Self-adapting protocol tuning for multi-hop wireless networks using Q-learning

2012 ◽  
Vol 23 (2) ◽  
pp. 119-136 ◽  
Author(s):  
Dan Marconett ◽  
Minsoo Lee ◽  
Xiaohui Ye ◽  
Rao Vemuri ◽  
S. J. B. Yoo
Keyword(s):  
Wireless Networks ◽  
Q Learning ◽  
Protocol Tuning

scholarly journals Deep Q-learning based resource allocation in industrial wireless networks for URLLC

2020 ◽  
Vol 14 (6) ◽  
pp. 1022-1027 ◽  
Author(s):  
Sanjay Bhardwaj ◽  
Rizki Rivai Ginanjar ◽  
Dong-Seong Kim
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Q Learning ◽  
Industrial Wireless Networks

Q-Learning Based Optimized Link State Routing Protocol for Different Mobility Pattern

2020 ◽  
Vol 17 (9) ◽  
pp. 4683-4687
Author(s):  
Yogesh Chaba ◽  
Mridul Chaba
Keyword(s):  
Wireless Networks ◽  
Routing Protocol ◽  
Learning Algorithm ◽  
Mobile Network ◽  
Delivery Ratio ◽  
Mobility Pattern ◽  
Mobility Patterns ◽  
Q Learning ◽  
Link State ◽  
Optimized Link State Routing

Now days wireless networks have become popular as the mobile applications are increasing day by day and mobility of nodes has become an important feature. The desirable property which separates mobile network from wireless networks is the mobility of communication devices. Therefore, there is a need to design routing mechanism in such a way that they can easily adopt to the frequent changes in the mobility pattern of the network. In this paper, Optimized Link State Routing protocol has been modified by implementing Q-Learning concept, a reinforcement learning algorithm which guides network to select next node to which it should forward packets by first calculating the reward R and then calculation of Q-value with neighbors. Performance of this modified routing protocol has been evaluated for parameters like delay, throughput and delivery ratio. Two mobility models have been used, Random Waypoint and Walk. It is observed that performance in terms of above parameters improve considerably in both mobility patterns when intelligent Q-Learning algorithm is implemented in Optimized Link State Routing.

A multi-state Q-learning based CSMA MAC protocol for wireless networks

2016 ◽  
Vol 24 (4) ◽  
pp. 1251-1264 ◽  
Author(s):  
Hossein Bayat-Yeganeh ◽  
Vahid Shah-Mansouri ◽  
Hamed Kebriaei
Keyword(s):  
Wireless Networks ◽  
Mac Protocol ◽  
Q Learning

scholarly journals Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Rashid Ali ◽  
Yazdan Ahmad Qadri ◽  
Yousaf Bin Zikria ◽  
Tariq Umer ◽  
Byung-Seo Kim ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Channel Access ◽  
Next Generation ◽  
Q Learning ◽  
Dense Wireless Networks
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