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Future Wireless Communication Networks: 3rd Edition
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Future Wireless Communication Networks: 3rd Edition

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

Deadline for manuscript submissions: 21 September 2025 | Viewed by 1054

Special Issue Editor

Dr. Hazer Inaltekin

E-Mail Website
Guest Editor
The School of Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
Interests: airborne networks; next generation broadband satellite systems; wireless edge computing; AI/ML based protocol design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the successor of 5G technology, 6G wireless networks will continue to revolutionize the industry and society by enabling many game-changing applications, including unmanned vehicle coordination and connectivity, smart factory operation, holographic telepresence, multisensory communications, telehealth, remote farming equipment operation, and remote IoT communications. Realizing the full potential of these applications will require order-of-magnitude improvements in data rates, latency, reliability, and coverage in 6G network deployments when compared to their 5G counterparts. Some key technologies that are expected to enable these applications include the utilization of terahertz bands; airborne network deployments; 3D networking integrating terrestrial, airborne, and satellite networks; next-generation satellite systems (e.g., regenerative LEO, MEO, and GEO satellites); and edge intelligence for joint communications, computing, and control.

This Special Issue focuses on potential technologies that will underpin the wireless networks beyond 5G and 6G. We solicit high-quality research papers on topics including, but not limited to, the following:

  • Terahertz communications;
  • Airborne network deployment and optimization;
  • 3D networking integrating terrestrial, airborne, and satellite networks;
  • AR/VR communications over wireless links;
  • New-generation satellite network architectures;
  • AI/ML-based protocol design for emerging wireless systems;
  • Quantum communications;
  • Wireless edge computing and control;
  • Airborne edge computing;
  • Satellite IoT systems;
  • Blockchain-based wireless networks.

Dr. Hazer Inaltekin
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

  • 5G and 6G
  • next-generation satellite systems
  • edge intelligence for joint communications, computing, and control

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 6437 KiB  
Article
Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks
by Hun Kim and Jaewoo So
Sensors 2025, 25(13), 4017; https://doi.org/10.3390/s25134017 - 27 Jun 2025
Viewed by 89
Abstract
In a multi-cell network, interference management between adjacent cells is a key factor that determines the performance of the entire cellular network. In particular, in order to control inter-cell interference while providing a high data rate to users, it is very important for [...] Read more.
In a multi-cell network, interference management between adjacent cells is a key factor that determines the performance of the entire cellular network. In particular, in order to control inter-cell interference while providing a high data rate to users, it is very important for the base station (BS) of each cell to appropriately control the transmit power in the downlink. However, as the number of cells increases, controlling the downlink transmit power at the BS becomes increasingly difficult. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based transmit power control scheme to maximize the sum rate in multi-cell networks. In particular, the proposed scheme incorporates a long short-term memory (LSTM) architecture into the MADRL scheme to retain state information across time slots and to use that information for subsequent action decisions, thereby improving the sum rate performance. In the proposed scheme, the agent of each BS uses only its local channel state information; consequently, it does not need to receive signal messages from adjacent agents. The simulation results show that the proposed scheme outperforms the existing MADRL scheme by reducing the amount of signal messages exchanged between links and improving the sum rate. Full article
(This article belongs to the Special Issue Future Wireless Communication Networks: 3rd Edition)
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21 pages, 1262 KiB  
Article
NeuroDetect: Deep Learning-Based Signal Detection in Phase-Modulated Systems with Low-Resolution Quantization
by Chanula Luckshan, Samiru Gayan, Hazer Inaltekin, Ruhui Zhang and David Akman
Sensors 2025, 25(10), 3192; https://doi.org/10.3390/s25103192 - 19 May 2025
Viewed by 659
Abstract
This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes [...] Read more.
This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case 12% margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems. Full article
(This article belongs to the Special Issue Future Wireless Communication Networks: 3rd Edition)
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