Chih‐Heng Ke
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View article: A Deep Reinforcement Learning-based bandwidth demand-oriented routing in Software-Defined Networking
A Deep Reinforcement Learning-based bandwidth demand-oriented routing in Software-Defined Networking Open
With the rise of bandwidth-intensive applications, such as video streaming and cloud services, efficient routing decision networks have become increasingly important. Bandwidth allocation issues arise from various causes. This paper examin…
View article: Adaptive Throughput Optimization in Multi-Rate IEEE 802.11 WLANs via Multi-Agent Deep Reinforcement Learning
Adaptive Throughput Optimization in Multi-Rate IEEE 802.11 WLANs via Multi-Agent Deep Reinforcement Learning Open
As wireless networks become increasingly important in modern society, their application scenarios are becoming more diverse and complex. However, the heterogeneity of nodes and transmission conditions presents significant challenges to exi…
View article: Differentiated QoS Provisioning in Wireless Networks Based on Deep Reinforcement Learning
Differentiated QoS Provisioning in Wireless Networks Based on Deep Reinforcement Learning Open
Wireless networks manage performance by adjusting the contention window, as they cannot directly detect collisions. Traditional contention window adjustment algorithms, such as the Binary Exponential Backoff (BEB) algorithm, may lead to lo…
View article: Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks
Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks Open
In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of …
View article: Deep reinforcement learning-based contention window optimization for IEEE 802.11 networks
Deep reinforcement learning-based contention window optimization for IEEE 802.11 networks Open
This study focuses on optimizing the contention window (CW) in IEEE 802.11 networks using deep reinforcement learning (DRL) to enhance the effectiveness of the contention mechanism. Recent research has employed a deep Q-learning network (D…
View article: A reinforcement learning approach for widest path routing in software-defined networks
A reinforcement learning approach for widest path routing in software-defined networks Open
In this paper, a routing method based on reinforcement learning (RL) under software-defined networks (SDN), namely the Q-learning widest-path routing algorithm (Q-WPRA), is proposed. This algorithm processes the reward function according t…
View article: Applying multi-agent deep reinforcement learning for contention window optimization to enhance wireless network performance
Applying multi-agent deep reinforcement learning for contention window optimization to enhance wireless network performance Open
This paper investigates the Contention Window (CW) optimization problem in multi-agent scenarios, where the fully cooperative among mobile stations is considered. A partially observable environment is employed to model and analyze the CW o…
View article: Applying Deep Reinforcement Learning to Improve Throughput and Reduce Collision Rate in IEEE 802.11 Networks
Applying Deep Reinforcement Learning to Improve Throughput and Reduce Collision Rate in IEEE 802.11 Networks Open
The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters.Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consist…
View article: Geographical forwarding algorithm based video content delivery scheme for internet of vehicles (IoV)
Geographical forwarding algorithm based video content delivery scheme for internet of vehicles (IoV) Open
This is an accepted manuscript of an article published by IEEE Multimedia Communications Technical Committee in MMTC Communications – Frontiers on 31/07/2020, available online: https://mmc.committees.comsoc.org/files/2020/07/MMTC_Communica…
View article: Smart routing: Towards proactive fault handling of software-defined networks
Smart routing: Towards proactive fault handling of software-defined networks Open
In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic in both the industrial and academic sectors. Software-defined networking offers numerous benefits against legacy networking systems b…
View article: Smart Routing: Towards Proactive Fault-Handling in Software-Defined Networks
Smart Routing: Towards Proactive Fault-Handling in Software-Defined Networks Open
Software-defined networking offers numerous benefits against the legacy networking systems through simplifying the process of network management and reducing the cost of network configuration. Currently, the management of failures in the d…
View article: THRIFTY: Towards High Reduction In Flow Table memorY
THRIFTY: Towards High Reduction In Flow Table memorY Open
The rapid evolution of information technology has compelled the ubiquitous systems and computing to adapt with this expeditious development. Because of its rigidity, computer networks failed to meet that evolvement for decades, however, th…
View article: Optimisation Methods for Fast Restoration of Software-Defined Networks
Optimisation Methods for Fast Restoration of Software-Defined Networks Open
The increasing complexity of modern day networked applications and the massive demand on the Internet resources has reignited interest and concern in the underlying networking infrastructures and their ability to cope with such complexity …
View article: A Hierarchical Packet Pre-Dropping Approach for Improved MPEG-4 Video Transmission over IEEE 802.11e Networks
A Hierarchical Packet Pre-Dropping Approach for Improved MPEG-4 Video Transmission over IEEE 802.11e Networks Open
This paper develops a hierarchical packet pre-dropping (HPPD) approach that exploits different methods to process I (intra-coded)/P (predictive-coded) /B (bidirectionally predictive-coded) video frame packets to improve the video transmiss…