Paper Info
Title
Towards Coexistence of Cellular and WiFi Networks in Unlicensed Spectrum: A Neural Networks Based Approach
Abstract
Long-Term Evolution in the Unlicensed Spectrum (LIE-U) is considered as an indispensable technology to mitigate the spectrum scarcity in wireless networks. Typical LTE transmissions are contention-free and centrally controlled by the Base Station (BS). However, the wireless networks that work in unlicensed bands use contention-based protocols for channel access, which raise the need to derive an efficient and fair coexistence mechanism among different radio access networks. In this paper, we propose a novel mechanism based on neural networks for the coexistence of an LIE-U BS in the unlicensed spectrum alongside with WiFi access points. Specifically, we model the problem in coexistence as a 2-Dimensions Hopfield Neural Network (2D-HNN) based optimization problem that aims to achieve fairness considering both the LIE-U data rate and the QoS requirements of WiFi networks. Using the energy function of 2D-HNNs, precise investigation of its minimization property can directly provide the solution of the optimization problem. Furthermore, the problem of allocating the unlicensed resources to LIE-U users is modeled as a 2D-HNN and its energy function is leveraged to allocate resources to LIE-U users based on their channel states. Numerical results show that the proposed algorithm allows the LTE-U BS to work efficiently in the unlicensed spectrum while protecting the WiFi networks. Moreover, more than 90% fairness among the LIE-U users is achieved when allocating the unlicensed resources to LTE-U users based on the proposed algorithm.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2933323
IEEE ACCESS
Keywords
DocType
Volume
LTE-U,5G wireless networks,hopfield neural networks,WiFi,proportional fair,resource allocation
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Madyan Alsenwi1282.12
Ibrar Yaqoob269230.84
Shashi Raj Pandey3858.95
Yan Kyaw Tun4486.18
Anupam Kumar Bairagi5436.16
Lok-won Kim600.34
Choong Seon Hong72044277.88