Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
New Technologies in Wireless Communication System
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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 2435

Special Issue Editors

Dr. Michel Kulhandjian

E-Mail Website
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
Prof. Dr. Hovannes Kulhandjian

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 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. 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

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

25 pages, 2327 KB  
Article
Joint Beamforming for Integrated Satellite–Terrestrial ISAC Systems
by Tengyu Wang and Qian Wang
Sensors 2026, 26(7), 2273; https://doi.org/10.3390/s26072273 - 7 Apr 2026
Viewed by 324
Abstract
Satellite–terrestrial integrated networks provide seamless global coverage, especially in remote areas where terrestrial deployment is costly. Integrated sensing and communications (ISAC) enhances spectral efficiency by merging both functions on a single platform. This paper proposes a novel integrated satellite–terrestrial ISAC architecture, where a [...] Read more.
Satellite–terrestrial integrated networks provide seamless global coverage, especially in remote areas where terrestrial deployment is costly. Integrated sensing and communications (ISAC) enhances spectral efficiency by merging both functions on a single platform. This paper proposes a novel integrated satellite–terrestrial ISAC architecture, where a satellite performs simultaneous communication and sensing. The satellite transmits communication signals and sensing waveforms to an Earth Station, which then relays them to a terrestrial base station to serve multiple users. We formulate a joint beamforming design problem to maximize the sum rate of users under quality-of-service constraints, backhaul capacity limits, beampattern requirements for sensing, and power budgets. With perfect channel state information, the non-convex problem is transformed into a difference-of-convex form and solved via the convex–concave procedure. For imperfect channel state information, a robust method combining successive convex approximation and the S-procedure is developed. Simulations show the proposed design outperforms benchmarks and is suitable for low-Earth orbit satellite systems. Full article
(This article belongs to the Special Issue New Technologies in Wireless Communication System)
►▼ Show Figures

Figure 1

28 pages, 2462 KB  
Article
Sparse Subsystem Discovery for Intelligent Sensor Networks
by Heli Sun, Xuechun Liu, Miaomiao Sun, Ruichen Cao, Bin Xing, Liang He and Hui He
Sensors 2026, 26(1), 288; https://doi.org/10.3390/s26010288 - 2 Jan 2026
Viewed by 356
Abstract
The Sparse Subgraph Finding (SGF) problem addresses the challenge of identifying sub–graphs with weak social interactions and sparse connections within a graph, which can be effectively modeled as discovering sparse subsystems in intelligent sensor networks. Traditional methods often rely on manually designed heuristics, [...] Read more.
The Sparse Subgraph Finding (SGF) problem addresses the challenge of identifying sub–graphs with weak social interactions and sparse connections within a graph, which can be effectively modeled as discovering sparse subsystems in intelligent sensor networks. Traditional methods often rely on manually designed heuristics, which are computationally expensive and lack scalability, especially when dealing with complex sensor network systems. In this paper, we propose RL-SGF, a novel framework that integrates deep reinforcement learning and graph embedding through joint optimization to overcome these limitations. By simultaneously optimizing subsystem sparsity and representation learning within a unified framework, RL-SGF enhances both the effectiveness and robustness of the model in sensor network applications. Experimental results on synthetic and real-world datasets, including social networks, citation networks, and sensor network simulations, demonstrate that RL-SGF outperforms existing algorithms in terms of efficiency and solution quality, making it highly applicable to real-world sparse subsystem discovery scenarios in intelligent sensor networks. Full article
(This article belongs to the Special Issue New Technologies in Wireless Communication System)
►▼ Show Figures

Figure 1

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 878
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)
►▼ 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