MADRL Based Uplink Joint Resource Block Allocation and Power Control in Multi-Cell Systems

Yang, Yunjuan; Lv, Tiejun; Cui, Yingping; Huang, Pingmu

doi:10.1109/wcnc55385.2023.10119106

2023 IEEE Wireless Communications and Networking Conference (WCNC) 2023

DOI: 10.1109/wcnc55385.2023.10119106

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MADRL Based Uplink Joint Resource Block Allocation and Power Control in Multi-Cell Systems

Yunjuan Yang

Tiejun Lv

Yingping Cui

et al.

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2024

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Cited by 2 publications

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Power Control of 5G-Connected Vehicular Network Using PPO-Based Deep Reinforcement Learning Algorithm

Raeisi,

Sesay

2024

IEEE Access

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Power Control of 5G-Connected Vehicular Network Using PPO-Based Deep Reinforcement Learning Algorithm

Raeisi,

Sesay

2024

IEEE Access

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Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

Wang,

Guo,

Zhu

et al. 2024

Electronics

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In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

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MADRL Based Uplink Joint Resource Block Allocation and Power Control in Multi-Cell Systems

Cited by 2 publications

References 12 publications

Power Control of 5G-Connected Vehicular Network Using PPO-Based Deep Reinforcement Learning Algorithm

Power Control of 5G-Connected Vehicular Network Using PPO-Based Deep Reinforcement Learning Algorithm

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

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