Channel Bonding Design for 100 Gb/s PON Based on FEC Codeword Alignment

2017 ◽  
Author(s):  
Liang Zhang ◽  
Yuanqiu Luo ◽  
Bo Gao ◽  
Xiang Liu ◽  
Frank Effenberger ◽  
...  
Keyword(s):  
Channel Bonding ◽  
Bonding Design

scholarly journals A Markov model for performance evaluation of channel bonding in IEEE 802.11

2021 ◽  
Vol 115 ◽  
pp. 102449
Author(s):  
Marija Stojanova ◽  
Thomas Begin ◽  
Anthony Busson
Keyword(s):  
Performance Evaluation ◽  
Markov Model ◽  
Ieee 802.11 ◽  
Channel Bonding

Implementation of Terrestrial 8K Broadcast System Using ATSC 3.0 Channel Bonding

2020 ◽  
Author(s):  
Hoiyoon Jung ◽  
Sung-Ik Park ◽  
Jae-young Lee ◽  
Namho Hur ◽  
Jeongchang Kim ◽  
...  
Keyword(s):  
Channel Bonding

scholarly journals On-Demand Channel Bonding in Heterogeneous WLANs: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2789 ◽  
Author(s):  
Hang Qi ◽  
Hao Huang ◽  
Zhiqun Hu ◽  
Xiangming Wen ◽  
Zhaoming Lu
Keyword(s):  
Reinforcement Learning ◽  
Transmission Rate ◽  
Single Agent ◽  
Time Of Day ◽  
Action Space ◽  
Traffic Load ◽  
Traffic Demand ◽  
Channel Bonding ◽  
On Demand ◽  
Multi Agent

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

scholarly journals Carrier aggregation/channel bonding in next generation cellular networks: methods and challenges

IEEE Network ◽  
2014 ◽  
Vol 28 (6) ◽  
pp. 34-40 ◽  
Author(s):  
Zaheer Khan ◽  
Hamed Ahmadi ◽  
Ekram Hossain ◽  
Marceau Coupechoux ◽  
Luiz A Dasilva ◽  
...  
Keyword(s):  
Cellular Networks ◽  
Carrier Aggregation ◽  
Next Generation ◽  
Channel Bonding

scholarly journals JCTEA Evaluation Tests for Advanced Cable TV Transmission System with a Channel Bonding Technology

2015 ◽  
Vol 69 (12) ◽  
pp. J361-J363
Author(s):  
Takuya Kurakake ◽  
Yoshitaka Hakamada ◽  
Takaaki Konishi ◽  
Naoki Maeda ◽  
Hitoshi Hiraoka ◽  
...  
Keyword(s):  
Transmission System ◽  
Cable Tv ◽  
Channel Bonding ◽  
Bonding Technology
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