Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Alraih, Saddam; Shayea, Ibraheem; Behjati, Mehran; Nordin, Rosdiadee; Abdullah, Nor Fadzilah; Abu-Samah, Asma; Nandi, Dalia

doi:10.3390/s22030762

Cited by 70 publications

(28 citation statements)

References 98 publications

(118 reference statements)

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“…In addition, ML-based models can improve their accuracy over time without having to be specifically programmed [25]. Therefore, it coincides with the development trend of mobile network technology which is dynamic and rapidly evolving [26]- [29].…”

Section: Recent Progress In Ml-based Propagation Modellingmentioning

confidence: 77%

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

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Nowadays, the dependency on high-performance digital mobile connectivity is not limited to human usage but also the intelligent objects increasingly deployed to serve the needs of Internet of Things (IoT) applications. However, the current network planning technique limitation has constrained the real potential of mobile digital connectivity development. This situation has hindered sustainable Internet-oriented economic and technological development. The 3 rd generation partnership project (3GPP), through its specification release 18 (Rel.18), has included and leveraged the potential capabilities of machine learning (ML) technologies in advanced mobile network planning. The main objective is to enhance mobile network planning performance and reduce complexity. To materialize this aim, we propose a novel ML-based Online coverage Estimator (MLOE) tool developed based on Random Forest (RF) ML algorithm. It uses seven unique features to predict the mobile network performance through reference signal received power (RSRP). Accordingly, the results showed that MLOE outperformed traditional empirical techniques and previous works. The final trained RF algorithm has achieved an outstanding root mean square error (RMSE) of 2.65 dB and a coefficient of determination (𝑅𝑅 2 ) of 0.93. With the dynamic and fast-growing mobile technology, MLOE has been deployed on an online platform using MATLAB ® Web App Server, which offers a modular and scalable architecture.

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Section: Recent Progress In Ml-based Propagation Modellingmentioning

confidence: 77%

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

Self Cite

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“…With the compatibility of 6G mobile networks, the resource-constrained nature of UAVs will be the most difficult challenge. For stand-alone UAVs, supporting 1 Tbps throughput, AI, XR, and integrated sensing with communication characteristics is difficult [138]. Moreover, developing an on-board wireless module capable of transmitting and receiving mm-Wave for UAVs would be a hard process.…”

Section: Device Compatibilitymentioning

confidence: 99%

Swarm of UAVs for Network Management in 6G: A Technical Review

Khan

Kumar

Mohsan

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

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“…New Radio Lite (NR-Lite) of the 5G will be replaced by Intelligent Radio (IR) for intelligent communications [7]. More detailed comparisons between 5G and 6G were specifically provided in [8]. 6G is expected to be fully backed by the satellite.…”

Section: Introductionmentioning

confidence: 99%

A Novel Three-Factor Authentication Protocol for Multiple Service Providers in 6G-Aided Intelligent Healthcare Systems

Hsu

et al. 2022

IEEE Access

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6G technology is now attracting many scientific researchers due to its prominent features including high mobility, high data rate, high operating frequency, and ultra-low end-to-end delay. Compared to the 5G, 6G has certain advantages that can transform smart healthcare to a new advanced intelligent healthcare system, where multiple issues and concerns of the services (e.g., slow ambulance response) are effectively resolved. In such a communication system, patients and service providers communicate with each other via an open internet channel. Since the healthcare data is important and very sensitive, security and privacy in the healthcare network become prominent. In this paper, we introduce a 6G-aided intelligent healthcare environment. Our work also proposes a solution called Centerless User-Controlled Single Sign-On (CL-UCSSO) for achieving a convenient and cost-saving communication in the constructed multi-server system. A threefactor mechanism (combining smart card, password and biometrics) and time bound property are integrated to design the protocol with fast authentication that allows patients and providers to efficiently establish secure communications. Security proof of the proposed protocol is provided using well-known verification tools including RoR model, AVISPA simulation and BAN logic. Results of performance comparisons on various aspects show that our work provides more functionalities and incurs less cost compared with the related works.

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Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Cited by 70 publications

References 98 publications

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Swarm of UAVs for Network Management in 6G: A Technical Review

A Novel Three-Factor Authentication Protocol for Multiple Service Providers in 6G-Aided Intelligent Healthcare Systems

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