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Electronics
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Digital Signal Processing and Wireless Communication
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Digital Signal Processing and Wireless Communication

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 4986

Special Issue Editors

Dr. Jihyung Kim

E-Mail Website
Guest Editor
Spatial Wireless Transmission Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea
Interests: 5G/6G; UAV; RIS; AI
Prof. Dr. Byungju Lee

E-Mail Website
Guest Editor
Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Republic of Korea
Interests: 5G/6G; NTN; ISAC
Prof. Dr. Junghyun Kim

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
Interests: artificial intelligence; drug discovery; wireless communication; postquantum cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our Special Issue focuses on various aspects and innovations in modern communication technology, with a particular emphasis on Digital Signal Processing (DSP) and wireless communications. DSP plays a crucial role as a core technology in modern communication systems, contributing significantly to signal processing and data analysis. Wireless communication technology has revolutionized our lives by enabling fast data access and real-time information exchange from anywhere. In this Special Issue, we cover a wide range of wireless communication technologies, including advancements in 5G and the upcoming 6G networks, as well as discussions about the future of wireless communication technology.

Through this Special Issue, readers can gain insights into the latest trends and innovative technologies in modern communication. We aim to contribute to the advancement of wireless communication by sharing the opinions and research findings of experts in various fields. We hope that this issue will become a valuable resource for the development of wireless communication technology.

Dr. Jihyung Kim
Prof. Dr. Byungju Lee
Prof. Dr. Junghyun Kim
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • modulation/waveform
  • antenna techniques
  • coding
  • UAV and satellite communication
  • AI wireless communication
  • 5G/6G

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Published Papers (5 papers)

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Research

16 pages, 1589 KiB  
Article
A Two-Phase Deep Learning Approach to Link Quality Estimation for Multiple-Beam Transmission
by Mun-Suk Kim
Electronics 2024, 13(22), 4561; https://doi.org/10.3390/electronics13224561 - 20 Nov 2024
Viewed by 298
Abstract
In the multi-user multiple-input-multiple-output (MU-MIMO) beamforming (BF) training defined by the 802.11ay standard, since a single initiator transmits a significant number of action frames to multiple responders, inefficient configuration of the transmit antenna arrays when sending these action frames increases the signaling and [...] Read more.
In the multi-user multiple-input-multiple-output (MU-MIMO) beamforming (BF) training defined by the 802.11ay standard, since a single initiator transmits a significant number of action frames to multiple responders, inefficient configuration of the transmit antenna arrays when sending these action frames increases the signaling and latency overheads of MU-MIMO BF training. To configure appropriate transmit antenna arrays for transmitting action frames, the initiator needs to accurately estimate the signal to noise ratios (SNRs) measured at the responders for each configuration of the transmit antenna arrays. In this paper, we propose a two-phase deep learning approach to improve the accuracy of SNR estimation for multiple concurrent beams by reducing the measurement errors of the SNRs for individual single beams when each action frame is transmitted through multiple concurrent beams. Through simulations, we demonstrated that our proposed scheme enabled more responders to successfully receive action frames during MU-MIMO BF training compared to existing schemes. Full article
(This article belongs to the Special Issue Digital Signal Processing and Wireless Communication)
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24 pages, 2630 KiB  
Article
The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion
by Yunhao Liu, Sicun Han, Chengjun Guo, Jiangyan Chen and Qing Zhao
Electronics 2024, 13(20), 4045; https://doi.org/10.3390/electronics13204045 - 14 Oct 2024
Viewed by 787
Abstract
Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain [...] Read more.
Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments. Full article
(This article belongs to the Special Issue Digital Signal Processing and Wireless Communication)
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22 pages, 7148 KiB  
Article
A High Dynamic Velocity Locked Loop for the Carrier Tracking of a Wide-Band Hybrid Direct Sequence/Frequency Hopping Spread-Spectrum Signal
by Ju Wang, Yiying Liang, Xuanyu Xu, Jinyi Wang and Yi Zhong
Electronics 2024, 13(9), 1794; https://doi.org/10.3390/electronics13091794 - 6 May 2024
Viewed by 960
Abstract
For hybrid direct sequence/frequency hopping (DS/FH) spread spectrum signals, even if the relative motion speed between the transmitter and receiver remains constant, the Doppler frequency will vary due to the continuous hopping of the carrier frequency. Under high dynamic conditions, the first-order and [...] Read more.
For hybrid direct sequence/frequency hopping (DS/FH) spread spectrum signals, even if the relative motion speed between the transmitter and receiver remains constant, the Doppler frequency will vary due to the continuous hopping of the carrier frequency. Under high dynamic conditions, the first-order and second-order change rates of the Doppler frequency attached to the received signal further increase the Doppler frequency agility, making it difficult for the carrier tracking loop to maintain steady-state tracking. To address these issues, a high dynamic velocity locked loop (HD-VLL) is proposed in this paper. Specifically, the accumulated phase tracking error caused by acceleration and jerk is first analyzed. Subsequently, to compensate for this phase tracking error with the system clock, the proposed loop adds an acceleration compensation module and a jerk compensation module. However, this results in the output of the high dynamic loop filter being updated with the system clock, which contradicts the multiplexing design of a traditional loop filter for parallel signal processing, making the hardware implementation of an HD-VLL impractical. Therefore, this contradiction leads us to design an HD-VLL-based multi-carrier NCO (HD-VLL-NCO). The HD-VLL and HD-VLL-NCO are simulated, revealing the HD-VLL’s superior dynamic adaptability and steady-state tracking, while the HD-VLL-NCO achieves comparable accuracy with the appropriate truncation bit width. Full article
(This article belongs to the Special Issue Digital Signal Processing and Wireless Communication)
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19 pages, 1674 KiB  
Article
Equalizer Parameters’ Adjustment Based on an Oversampled Channel Model for OFDM Modulation Systems
by Marcin Kucharczyk, Grzegorz Dziwoki, Jacek Izydorczyk, Wojciech Sułek, Adam Dustor, Wojciech Filipowski, Weronika Izydorczyk, Piotr Kłosowski, Piotr Zawadzki, Piotr Sowa and Michał Rajzer
Electronics 2024, 13(5), 843; https://doi.org/10.3390/electronics13050843 - 22 Feb 2024
Viewed by 1011
Abstract
A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters’ estimation by a greedy OFDM (Orthogonal Frequency [...] Read more.
A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters’ estimation by a greedy OFDM (Orthogonal Frequency Division Multiplexing) equalizer. The equalizer works flawlessly if delays between propagation paths are in the sampling grid. Otherwise, the channel impulse response loses its compressible characteristic and the number of coefficients to find increases. It is possible to get back to the simple channel model by data oversampling. The paper describes how the above idea helps the OMP (Orthogonal Matching Pursuit) algorithm estimate channel coefficients. The authors analyze the oversampling algorithm on the one hand to assess the influence of filtering function and signal resolution on the quality of the channel impulse response reconstruction. On the other hand, the abilities of the OMP algorithm are analyzed to distinguish components of the oversampled signal. Based on these analyses, we proposed modifications to the compressible channel’s impulse response reconstruction algorithm to minimize the number of transmission errors. A distinction was made between the filters used in the OMP search and channel reconstruction stages before calculating equalizer coefficients. Additionally, the results of the search stage were considered as elements within the groups. Full article
(This article belongs to the Special Issue Digital Signal Processing and Wireless Communication)
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15 pages, 983 KiB  
Article
Augmenting Beam Alignment for mmWave Communication Systems via Channel Attention
by Jihyung Kim and Junghyun Kim
Electronics 2023, 12(20), 4318; https://doi.org/10.3390/electronics12204318 - 18 Oct 2023
Cited by 5 | Viewed by 1124
Abstract
The beamforming technique has attracted considerable attention in wireless communication due to its various advantages such as interference reduction and improved wireless resource efficiency. However, the beam alignment between transmitting and receiving devices, which is fundamentally required for the beamforming, poses a significant [...] Read more.
The beamforming technique has attracted considerable attention in wireless communication due to its various advantages such as interference reduction and improved wireless resource efficiency. However, the beam alignment between transmitting and receiving devices, which is fundamentally required for the beamforming, poses a significant challenge due to the continuous variability of the wireless channel. Recently, a deep learning-based technique has been proposed to predict narrow beam indices by measuring wide beams. However, there is room for improvement in the performance of the neural network architecture employed in this technique. Therefore, we suggest a novel deep learning model architecture that incorporates a channel attention module for beam training. The simulation results show a significant enhancement in performance with our scheme compared to both a state-of-the-art approach and other existing methods across all scenarios. Particularly, we confirm that even when reducing the number of wide beams used for measurement by approximately 50%, our proposed approach achieves a performance close to that of the state-of-the-art scheme. Full article
(This article belongs to the Special Issue Digital Signal Processing and Wireless Communication)
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