Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Wilhelmi, Francesc; Carrascosa, Marc; Cano, Cristina; Jönsson, Anders; Ram, Vishnu; Bellalta, Boris

doi:10.1109/mwc.001.2000206

Cited by 25 publications

(21 citation statements)

References 11 publications

(13 reference statements)

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“…Moreover, the simulator was already used in real-world testbeds [ 7 ]. In this case, Komondor characterized the WLAN testbed and generated a simulated network twin.…”

Section: Data Setmentioning

confidence: 99%

“…The role of network simulators in future 5G/6G networks has been pointed out as a key tool to assist the increasingly adopted ML operation in communications [ 7 ]. The fact is that network simulators can represent unknown situations that may not be present in real traces (due to the limitations in acquiring data from real networks), thus allowing to support procedures, such as training, testing, and validation of ML models.…”

Section: Data Setmentioning

confidence: 99%

“…(There is a trade-off between the simulation time and the stability of the simulated results. In particular, 10 seconds was shown to properly address this trade-off by providing accurate results at a low execution time in Komondor [ 7 ].).…”

Section: Data Setmentioning

confidence: 99%

“…Data extracted from real deployments are often limited by privacy issues (e.g., proprietary data from a network operator) or sparsity (e.g., deployments with a couple of transmitters and receivers). On the contrary, synthetic data can be easily generated where multiple deployments with a high number of transmitting devices and different configurations can be evaluated through simulations [ 7 ]. These synthetic data can train accurate ML models that adapt well to real and changing situations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

Soto

Camelo

Mets

et al. 2021

Sensors

Self Cite

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IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features.

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“…Moreover, the simulator was already used in real-world testbeds [ 7 ]. In this case, Komondor characterized the WLAN testbed and generated a simulated network twin.…”

Section: Data Setmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

Soto

Camelo

Mets

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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“…In the sandbox, MNOs can compare the performance of various ML models and mitigate the risks associated with the adoption of ML techniques. Training ML models in the sandbox may use either real-network or simulation data, leveraging digital twins of the underlay network [92]. Therefore, the sandbox enables the ML pipeline to dynamically adapt to changes in the underlay network or the environment.…”

Section: • Service Design •mentioning

confidence: 99%

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Koufos

Haloui

Dianati

et al. 2021

JSAN

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The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.

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Challenges in meeting QoS requirements toward 6G wireless networks: A state of the art survey

Ahmed

Mahmood

Matin

2022

Trans Emerging Tel Tech

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The 6G wireless network with its superior capabilities must be considered to accommodate the much more stringent quality of service (QoS) requirements of the numerous emerging applications and services in the future. Attempting to adhere these constraints raise various challenges that are required to be addressed and solved in order to realize the vision of a fully intelligent and automated future society. This article provides the key enabling technologies and future applications for 6G networks followed by a discussion on QoS. It also presents a state-of-the-art survey on the challenges faced while trying to meet the QoS requirements regarding the time latency, throughput, connectivity, packet loss, and bandwidth, together with possible solutions to handle various constraints. A discussion on user satisfaction estimation from QoS analysis is also included. Finally, the article concludes with future research opportunities in meeting the QoS constraints of the 6G network.

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Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Cited by 25 publications

References 11 publications

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Challenges in meeting QoS requirements toward 6G wireless networks: A state of the art survey

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