Challenges and Future Trends of Wireless Networks

Abstract submission deadline

30 June 2026

Manuscript submission deadline

30 September 2026

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Topic Information

Dear Colleagues,

Since their invention, wireless communication networks have been one of the driving forces of innovation, acting as the skeleton and the nervous system upon which future technological applications are grounded.

The requirements of the upcoming technological revolutions range from determinism and the reliability of industrial networks to achieving extremely low power consumption for wireless sensor (and actuation) networks and personal networks. The presence, coexistence, and improvements of different communication technologies (e.g., Wi-Fi, 5G/6G/xG, LoRaWAN, IEEE 802.15.4, Bluetooth, to cite a few); the introduction of artificial intelligence (AI) and machine learning (ML) algorithms to optimize network behavior in real-time; and research on network digital twins and intelligent networks in general have combined to make wireless networks a promising and challenging research topic.

Current wireless networks represent an interdisciplinary topic due to their heterogeneity and the fact that research regarding modern wireless networks covers various disciplinary fields, including AI, ML, distributed systems, optimization, Internet of Things (IoT), cloud/fog/edge computing, security and safety aspects, communication protocols, standardization, management of smart cities, grids, health, buildings, transportation, homes, and agriculture.

This Topic is open to anyone who wishes to submit a relevant research manuscript about technological improvements in wireless networks and their application.

Dr. Stefano Scanzio
Dr. Ramez Daoud
Dr. Jetmir Haxhibeqiri
Dr. Pedro Santos
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	23.1 Days	CHF 1800	Submit
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Future Internet futureinternet	3.6	8.3	2009	16.1 Days	CHF 1800	Submit
IoT IoT	2.8	8.7	2020	25.5 Days	CHF 1400	Submit
Journal of Sensor and Actuator Networks jsan	4.2	9.4	2012	23.6 Days	CHF 2000	Submit
Network network	3.1	6.5	2021	23.9 Days	CHF 1200	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800	Submit

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Published Papers (3 papers)

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All Computers Electronics Future Internet IoT Network Sensors JSAN Technologies BDCC

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29 pages, 1900 KB  

Open AccessArticle

A Low-Complexity Hybrid Handover Strategy for LEO NTN: Balancing Stability and Link Quality

by Khalid Aldubaikhy

Sensors 2026, 26(5), 1449; https://doi.org/10.3390/s26051449 - 26 Feb 2026

Viewed by 706

Abstract

The deployment of low Earth orbit (LEO) satellite mega-constellations enables global broadband access, but their high orbital velocity demands frequent handover decisions that critically impact service continuity. Conventional strategies that maximize instantaneous signal quality often trigger excessive handovers, while stability-focused approaches may sacrifice [...] Read more.

The deployment of low Earth orbit (LEO) satellite mega-constellations enables global broadband access, but their high orbital velocity demands frequent handover decisions that critically impact service continuity. Conventional strategies that maximize instantaneous signal quality often trigger excessive handovers, while stability-focused approaches may sacrifice link performance. In this paper, we propose the Hybrid Handover Strategy (HHS), a low-complexity algorithm that addresses this trade-off. The HHS utilizes a multi-attribute utility function that integrates the signal-to-interference-plus-noise ratio (SINR), satellite elevation angle, and network load with a novel logistic-decay stability bonus mechanism. We provide a formal mathematical analysis of the algorithm’s stability and performance trade-offs. To ensure industrial relevance, the strategy is validated using a high-fidelity simulator driven by real-world two-line element (TLE) data from the Starlink constellation. Results demonstrate that the HHS reduces the handover frequency by 64% compared to SINR-based benchmarks while maintaining service availability of 90.2%. The proposed algorithm delivers these improvements with significantly smaller computational overhead than machine learning approaches, making it suitable for resource-constrained on-board processing and ground terminals. Full article

(This article belongs to the Topic Challenges and Future Trends of Wireless Networks)

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20 pages, 9630 KB  

Open AccessArticle

A Novel Intersection-Statistics-Based Indoor TOA Localization Algorithm with Adaptive Error Correction for NLOS Environments

by Zhaohui Wang, Chengchun Zhang, Peng Zhao, Liangkui Ding, Yanmei Lu, Longhua Shang, Mingyang Wei, Mingming Xie and Hongwei Li

Electronics 2026, 15(3), 639; https://doi.org/10.3390/electronics15030639 - 2 Feb 2026

Viewed by 552

Abstract

To address the performance degradation of existing base station-based indoor localization algorithms in non-line-of-sight (NLOS) environments, we propose a novel intersection-statistics-based localization method. The proposed algorithm introduces an adaptive error-correction mechanism that mitigates the aggregated effects of multipath interference and environmentally induced variations [...] Read more.

To address the performance degradation of existing base station-based indoor localization algorithms in non-line-of-sight (NLOS) environments, we propose a novel intersection-statistics-based localization method. The proposed algorithm introduces an adaptive error-correction mechanism that mitigates the aggregated effects of multipath interference and environmentally induced variations in TOA measurements. The core innovation lies in establishing a statistical framework that utilizes intersection density within minimum bounding circles to optimize correction parameters. Subsequent refinement employs standard deviation analysis to eliminate spatial outliers before final coordinate estimation. Comparative experimental results demonstrate significant improvements over conventional least squares (LS) and Nano algorithms across three key metrics: mean positioning error (reduced by 38.7%), maximum error (decreased by 42.1%), and error variance (improved by 57.3%). Empirical validation shows that the algorithm achieves 97.36% of absolute positioning errors within 1 m precision under optimized parameters, while maintaining 85.82% sub-meter accuracy using universal correction factors. These performance characteristics satisfy rigorous requirements for commercial indoor positioning systems while providing practical implementation advantages through adaptive parameter tuning. Full article

(This article belongs to the Topic Challenges and Future Trends of Wireless Networks)

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23 pages, 5201 KB  

Open AccessArticle

HiFiRadio: High-Fidelity Radio Map Reconstruction for 3D Real-World Scenes

by Ke Liao, Mengyu Ma, Luo Chen, Yifan Zhang and Ning Jing

Technologies 2026, 14(1), 58; https://doi.org/10.3390/technologies14010058 - 12 Jan 2026

Viewed by 679

Abstract

The reconstruction of high-fidelity radio maps is pivotal for wireless network planning but remains challenging due to the tension between physical accuracy and computational efficiency. We propose HiFiRadio, a novel framework that achieves a breakthrough in this balance by integrating centimeter-resolution 3D environmental [...] Read more.

The reconstruction of high-fidelity radio maps is pivotal for wireless network planning but remains challenging due to the tension between physical accuracy and computational efficiency. We propose HiFiRadio, a novel framework that achieves a breakthrough in this balance by integrating centimeter-resolution 3D environmental meshes with semantic-aware propagation modeling. At its core, HiFiRadio introduces a semantic-enhanced 3D indexing structure that efficiently manages complex terrain data, enabling real-time classification of signal paths into line-of-sight, non-line-of-sight, and vegetation-obstructed categories. This classification directly guides a hybrid propagation model, which dynamically applies dedicated loss calculations for buildings and foliage, grounded in physical principles. Extensive experiments demonstrate that HiFiRadio attains an accuracy comparable to commercial ray-tracing tools while being orders of magnitude faster. It also significantly outperforms existing learning-based baselines in both accuracy and scalability, a claim further validated by field measurements. By making high-fidelity, real-time radio map reconstruction practical for large-scale scenes, HiFiRadio establishes a new state of the art with immediate applications in network planning, UAV pathing, and dynamic spectrum access. Full article

(This article belongs to the Topic Challenges and Future Trends of Wireless Networks)

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