A learning-based distributed algorithm for scheduling in multi-hop wireless networks

Park, Dae-Hyun; Kang, Sunjung; Joo, Changhee

doi:10.23919/jcn.2021.000030

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…Although there has been a continuous demand for automatic network management in the past, as described above, the complexity of the network to be managed is increasing exponentially as the network evolves; thus, a more scalable automatic network management technique is required. In addition, in order to effectively reflect the complex and variable network services, traffic, and user patterns, it is essential to automatically manage networks using data-based AI learning models rather than traditional statistical numerical models or rule-based techniques [47].…”

Section: Ai-native Networkingmentioning

confidence: 99%

“…In the former case, a method of applying the CR technology is being studied, which can strengthen the network's capacity and reduce the frequency license cost. In the latter case, a method to apply the new Q/V band (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50) to the satellite network is being studied, which can provide a wide bandwidth and thus can be applied to the operation of high throughput satellites (HTS). Currently, foreign satellite-related companies are waiting for FCC certification to use the band [66], [67].…”

Section: B Research Trends and Directionsmentioning

confidence: 99%

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6G R&D vision: Requirements and candidate technologies

et al. 2022

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The Korean Institute of Communications and Information Sciences (KICS), which is the largest information and communication technology institute in Korea, has been active in working towards development and standardization of mobile communication technology. In response to the need to meet upcoming 6G technical challenges in innovative applications such as hologram telepresence, extended reality (XR), digital twin, and connected robotics, the KICS 6G research initiative (KICS 6GRI) group has been created to establish a vision for key 6G technologies and identify research trends and directions. This article, therefore, covers major performance indicators and requirements envisioned by the KICS. In addition, we provide a comprehensive discussion of various candidate 6G technologies including (sub-)terahertz (THz), intelligent reflecting surface (IRS), artificial intelligence (AI)-based techniques, and non-terrestrial network (NTN).Index Terms-6G, the Korean Institute of Communications and Information Sciences (KICS), mobile communication standard. I. INTRODUCTIONF ROM the early stages of second-generation mobile communication development to the fifth-generation (5G), the

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Section: Ai-native Networkingmentioning

confidence: 99%

Section: B Research Trends and Directionsmentioning

confidence: 99%

6G R&D vision: Requirements and candidate technologies

et al. 2022

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“…For example, in [6], the authors aim to achieve high throughput performance while mitigating the interference in LEO satellite networks in such setting. They exploit Upper Confidence Bound (UCB) algorithms for frequency allocation, which are based on simple computation of indexes and known to achieve the optimal performance asymptotically [7]- [10]. There are also studies [11], [12] that address the time-varying interference problems in the satellite network using Deep Reinforcement Learning (DRL) techniques.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications

Yun

et al. 2023

IEEE Access

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We investigate effective interference management for Low Earth Orbit (LEO) satellite networks that provide downlink services to ground users and share the same frequency spectrum range. Since there are multi-group LEO satellites with different constellation orbits, the ground users will experience time-varying interference due to the overlapping of main/side lobes of the satellite beams, which becomes even challenging when the interfering satellites cannot communicate directly. To address the problem, we consider two LEO satellite groups that provide communication service in the same ground area, while competing for communication resources. We develop solutions that maximize the throughput and manage the time-varying interference under a certain level, without explicit message exchanges between the satellite groups. By exploiting statistical learning and deep reinforcement learning techniques, we develop learning-based resource allocation schemes and evaluate their performance through extensive simulations. We show their effectiveness under different reward settings and different interference managements, and demonstrate that a Deep Q-Network (DQN)-based scheme can achieve the close-to-optimal performance.

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“…We investigate the problem of efficient resource allocation of the control satellite without prior knowledge about the behavior of the interference satellite, while satisfying interference constraints. Different from the aforementioned works, we make use of UCB index, which has been used in cognitive radio networks [9], [10]. Further, we satisfy the interference constraint at the interference satellite without explicit information exchange as well as the interference constraint at the control satellite.…”

mentioning

confidence: 99%