern wireless and mobile communications.

This Special Issue in Electronics aims to be a premier platform for disseminating cutting-edge research on the theoretical foundations, novel algorithms, practical implementations, and future challenges of AI-driven signal processing in communications. We seek contributions that fundamentally advance how signals are generated, transmitted, received, and interpreted using AI techniques. We are particularly interested in work that addresses the unique signal processing challenges posed by dynamic channel conditions, massive connectivity, ultra-low latency requirements, extreme bandwidths, and the need for energy-efficient solutions. Topics of interest include, but are not limited to, the following:

  • AI-driven signal processing for wireless and wireline communications;
  • Machine/deep learning for physical layer design in Beyond 5G and 6G access and core networks;
  • AI-based channel estimation, prediction, and equalization;
  • Intelligent signal detection and interference management/mitigation;
  • AI-powered resource allocation and network optimization;
  • Machine learning for waveform design, modulation, and coding;
  • AI applications in massive MIMO, smart antennas, and beamforming;
  • Signal processing for AI-enabled Internet of Everything (IoE) and IoT networks;
  • AI-driven signal processing in autonomous driving, V2X solutions, and vehicular networks;
  • Energy-efficient and low-latency AI signal processing for edge computing in communications;
  • AI for semantic communications and holographic communication signal processing;
  • Security and privacy in AI-driven communication signal processing (e.g., adversarial attacks and defenses);
  • Big data analytics and AI for communication signal intelligence;
  • AI-driven integrated sensing and communication (ISAC);
  • AI-driven designs for reconfigurable intelligent surfaces (RIS) and movable/fluid antennas.

Dr. Tao Guo
Dr. Huihui Wu
Dr. Xiaoyu Zhao
Dr. Mingjie Shao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI) in communications
  • machine learning for signal processing
  • deep learning for wireless communications
  • intelligent signal processing
  • 5G/6G signal processing
  • AI-based channel estimation
  • AI for physical layer communications
  • reinforcement learning in communications
  • semantic communications signal processing

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Published Papers (3 papers)

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Research

22 pages, 5092 KB  
Article
A Frequency Identification Method for Differential Frequency-Hopping Signals Based on the Super-Resolution Reconstruction of Time–Frequency Images
by Pengteng Yang, Bo Qian, Bingzhen Mu, Mingjiao Qi and Hailong Wang
Electronics 2026, 15(10), 2070; https://doi.org/10.3390/electronics15102070 - 12 May 2026
Viewed by 339
Abstract
The frequency identification technology of differential frequency-hopping (DFH) signals is the key to decoding at the receiver. Aiming to improve frequency identification accuracy under low signal-to-noise ratio (SNR) conditions, a method based on super-resolution image reconstruction technology is proposed for the instantaneous frequency [...] Read more.
The frequency identification technology of differential frequency-hopping (DFH) signals is the key to decoding at the receiver. Aiming to improve frequency identification accuracy under low signal-to-noise ratio (SNR) conditions, a method based on super-resolution image reconstruction technology is proposed for the instantaneous frequency identification of DFH signals. Firstly, the time–frequency image of the DFH signal is obtained using short-time Fourier transform (STFT). Then, a U-Net neural network with an attention mechanism is designed to suppress noise and interference components in the time–frequency image and reconstruct a super-resolution time–frequency image. Furthermore, based on the correlation between adjacent hop signals in accordance with the frequency transfer function, a ResNet neural network is designed to identify frequencies from the super-resolution time–frequency image of DFH signals. Simulation results demonstrate that the designed U-Net neural network can effectively suppress noise and interference components and reconstruct high-quality super-resolution time–frequency images. Comparative experimental results show that the proposed ResNet neural network can significantly improve the identification accuracy of DFH signals under low-SNR conditions. Specifically, the identification accuracy can reach more than 90% when the low SNR is not less than −10 dB, which is a significant improvement compared with other methods. Ablation experiment results indicate that the attention mechanism can improve model performance by 3.74%. Full article
(This article belongs to the Special Issue AI-Driven Signal Processing in Communications)
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19 pages, 956 KB  
Article
ResDiff: Hardware-Aware Physical-Layer Covert Communication via Diffusion-Based Residual Perturbation
by Qi Feng, Junyi Zhang, Qiang Li, Mingdi Li and Li Chen
Electronics 2026, 15(3), 635; https://doi.org/10.3390/electronics15030635 - 2 Feb 2026
Cited by 1 | Viewed by 794
Abstract
Physical-layer covert communication is increasingly challenged by powerful detectors that exploit the fine-grained statistical structure of received signals. In realistic Radio Frequency (RF) front ends, signal-dependent impairments such as power amplifier (PA) nonlinearity and In-phase and Quadrature (I/Q) imbalance induce transmitter-specific, non-Gaussian emission [...] Read more.
Physical-layer covert communication is increasingly challenged by powerful detectors that exploit the fine-grained statistical structure of received signals. In realistic Radio Frequency (RF) front ends, signal-dependent impairments such as power amplifier (PA) nonlinearity and In-phase and Quadrature (I/Q) imbalance induce transmitter-specific, non-Gaussian emission statistics under which conventional Gaussian embedding rules cause detectable distribution drift. We propose ResDiff, a two-stage learn-then-embed framework that first trains a symbol-conditional diffusion prior to capture a hardware-consistent emission manifold, then embeds covert information through bounded, variance-adaptive residuals spread over a K-symbol block with coherent block decoding at the legitimate receiver. Simulations under a severe impairment profile in an Additive White Gaussian Noise (AWGN) channel show that ResDiff improves stealthiness while maintaining reliable covert recovery and that increasing K reduces detectability by lowering the per symbol embedding pressure. Overall, the results indicate that hardware-aware generative priors, combined with rate-controlled block embedding, provide a practical path to covert-in-cover-traffic communication under modern detection capabilities. Full article
(This article belongs to the Special Issue AI-Driven Signal Processing in Communications)
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17 pages, 49679 KB  
Article
A Lightweight Denoising Network with TCN–Mamba Fusion for Modulation Classification
by Yubo Kong, Yang Ge and Zhengbing Guo
Electronics 2026, 15(1), 188; https://doi.org/10.3390/electronics15010188 - 31 Dec 2025
Cited by 1 | Viewed by 1124
Abstract
Automatic modulation classification (AMC) under low signal-to-noise ratio (SNR) and complex channel conditions remains a significant challenge due to the trade-off between robustness and efficiency. This study proposes a lightweight temporal convolutional network (TCN) and Mamba fusion architecture designed to enhance modulation recognition [...] Read more.
Automatic modulation classification (AMC) under low signal-to-noise ratio (SNR) and complex channel conditions remains a significant challenge due to the trade-off between robustness and efficiency. This study proposes a lightweight temporal convolutional network (TCN) and Mamba fusion architecture designed to enhance modulation recognition performance. In the modulation signal denoising stage, a non-local adaptive thresholding denoising module (NATM) is introduced to explicitly improve the effective signal-to-noise ratio. In the parallel feature extraction stage, TCN captures local symbol-level dependencies, while Mamba models long-range temporal relationships. In the output stage, their outputs are integrated through additive layer-wise fusion, which prevents parameter explosion. Experiments were conducted on the RadioML 2016.10A, 2016.10B, and 2018.01A datasets with leakage-controlled partitioning strategies including GroupKFold and Leave-One-SNR-Out cross-validation. The proposed method achieves up to a 3.8 dB gain in the required signal-to-noise ratio at 90 percent accuracy compared with state-of-the-art baselines, while maintaining a substantially lower parameter count and reduced inference latency. The denoising module provides clear robustness improvements under low signal-to-noise ratio conditions, particularly below −8 dB. The results show that the proposed network strikes a balance between accuracy and efficiency, highlighting its application potential in real-time wireless receivers under resource constraints. Full article
(This article belongs to the Special Issue AI-Driven Signal Processing in Communications)
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