6G Cell-Free Network Architecture

Zong, Jiaying; Liu, Yang; Liu, Haitao; Wang, Qingtian; Chen, Peng

doi:10.1109/icetci55101.2022.9832308

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Flexible segmentation of network functions is a key technology to realize high dynamic network slicing management, which will be built based on microservices 7 and cloud-native 8 . Network functions are decoupled into distributed and loosely coupled multiple microservices through modular design.…”

Section: High Dynamic End-to-end Network Slicing Management Technologymentioning

confidence: 99%

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

LIU,

SONG,

et al. 2023

Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manu

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Section: High Dynamic End-to-end Network Slicing Management Technologymentioning

confidence: 99%

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

LIU,

SONG,

et al. 2023

Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manu

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“…However, in traditional cellular networks, the flexible mobility of UAVs leads to frequent handovers and inter-cell interference issues [4]. The emergence of cell-free networks without cells and cell boundaries effectively reduces this problem and has been an important topic of future 6G research [5]. A cell-free network is a distributed multiple-input multiple-output (MIMO) system with a large number of access points (APs).…”

Section: Introductionmentioning

confidence: 99%

Joint AP Selection and Task Offloading Based on Deep Reinforcement Learning for Urban-Micro Cell-Free UAV Network

Pan,

Wang,

Yue

et al. 2023

Electronics

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The flexible mobility feature of unmanned aerial vehicles (UAVs) leads to frequent handovers and serious inter-cell interference problems in UAV-assisted cellular networks. Establishing a cell-free UAV (CF-UAV) network without cell boundaries effectively alleviates frequent handovers and interference problems and has been an important topic of 6G research. However, in existing CF-UAV networks, a large amount of backhaul data increases the computational pressure on the central processing unit (CPU), which also increases system delay. Meanwhile, the mobility of UAVs also leads to time-varying channel conditions. Therefore, designing dynamic resource allocation schemes with the help of edge computing can effectively alleviate this problem. Thus, aiming at partial network breakdown in an urban-micro (UMi) environment, an urban-micro CF-UAV (UMCF-UAV) network architecture is proposed in this paper. A delay minimization problem and a dynamic task offloading (DTO) strategy that jointly optimizes access point (AP) selection and task offloading is proposed to reduce system delay in this paper. Considering the coupling of various resources and the non-convex feature of the proposed problem, a dynamic resource cooperative allocation (DRCA) algorithm based on deep reinforcement learning (DRL) to flexibly deploy AP selection and task offloading of UAVs between the edge and locally is proposed to solve the problem. Simulation results show fast convergence behavior of the proposed algorithm compared with classical reinforcement learning. Decreased system delay is obtained by the proposed algorithm compared with other baseline resource allocation schemes, with the maximize improvement being 53%.

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“…In addition, the ultra-dense deployment of small cells or femtocells will lead to cell-less or cell-free architecture which will add higher interference with the Macro-Base Station (MBS) signals [6]. As a result, the CH hole will appear.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised learning-based coverage hole detection in cellular networks

Al-Ahmed¹,

Shakir²

2022

ITU J-FET

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For any time-critical mobile network-dependent applications and services, coverage is one of the prominent factors for providing the best Quality of Service (QoS) and Quality of Experience (QoE). A simple Coverage Hole (CH) may degrade the performance and the reputation of any operator by reducing the Key Performance Indicators (KPIs). This is one of the important aspects which need to be planned from the phase of network deployment throughout the whole operational stage. Many factors can cause CH such as attenuation, obstacles and improper network planning. Traditionally, a Drive Test (DT) used to be carried out in order to assess the quality of the mobile network signal. With technological advancement, DT has been replaced by the Minimization of Drive Test (MDT) and included as a part of Self-Organizing Networkss (SONs). The MDT process is applicable to networks that operate in 3G, 4G and 5G technologies. With this method, operators are able to measure network performance with the help of end users' devices. Thus, the network can be managed more conveniently, performance is improved, quality is increased, and maintenance costs are reduced for the network. However, the processing of MDT at the operators' side remains time-consuming and complex especially for CH analysis and detection from mobile network data. Therefore, we present a method by utilising Semi-Supervised Learning (SSL) in this paper so that this task becomes uncomplicated with improved accuracy. Our results show that the proposed method achieves better accuracy than the usual classification algorithm.

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6G Cell-Free Network Architecture

Cited by 12 publications

References 11 publications

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

Joint AP Selection and Task Offloading Based on Deep Reinforcement Learning for Urban-Micro Cell-Free UAV Network

Semi-supervised learning-based coverage hole detection in cellular networks

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