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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: closed (30 November 2025) | Viewed by 5675

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 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

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

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Related Special Issue

Published Papers (5 papers)

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Research

27 pages, 1112 KB  
Article
Joint Coherent/Non-Coherent Detection for Distributed Massive MIMO: Enabling Cooperation Under Mixed Channel State Information
by Supuni Gunasekara, Peter Smith, Margreta Kuijper and Rajitha Senanayake
Sensors 2025, 25(21), 6800; https://doi.org/10.3390/s25216800 - 6 Nov 2025
Viewed by 557
Abstract
Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) [...] Read more.
Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) due to fronthaul constraints, user mobility, or hardware limitation. In this paper, we propose two novel detectors that enable cooperation between BSs with differing CSI availability. In this setup, some BSs have access to instantaneous CSI, while others only have long-term channel information. The proposed detectors—termed the coherent/non-coherent (CNC) detector and the differential CNC detector—integrate coherent and non-coherent approaches to signal detection. This framework allows BSs with only long-term information to actively contribute to the detection process, while leveraging instantaneous CSI where available. This approach enables the system to integrate the advantages of non-coherent detection with the precision of coherent processing, improving overall performance without requiring full CSI at all cooperating BSs. We formulate the detectors based on the maximum likelihood (ML) criterion and derive analytical expressions for their pairwise block error probabilities under Rayleigh fading channels. Leveraging the pairwise block error probability expression for the CNC detector, we derive a tight upper bound on the average block error probability. Numerical results show that the CNC and differential CNC detectors outperform their respective single-BS baseline-coherent ML and non-coherent differential detection. Moreover, both detectors demonstrate strong resilience to mid-to-high range correlation at the BS antennas. Full article
(This article belongs to the Special Issue Future Wireless Communication Networks: 3rd Edition)
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21 pages, 630 KB  
Article
Scalability of Wi-Fi Performance in Virtual Reality Scenarios
by Vyacheslav Loginov, Sergei Tutelian, Ivan Startsev and Evgeny Khorov
Sensors 2025, 25(20), 6338; https://doi.org/10.3390/s25206338 - 14 Oct 2025
Viewed by 522
Abstract
The adoption of Virtual Reality (VR) applications in Wi-Fi networks intensifies each year. VR applications impose strict Quality of Service (QoS) requirements, necessitating low latency and high throughput. Meeting VR QoS requirements in Wi-Fi networks is especially challenging due to unpredictable channel fading [...] Read more.
The adoption of Virtual Reality (VR) applications in Wi-Fi networks intensifies each year. VR applications impose strict Quality of Service (QoS) requirements, necessitating low latency and high throughput. Meeting VR QoS requirements in Wi-Fi networks is especially challenging due to unpredictable channel fading and interference. This paper presents a comprehensive scalability study of Wi-Fi performance in multi-user VR scenarios. We investigate whether simply increasing Access Point (AP) capabilities, specifically through Multi-User MIMO (MU-MIMO), is sufficient to support dense VR deployments. To this end, we developed a high-fidelity simulation framework in ns-3 to estimate the network capacity when serving VR traffic. Our analysis meticulously evaluates the impact of critical factors, including the number of antennas at the AP and STAs, MU-MIMO scheduling algorithms, channel sounding period, and different channel conditions. The results reveal a critical finding: Scalability is not linear. In particular, doubling AP antennas from 8 to 16 yields only a 35% gain in capacity under typical conditions, not the 100% linear scaling one might expect. We identify and analyze the key bottlenecks that prevent performance from scaling indefinitely with an increased number of AP antennas, providing crucial insights for the design of next-generation Wi-Fi systems aimed at supporting the Metaverse and future immersive VR applications. Full article
(This article belongs to the Special Issue Future Wireless Communication Networks: 3rd Edition)
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17 pages, 369 KB  
Article
AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR
by Alejandro Villena-Rodríguez, Francisco J. Martín-Vega, Gerardo Gómez, Mari Carmen Aguayo-Torres, José Outes-Carnero, F. Yak Ng-Molina and Juan Ramiro-Moreno
Sensors 2025, 25(18), 5875; https://doi.org/10.3390/s25185875 - 19 Sep 2025
Viewed by 672
Abstract
The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM) to cope with the diverse operational conditions of the power amplifiers (PAs) in different user equipment (UEs). CP-OFDM [...] Read more.
The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM) to cope with the diverse operational conditions of the power amplifiers (PAs) in different user equipment (UEs). CP-OFDM leads to higher throughput when the PAs are operating in their linear region, which is mostly the case for cell-interior users, whereas DFT-S-OFDM is more appealing when PAs are exhibiting non-linear behavior, which is associated with cell-edge users. Therefore, existing waveform selection solutions rely on predefined signal-to-noise ratio (SNR) thresholds that are computed offline. However, the varying user and channel dynamics, as well as their interactions with power control, require an adaptable threshold selection mechanism. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL) that learns optimal switching thresholds for the current operational conditions. In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users’ service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. In addition, the solution accounts for the switching cost, which is related to the interruption of the communication after every switch due to implementation issues, which has not been considered in existing solutions. Results show that our proposed scheme achieves remarkable gains in terms of throughput for cell-edge users without degrading the average throughput. Full article
(This article belongs to the Special Issue Future Wireless Communication Networks: 3rd Edition)
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20 pages, 6437 KB  
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
Cited by 1 | Viewed by 1715
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 KB  
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
Cited by 1 | Viewed by 1469
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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