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New Technologies in Wireless Communication System

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 658

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
Interests: wireless communications; adaptive machine learning; adaptive signal processing; error correcting codes; optimization algorithms
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, California State University, Fresno, CA 93740, USA
Interests: underwater communications; visible light communications; applied machine learning; wireless communications; wireless networking; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scope of this Special Issue encompasses a diverse array of cutting-edge technologies that are revolutionizing wireless communication systems. In the realm of the Internet of Things (IoT) and Machine-to-Machine (M2M) communications, innovations are focused on enhancing protocols, ensuring robust security, and optimizing energy efficiency in low-power wide-area networks (LPWAN). Millimeter Wave (mmWave) and Terahertz (THz) communications are pushing the boundaries of high-frequency communication, with advancements being made in antenna design, beamforming techniques, and channel modeling to support ultra-fast data rates and new applications. The integration of artificial intelligence (AI) and machine learning (ML) is transforming wireless networks, enabling intelligent resource management, predictive maintenance, and enhanced network optimization.

Additionally, significant progress is being made in vehicular and mobile communications, particularly with Vehicle-to-Everything (V2X) technologies that facilitate seamless connectivity and mobility management in dynamic environments. Satellite and UAV communications are bridging gaps in the coverage, offering innovative protocols for integration with terrestrial networks. Optical wireless communications and emerging fields like semantic and holographic communication are paving the way for high-capacity and immersive experiences. Quantum communication promises unprecedented levels of security and computational power, while Integrated Sensing and Communication (ISAC) technologies are creating multifunctional systems that synergize communication and sensing capabilities, marking a new era of wireless communication.

The Special Issue including, but not limited to, the following: 

  • Internet of Things (IoT) and Machine-to-Machine (M2M) Communications;
  • Millimeter Wave and Terahertz Communication;
  • Artificial Intelligence and Machine Learning for Wireless Networks;
  • Vehicular and Mobile Communications;
  • Satellite and UAV Communications;
  • Optical Wireless Communications;
  • Semantic communication;
  • Holographic communication;
  • Quantum communication;
  • Integrated (joint) sensing and communication (ISAC).

Dr. Michel Kulhandjian
Dr. Hovannes Kulhandjian
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. 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

  • wireless communications
  • wireless networks
  • Internet of Things

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Published Papers (1 paper)

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Research

17 pages, 1146 KB  
Article
Delay-Fluctuation-Resistant Underwater Acoustic Network Access Method Based on Deep Reinforcement Learning
by Jinli Shi, Kun Tian and Jun Zhang
Sensors 2025, 25(21), 6673; https://doi.org/10.3390/s25216673 - 1 Nov 2025
Viewed by 57
Abstract
The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve [...] Read more.
The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve network communication efficiency by effectively utilizing inter-node delay differences for concurrent communication. However, they still suffer from shortcomings such as not accounting for random delay fluctuations in underwater acoustic links and low learning efficiency. This paper proposes a DRL-based delay-fluctuation-resistant underwater acoustic network access method. First, delay fluctuations are integrated into the state model of deep reinforcement learning, enabling the model to adapt to delay fluctuations during learning. Then, a double deep Q-network (DDQN) is introduced, and its structure is optimized to enhance learning and decision-making in complex environments. Simulations demonstrate that the proposed method achieves an average improvement of 29.3% and 15.5% in convergence speed compared to the other two DRL-based methods under varying delay fluctuations. Furthermore, the proposed method significantly enhances the normalized throughput compared to conventional Time Division Multiple Access (TDMA) and DOTS protocols. Full article
(This article belongs to the Special Issue New Technologies in Wireless Communication System)
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