Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Intelligent Sensors and Technology for Optical Wireless Communications Systems
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Sensors and Technology for Optical Wireless Communications Systems

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 3340

Special Issue Editor

Dr. Wen Zhou

E-Mail Website
Guest Editor
School of Information Science and Technology, Fudan University, Shanghai 200433, China
Interests: fiber-optic and wireless communications; associated digital signal processing

Special Issue Information

Dear Colleagues,

Today, 5G is defined as millimeter-wave (MMW) bands below 100 GHz, whereas 100 GHz-3 THz is categorized as a THz band in 6G. Photonics-aided MMW generation is a key technique used in fiber-wireless networks, which overcomes the bottleneck of the deployed electrical devices. The major constraint factors of the photonics-sided high-frequency MMW transmission system are a large atmosphere attenuation loss and nonlinearity that results from optoelectrical devices. Although optical wireless communication (OWC) has appealing potential, it introduces many novel signal processing challenges such as physical-layer transmission, joint sensing and communication, security, channel modeling, coding, detection and so on. Especially in 6G, the most effective approaches to modeling the nonlinear wireless behavior are those based on AI techniques. To tackle the challenges, this Special Issue seeks to introduce the latest advances in intelligent techniques of OWC. Topics of interest include, but are not limited to:

  • Signal processing algorithms for OWC.
  • Intelligent sensors for OWC.
  • OWC propagation, channel modeling and measurement.
  • OWC channel estimation, coding and detection strategies.
  • OWC physical-layer transmission beyond 100 GHz.
  • Joint optical millimeter/THz sensing and communication.
  • Theoretical performance analysis of OWC systems.
  • Pure data-driven and model-driven AI techniques for 6G OWC.

Dr. Wen Zhou
Guest Editor

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. Sensors 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 2600 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

  • optical wireless communication (OWC)
  • signal processing algorithms
  • AI techniques
  • 6G physical-layer transmission
  • joint sensing and communication
  • channel modeling, coding and detection

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 10011 KiB  
Article
Deep Learning Equalizer Connected with Viterbi-Viterbi Algorithm for PAM D-Band Radio over Fiber Link
by Tangyao Xie, Qiang Sheng and Jianguo Yu
Sensors 2023, 23(24), 9773; https://doi.org/10.3390/s23249773 - 12 Dec 2023
Viewed by 654
Abstract
D-band (110–170 GHz) has been regarded as a potential candidate for the future 6G wireless network because of its large available bandwidth. At present, the lack of electrical amplifiers operating in the high frequency band and the strong nonlinear effect, i.e., the D-band, [...] Read more.
D-band (110–170 GHz) has been regarded as a potential candidate for the future 6G wireless network because of its large available bandwidth. At present, the lack of electrical amplifiers operating in the high frequency band and the strong nonlinear effect, i.e., the D-band, are still important problems. Therefore, effective methods to mitigate the nonlinear issue resulting from the ROF link are indispensable, among of which machine learning is considered the most effective paradigm to model the nonlinear behavior due to its nonlinear active function and structure. In order to reduce the computation amount and burden, a novel deep learning neural network equalizer connected with typical mathematical frequency offset estimation (FOE) and carrier phase recovery (CPR) algorithms is proposed. We implement D-band 45 Gbaud PAM-4 and 20 Gbaud PAM-8 ROF transmission simulations, and the simulation results show that the real value neural network (RVNN) equalizer connected with the Viterbi-Viterbi algorithm exhibits better compensation ability for nonlinear impairment, especially when dealing with serious inter-symbol interference and nonlinear effects. In our experiment, we employ coherent detection to further improve the receiver sensitivity, so a complex baseband signal after down conversion at the receiver is inherently produced. In this scenario, the complex value neural network (CVNN) and RVNN equalizer connected with the Viterbi-Viterbi algorithm have better BER performance with an error rate lower than the HD-FEC threshold of 3.8 × 10−3. Full article
(This article belongs to the Special Issue Intelligent Sensors and Technology for Optical Wireless Communications Systems)
►▼ Show Figures

Figure 1

15 pages, 13154 KiB  
Article
Two-Lane DNN Equalizer Using Balanced Random-Oversampling for W-Band PS-16QAM RoF Delivery over 4.6 km
by Sicong Xu, Bohan Sang, Lingchuan Zeng and Li Zhao
Sensors 2023, 23(10), 4618; https://doi.org/10.3390/s23104618 - 10 May 2023
Cited by 2 | Viewed by 1153
Abstract
For W-band long-range mm-wave wireless transmission systems, nonlinearity issues resulting from photoelectric devices, optical fibers, and wireless power amplifiers can be handled by deep learning equalization algorithms. In addition, the PS technique is considered an effective measure to further increase the capacity of [...] Read more.
For W-band long-range mm-wave wireless transmission systems, nonlinearity issues resulting from photoelectric devices, optical fibers, and wireless power amplifiers can be handled by deep learning equalization algorithms. In addition, the PS technique is considered an effective measure to further increase the capacity of the modulation-constraint channel. However, since the probabilistic distribution of m-QAM varies with the amplitude, there have been difficulties in learning valuable information from the minority class. This limits the benefit of nonlinear equalization. To overcome the imbalanced machine learning problem, we propose a novel two-lane DNN (TLD) equalizer using the random oversampling (ROS) technique in this paper. The combination of PS at the transmitter and ROS at the receiver improved the overall performance of the W-band wireless transmission system, and our 4.6-km ROF delivery experiment verified its effectiveness for the W-band mm-wave PS-16QAM system. Based on our proposed equalization scheme, we achieved single-channel 10-Gbaud W-band PS-16QAM wireless transmission over a 100 m optical fiber link and a 4.6 km wireless air-free distance. The results show that compared with the typical TLD without ROS, the TLD-ROS can improve the receiver‘s sensitivity by 1 dB. Furthermore, a reduction of 45.6% in complexity was achieved, and we were able to reduce training samples by 15.5%. Considering the actual wireless physical layer and its requirements, there is much to be gained from the joint use of deep learning and balanced data pre-processing techniques. Full article
(This article belongs to the Special Issue Intelligent Sensors and Technology for Optical Wireless Communications Systems)
►▼ Show Figures

Figure 1

10 pages, 4625 KiB  
Communication
4Gbaud PS-16QAM D-Band Fiber-Wireless Transmission over 4.6 km by Using Balance Complex-Valued NN Equalizer with Random Oversampling
by Tangyao Xie and Jianguo Yu
Sensors 2023, 23(7), 3655; https://doi.org/10.3390/s23073655 - 31 Mar 2023
Cited by 2 | Viewed by 1066
Abstract
D-band (110–170 GHz) is a promising direction for the future of 6th generation mobile networks (6G) for high-speed mobile communication since it has a large available bandwidth, and it can provide a peak rate of hundreds of Gbit/s. Compared with the traditional electrical [...] Read more.
D-band (110–170 GHz) is a promising direction for the future of 6th generation mobile networks (6G) for high-speed mobile communication since it has a large available bandwidth, and it can provide a peak rate of hundreds of Gbit/s. Compared with the traditional electrical approach, photonics millimeter wave (mm-wave) generation in D-band is more practical and effectively overcomes the bottleneck of electrical devices. However, long-distance D-band wireless transmission is still limited by some key factors such as large absorption loss and nonlinear noises. Deep neural network algorithms are regarded as an important technique to model the nonlinear wireless behavior, among which the study on complex-value equalization is critical, especially in coherent detection systems. Moreover, probabilistic shaping is useful to improve the transmission capacity but also causes an imbalanced machine learning issue. In this paper, we propose a novel complex-valued neural network equalizer coupled with balanced random oversampling (ROS). Thanks to the adaptive deep learning method for probabilistic shaping-quadrature amplitude modulation (PS-QAM), we successfully realize a 135 GHz 4Gbaud PS-16QAM with a shaping entropy of 3.56 bit/symbol wireless transmission over 4.6 km. The bit error ratio (BER) of 4Gbaud PS-16QAM can be decreased to a soft-decision forward error correction (SD-FEC) with a 25% overhead of 2 × 10−2. Therefore, we can achieve a net rate of an 11.4 Gbit/s D-band radio-over-fiber (ROF) delivery over 4.6 km air free wireless distance. Full article
(This article belongs to the Special Issue Intelligent Sensors and Technology for Optical Wireless Communications Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop