Towards 6G Communications: Architecture, Challenges, and Future Directions

Mitra, Purbita; Bhattacharjee, Rouprita; Chatterjee, Twinkle; De, Soumalya; Karmakar, Raja; Ghosh, Arindam; Adhikari, Tinku

doi:10.1109/icccnt51525.2021.9580084

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…The study effectively performed the convergence process in a single PON infrastructure. Conversely, Mitra et al described that the optical network deployment involved significant concerns regarding land acquisition and long deployment time [11].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

Hisyam Ng,

Mahmoodi

2024

Computers

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Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources.

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Section: Related Workmentioning

confidence: 99%

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

Hisyam Ng,

Mahmoodi

2024

Computers

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“…In this context, AI algorithms empower networks with the ability to autonomously detect, heal, and protect themselves, ensuring seamless and uninterrupted service delivery. This is why sixth generation (6G) cellular networks are expected to utilise a wide range of AI services from the core network to end users focused on the continuous optimisation of 6G networks [1]. This is achieved by following an AI-driven approach where intelligence will be an intrinsic feature of the 6G architecture [2].…”

Section: Introductionmentioning

confidence: 99%

NetLabeller: Architecture with data extraction and labelling framework for beyond 5G networks

Andrade-Hoz,

Alcaraz-Calero,

Wang

2024

J. Commun. Netw.

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The next generation of network capabilities coupled with artificial intelligence (AI) can provide innovative solutions for network control and self-optimisation. Network control demands a detailed knowledge of the network components to enforce the correct control rules. To this end, an immense number of metrics related to devices, flows, network rules, etc. can be used to describe the state of the network and to gain insights about which rule to enforce depending on the context. However, selection of the most relevant metrics often proves challenging and there is no readily available tool that can facilitate the dataset extraction and labelling for AI model training. This research work therefore first develops an analysis of the most relevant metrics in terms of network control to create a training dataset for future AI development purposes. It then presents a new architecture to allow the extraction of these metrics from a 5G network with a novel dataset visualisation and labelling tool to help perform the exploratory analysis and the labelling process of the resultant dataset. It is expected that the proposed architecture and its associated tools would significantly speed up the training process, which is crucial for the data-driven approach in developing AI-based network control capabilities.

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“…6G wireless networks will allow for the connectivity of a huge variety of users through the dense deployment of multi-antenna base stations (BSs) and access points (APs). Consequently, the energy-efficiency (EE) behavior of 6G is a crucial topic [3,4,5]. One of the potential solutions for green communication in 6G is the reconfigurable intelligent surface (RIS), a recently emerging hardware technology with increasing potential for large energy consumption reductions [3].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the energy-efficiency (EE) behavior of 6G is a crucial topic [3,4,5]. One of the potential solutions for green communication in 6G is the reconfigurable intelligent surface (RIS), a recently emerging hardware technology with increasing potential for large energy consumption reductions [3]. In its simple form, an RIS is a meta-surface made up of numerous inexpensive passive antennas that may effectively reflect the electromagnetic waves impinging on it on it in a controllable way to improve performances [5].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Resource Allocation for Re-configurable Intelligent Surface-Assisted Wireless Networks

Bidabadi

Ouameur

Bagaa

et al. 2023

Preprint

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This paper considers energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. In particular, it revisits the energy efficiency (EE) problem that uses the alternating optimization (AO) approach. In each AO's iteration, RIS phase optimization is achieved using gradient decent method whose convergence is unfortunately not guaranteed. We therefore, discuss the convergence issue herein while proposing two alternatives, namely, the Wolfe based gradient-descent algorithm (GAW) and the trust region (TR) method. However, the optimal transmit power allocation is obtained using Dinkelbach’s algorithm. Our results show that the proposed methods outperform the existing method that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method.

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Towards 6G Communications: Architecture, Challenges, and Future Directions

Cited by 11 publications

References 40 publications

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

NetLabeller: Architecture with data extraction and labelling framework for beyond 5G networks

Energy Efficient Resource Allocation for Re-configurable Intelligent Surface-Assisted Wireless Networks

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