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Advances in Deep Learning-Based Wireless Communication Systems
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Advances in Deep Learning-Based Wireless Communication Systems

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

Deadline for manuscript submissions: closed (15 August 2024) | Viewed by 4747

Special Issue Editors

Dr. Wenyu Zhang

E-Mail Website
Guest Editor
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100081, China
Interests: semantic communications; network AI; edge AI; AIaaS
Dr. Tianwei Hou

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: next generation multiple access; reconfigurable intelligent surface; integrated navigation and communication
Prof. Dr. Sherali Zeadally

grade E-Mail Website
Guest Editor
College of Communication and Information, University of Kentucky, Lexington, KY 40506-0224, USA
Interests: cybersecurity; privacy; Internet of Things; computer networks (including vehicular networks)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), especially deep learning (DL), is becoming a key enabler for solving a broad range of problems, such as network management and optimization, multiple access, coding, signal detection, and channel feedback, from the physical layer to the application layer in wireless communication systems. Emerging communication technologies, such as semantic communications, integrated sensing and communications (ISC), and reconfigurable intelligent surface (RIS), have brought new challenges and research opportunities for the design and optimization of the functional modules in wireless communications, and DL can play a vital role in these new scenarios. For example, semantic communication is a new end-to-end communication paradigm based on the powerful data-processing ability of DL, and it has shown exciting noise-resilience capability compared with conventional communication schemes. On the other hand, AI as a service (AIaaS) will be an essential functionality in future wireless networks to meet the growing demand for AI services for both the user side and the network side. In future years, we expect DL techniques will have a significant impact on the design and management of wireless communications systems, but DL for wireless communication is still in its infancy, and its advantages compared to conventional communication schemes still need to be explored.

Dr. Wenyu Zhang
Dr. Tianwei Hou
Prof. Dr. Sherali Zeadally
Guest Editors

Manuscript Submission Information

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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

  • DL4Net: DL-based transmission, processing, optimization, and management in wireless communication systems
  • Net4DL: Providing high-quality network services for DL applications
  • AIaaS: Integrating AI functionality in wireless communication systems
  • architecture and model design for DL-based semantic communications
  • DL for newly emerged scenarios, such as ISC, RIS, uRLLC, mmWave, and Terahertz. Edge AI for DL applications
  • privacy and security issues in DL-based wireless communication systems
  • engineering and implementation issues in DL-based wireless communication systems
  • performance evaluation of DL-based wireless communication systems
  • experimental testbeds for DL-based wireless research

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Published Papers (4 papers)

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11 pages, 18597 KiB  
Article
Demodulating Optical Wireless Communication of FBG Sensing with Turbulence-Caused Noise by Stacked Denoising Autoencoders and the Deep Belief Network
by Shegaw Demessie Bogale, Cheng-Kai Yao, Yibeltal Chanie Manie, Amare Mulatie Dehnaw, Minyechil Alehegn Tefera, Wei-Long Li, Zi-Gui Zhong and Peng-Chun Peng
Electronics 2024, 13(20), 4127; https://doi.org/10.3390/electronics13204127 - 20 Oct 2024
Viewed by 763
Abstract
Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO [...] Read more.
Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO path is a potential problem. This work aims to deep denoise sensed signals from fiber Bragg grating (FBG) sensors based on FSO link transmission using advanced denoising deep learning techniques, such as stacked denoising autoencoders (SDAE). Furthermore, it will demodulate the sensed wavelength of FBGs by applying the deep belief network (DBN) technique. This is the first time the real FBG sensing experiment has utilized the actual noise interference caused by the environmental turbulence from an FSO link rather than adding noise through numerical processing. Consequently, the spectrum of the FBG sensors is clearly modulated by the noise and the issue with peak power variation. This complicates the determination of the center wavelengths of multiple stacked FBG spectra, requiring the use of machine learning techniques to predict these wavelengths. The results indicate that SDAE is efficient in denoising from the FBG spectrum, and DBN is effective in demodulating the central wavelength of the overlapped FBG spectrum. Thus, it is beneficial to implement an FSO link-based FBG sensing system in adverse weather conditions or atmospheric turbulence. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Wireless Communication Systems)
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17 pages, 4291 KiB  
Article
Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
by Sümeye Nur Karahan and Aykut Kalaycıoğlu
Electronics 2024, 13(19), 3945; https://doi.org/10.3390/electronics13193945 - 7 Oct 2024
Viewed by 707
Abstract
In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for [...] Read more.
In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding in MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the Tikhonov regularization parameter, as well as the step size and search direction coefficients of the conjugate gradient (CG) method, as trainable parameters within the deep learning framework. This enables dynamic adjustments based on varying channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach across a range of MIMO configurations and channel conditions, consistently achieving lower bit error rate (BER) and normalized minimum mean square error (NMSE) compared to well-known techniques like DetNet and CG. The proposed method has superior performance over CG and other model-based methods, especially with a small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations, modulation orders, and different channel conditions. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Wireless Communication Systems)
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18 pages, 590 KiB  
Article
Multi-Agent-Deep-Reinforcement-Learning-Enabled Offloading Scheme for Energy Minimization in Vehicle-to-Everything Communication Systems
by Wenwen Duan, Xinmin Li, Yi Huang, Hui Cao and Xiaoqiang Zhang
Electronics 2024, 13(3), 663; https://doi.org/10.3390/electronics13030663 - 5 Feb 2024
Cited by 3 | Viewed by 1514
Abstract
Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which [...] Read more.
Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Wireless Communication Systems)
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16 pages, 656 KiB  
Article
Learning-Based Multi-Domain Anti-Jamming Communication with Unknown Information
by Yongcheng Li, Jinchi Wang and Zhenzhen Gao
Electronics 2023, 12(18), 3901; https://doi.org/10.3390/electronics12183901 - 15 Sep 2023
Cited by 3 | Viewed by 1010
Abstract
Due to the open nature of the wireless channel, wireless networks are vulnerable to jamming attacks. In this paper, we try to solve the anti-jamming problem caused by smart jammers, which can adaptively adjust the jamming channel and the jamming power. The interaction [...] Read more.
Due to the open nature of the wireless channel, wireless networks are vulnerable to jamming attacks. In this paper, we try to solve the anti-jamming problem caused by smart jammers, which can adaptively adjust the jamming channel and the jamming power. The interaction between the legitimate transmitter and the jammers is modeled as a non-zero-sum game. Considering that it is challenging for the transmitter and the jammers to acquire each other’s information, we propose two anti-jamming communication schemes based on the Deep Q-Network (DQN) algorithm and hierarchical learning (HL) algorithm to solve the non-zero-sum game. Specifically, the DQN-based scheme aims to solve the anti-jamming strategies in the frequency domain and the power domain directly, while the HL-based scheme tries to find the optimal mixed strategies for the Nash equilibrium. Simulation results are presented to validate the effectiveness of the proposed schemes. It is shown that the HL-based scheme has a better convergence performance and the DQN-based scheme has a higher converged utility of the transmitter. In the case of a single jammer, the DQN-based scheme achieves 80% of the transmitter’s utility of the no-jamming case, while the HL-based scheme achieves 63%. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Wireless Communication Systems)
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