Deep Reinforcement Learning Paradigm for Dense Wireless Networks in Smart Cities

2019 ◽  
pp. 43-70 ◽  
Author(s):  
Rashid Ali ◽  
Yousaf Bin Zikria ◽  
Byung-Seo Kim ◽  
Sung Won Kim
Keyword(s):  
Wireless Networks ◽  
Reinforcement Learning ◽  
Smart Cities ◽  
Learning Paradigm ◽  
Dense Wireless Networks

Solutions to Support Mobility in SDN-Based Dense Wireless Networks

2015 ◽  
Author(s):  
Ngoc-Dung Dao ◽  
Hang Zhang ◽  
Hamid Farmanbar ◽  
Xu Li
Keyword(s):  
Wireless Networks ◽  
Dense Wireless Networks

scholarly journals CHANNEL ALLOCATION FOR WIRELESS NETWORKS BASED ON INTELLIGENT METHOD

2021 ◽  
Vol 20 (4) ◽  
pp. 32-37
Author(s):  
Dominik NEZNÍK ◽  
◽  
Ľubomír DOBOŠ
Keyword(s):  
Wireless Networks ◽  
Data Transfer ◽  
Channel Allocation ◽  
Smart Cities ◽  
Data Rates ◽  
Different Types ◽  
Fuzzy Logic Method ◽  
Network Technologies ◽  
Near Future

In this paper, will be presented actual research of the intelligent channel allocation. The intelligent channel allocation is based on combination of fuzzy logic method and game theory attributes to increase quality of link in network. The channel allocations will become an important phenomenon in different types of networks such as 5G technology, wireless networks (IEEE 802.11xx), Z-Wave, LoRa, 3G, 4G, etc. In the near future, new network technologies, Internet of Things (IoT) and Smart Cities will need to have intelligent channel allocation to prevent interference on the channels used for data transfer. These networks along with IoT are considered as promising technology, that interconnects different types of networks into one fully functional network. The aim of this paper is to present the concept of a methods for channel allocation in wireless networks, where channels work as communication medium based on IEEE 802.11xx technology. The simulations prove, that proposed method is able to provide lower interference, improve data rates and increase quality of links.

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