Supervised-Learning-Based Resource Allocation in Wireless Networks

Jang, Jonggyu; Park, Jung Hwa; Yang, Hyun Jong

doi:10.1109/ictc49870.2020.9289481

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The model can then be used to make predictions on new inputs. In NTN optimization, SL can be used for various tasks, such as channel estimation [70], interference mitigation [71], and resource allocation [72]. For example, a deep neural network (DNN) can be trained to predict the best channel and power allocation for a given set of users and resources.…”

Section: Ai Techniques For Ntns Optimizationmentioning

confidence: 99%

Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

Iqbal,

Tham,

Wong

et al. 2023

IEEE Access

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6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs.

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Section: Ai Techniques For Ntns Optimizationmentioning

confidence: 99%

Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

Iqbal,

Tham,

Wong

et al. 2023

IEEE Access

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“…Given this possible high amount of data and the increasing dif iculty of the several tasks performed in the network, ML techniques have been adopted in several works [7,8], especially Deep Neural Networks (DNNs). DNNs are optimized for an application, in general, with supervised learning approaches, which may require a prohibitive amount of data, depending on the model being trained.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous beam selection and users scheduling evaluation in a virtual world with reinforcement learning

Correa¹,

Oliveira²,

Du³

et al. 2022

ITU J-FET

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The fifth generation of mobile networks evolved to serve applications with distinct requirements, which results in a high management complexity due to simultaneous real-time tasks. In the physical layer, code words that allow proper data exchange between the Base Station (BS) and the served users must be chosen. While, in higher layers, the BS must choose users to be served in a given transmission opportunity. There are approaches based on Machine Learning (ML) to solve these combined tasks. However, due to the high amount of possible inputs, a challenge is the availability of data to train the models. In some cases, there may not even exist a predefined optimal answer to use as a "label" for supervised approaches. In this paper, we evaluate solutions for the combined problems of beam selection and user scheduling with Reinforcement Learning (RL), which does not need labels, as a solution for problems without a predefined answer. The algorithms were proposed for Problem Statement 6 of the challenge organized by the International Telecommunication Union (ITU) in 2021, which ranked as the finalists. We compare the approaches in relation to the cumulative reward received by the agents and show a performance comparison of different RL approaches by comparing them with baselines developed for the challenge. The paper also shows how the action taken by the trained agents affect network operation by comparing the number of packets transmitted, which is highly related to the proper selection of users and code words.

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