使用DQN 深度學習演算法的O-RAN 無線電單元頻寬使用率之負載平衡機制
KP Chou - 2022 - ir.lib.ncu.edu.tw
摘要(中) 在現今工業物聯網(IIoT) 場域中, IIoT 設備之大幅增加, 導致網路流量需求逐年上升,
因而使其之控管變得極為不易. 有鑑於此, O-RAN 之網路架構的設計恰好可有效解決此問題 …
因而使其之控管變得極為不易. 有鑑於此, O-RAN 之網路架構的設計恰好可有效解決此問題 …
[PDF][PDF] 無線區域網路下透過深度強化學習提升吞吐量及降低碰撞率之研究
L Astuti - researchgate.net
摘要競爭視窗(Contention Window, CW) 參數的優化對於Wi-Fi 網路的傳輸效能有很大的影響.
無線網路所採用的傳統方法並沒有足夠的可擴展性讓過多的節點能夠維持一致的性能 …
無線網路所採用的傳統方法並沒有足夠的可擴展性讓過多的節點能夠維持一致的性能 …
Meta-Learning Traffic Pattern Adaptation for DRL-Based Radio Resource Management
YUJUI CHEN - 2021 - ir.lib.ncu.edu.tw
摘要 (中) 隨著第五代行動通訊技術的發展逐步成熟, 人們對於透過行動網路進行交流與溝通的
需求也持續飛快增長, 未來將出現許多對延遲和可靠度要求嚴格的新型應用服務, 例如: 以 360 …
需求也持續飛快增長, 未來將出現許多對延遲和可靠度要求嚴格的新型應用服務, 例如: 以 360 …
Research on computer wireless communication technology based on machine learning algorithm
J Li, X Ye - 2023 3rd International Conference on Electronic …, 2023 - ieeexplore.ieee.org
Different services in electric power communication network have different network
requirements. Traditional centralized network architecture is difficult to provide …
requirements. Traditional centralized network architecture is difficult to provide …
[HTML][HTML] Scalable Radio Resource Management using DDPG Meta Reinforcement Learning
YC Chen - 2020 - ir.lib.ncu.edu.tw
摘要 (中) 由於第五代通訊系統 (5G) 的發展, 增強了網路系統的能力及靈活度,
將允許更多極端且嚴峻的應用服務出現在第五代通訊系統上, 例如大型多人的虛擬實境線上遊戲 …
將允許更多極端且嚴峻的應用服務出現在第五代通訊系統上, 例如大型多人的虛擬實境線上遊戲 …
Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11 ah MAC Layer
The IEEE 802.11 ah standard is introduced to address the growing scale of internet of things
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
Special issue on deep reinforcement learning for emerging IoT systems
J Hu, P Liu, H Liu, O Anya… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Nowadays we are witnessing the formation of a massive Internet-of-Things (IoT) ecosystem
that integrates a variety of wireless-enabled devices ranging from smartphones, wearables …
that integrates a variety of wireless-enabled devices ranging from smartphones, wearables …
Sequential state q-learning uplink resource allocation in multi-ap 802.11 be network
Y Liu, Y Yu, Z Du, L Cuthbert - 2022 IEEE 96th Vehicular …, 2022 - ieeexplore.ieee.org
Expected high demand of user applications in the WLAN is a driver for WLANs to share
radio resources more efficiently. The move to 802.11 be with OFDMA and MU-MIMO makes …
radio resources more efficiently. The move to 802.11 be with OFDMA and MU-MIMO makes …
Survey on reinforcement learning applications in communication networks
Y Qian, J Wu, R Wang, F Zhu… - … of Communications and …, 2019 - ieeexplore.ieee.org
In recent years, intelligent communication has drawn huge research efforts in both academia
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …
[HTML][HTML] Wireless LAN performance enhancement using double deep Q-networks
K Asaf, B Khan, GY Kim - Applied Sciences, 2022 - mdpi.com
Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage
pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access …
pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access …