Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective

Fayad, Abdulhalim; Cinkler, Tibor; Rak, Jacek; Jha, Manish

doi:10.3390/s22239394

Cited by 27 publications

(6 citation statements)

References 41 publications

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“…Implement a phased approach to 6G deployment, focusing on specific geographic areas, use cases, or network functionalities. This approach minimizes high upfront costs [125].…”

Section: Investment Costmentioning

confidence: 99%

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Chataut,

Nankya,

Akl

2024

Sensors

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In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field.

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“…Implement a phased approach to 6G deployment, focusing on specific geographic areas, use cases, or network functionalities. This approach minimizes high upfront costs [125].…”

Section: Investment Costmentioning

confidence: 99%

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Chataut,

Nankya,

Akl

2024

Sensors

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“…FSO communication has been extensively studied in the literature [65][66][67][68][69][70][71][72][73][74]. Most of the earlier works were limited to terrestrial networks.…”

Section: Reliabilitymentioning

confidence: 99%

Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks

Elamassie,

Uysal

2023

Photonics

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The deployment of non-terrestrial networks (NTNs) is envisioned to achieve global coverage for 6G and beyond. In addition to space nodes, aerial NTN nodes such as high-altitude platform stations (HAPSs) and rotary-wing unmanned aerial vehicles (UAVs) could be deployed, based on the intended coverage and operational altitude requirements. NTN nodes have the potential to support both wireless access and backhauling. While the onboard base station provides wireless access for the end users, the backhauling link connects the airborne/space-borne base station to the core network. With its high data transmission capability comparable to fiber optics and its ability to operate in the interference-free optical spectrum, free space optical (FSO) communication is ideally suited to backhauling requirements in NTNs. In this paper, we present a comprehensive tutorial on airborne FSO backhauling. We first delve into the fundamentals of FSO signal transmission and discuss aspects such as geometrical loss, atmospheric attenuation, turbulence-induced fading, and pointing errors, all of which are critical for determining received signal levels and related link budget calculations. Then, we discuss the requirements of airborne backhaul system architectures, based on use cases. While single-layer backhaul systems are sufficient for providing coverage in rural areas, multi-layer designs are typically required to establish connectivity in urban areas, where line of sight (LoS) links are harder to maintain. We review physical layer design principles for FSO-based airborne links, discussing both intensity modulation/direct detection (IM/DD) and coherent modulation/coherent demodulation (CM/CD). Another critical design criteria for airborne backhauling is self-sustainability, which is further discussed in our paper. We conclude the paper by discussing current challenges and future research directions. In this context, we discuss reconfigurable intelligent surfaces (RIS) and spatial division multiplexing (SDM), for improved performance and an extended transmission range. We emphasize the importance of advanced handover techniques and scalability issues for practical implementation. We also highlight the growing role of artificial intelligence/machine learning (AI/ML) and their potential applications in the design and optimization of future FSO-based NTNs.

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“…For the atmospheric turbulence model, there are several other turbulence models. For example, Gamma-Gamma distribution, log-normal, negative exponential and Malaga [33][34][35]. Moreover, a multitude of analytical models have been proposed to simulate the C 2 n [36,37].…”

Section: Atmospheric Turbulence Theorymentioning

confidence: 99%

Atmospheric Turbulence Strength Estimation Using Convolution Neural Network

Gao,

Liu,

Chen

et al. 2023

IEEE Photonics J.

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Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective

Cited by 27 publications

References 41 publications

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks

Atmospheric Turbulence Strength Estimation Using Convolution Neural Network

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