Paper Info
Title
A Recurrent Neural Network MAC Protocol Towards to Opportunistic Communication in Wireless Networks
Abstract
One of the major challenges in opportunistic networks is the correct identification of transmission opportunities. In this work, a new cognitive MAC protocol, called Makiuchi is proposed. The Makiuchi is builtin using a recurrent neural network to model the channel occupation and to detect the exact moment of transmission opportunity. The training process was performed based on data from a real world experiment using Software Defined Radio (SDR) for monitoring a Wi-Fi channel. The Makiuchi MAC protocol was implemented using the discrete event simulator OMNeT++ and INET networking framework. Preliminary simulation results demonstrate the expected behavior of the Makiuchi protocol in the process of identification and allocation of Secondary Users' transmission opportunities. When compared with IEEE 802.11, the Makiuchi algorithm is able to improve in 22% the network performance, embracing a bigger number of transmissions opportunities, while it reduces in ≈50% the number of collisions with the Primary User.
Year
DOI
Venue
2019
10.1109/ISWCS.2019.8877272
2019 16th International Symposium on Wireless Communication Systems (ISWCS)
Keywords
Field
DocType
recurrent neural networks,opportunistic transmission,wireless communication,cognitive MAC protocol
Wireless network,Computer science,Software-defined radio,Computer network,Communication channel,Recurrent neural network,Inet,Transmission opportunity,Hidden Markov model,Network performance
Conference
ISSN
ISBN
Citations 
2154-0217
978-1-7281-2528-2
1
PageRank 
References 
Authors
0.37
10
6
Name
Order
Citations
PageRank
Marcos F. Caetano1156.49
Mariana R. Makiuchi210.37
Silas S. Fernandes310.71
Marcus Vinicius Lamar422.07
Jacir Luiz Bordim55718.86
Priscila Solís Barreto610.71