ROOMMATEs: An Unsupervised Indoor Peer Discovery Approach for LTE D2D Communications

Recently, there has been an increasing interest in offloading the 3GPP LTE data by using device-to-device (D2D) communications between devices. However, the peer discovering is challenging, especially in the indoor environment, since traditional users use a cellular signal to find peers, leading to...

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Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 67; no. 6; pp. 5069 - 5083
Main Authors Nguyen, Nam Tuan, Choi, Kae Won, Song, Lingyang, Han, Zhu
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Cellular networks
Decibels
Device-to-device communication
Electronic devices
Energy conservation
Indoor environments
indoor places
Interference
Location-dependent and sensitive
Long Term Evolution
LTE D2D communications indoor places
mobile applications
Mobile communication systems
Nonparametric statistics
sequential probability ratio test
Wireless communication
Wireless communications
Wireless fidelity
Wireless sensor networks
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Summary:Recently, there has been an increasing interest in offloading the 3GPP LTE data by using device-to-device (D2D) communications between devices. However, the peer discovering is challenging, especially in the indoor environment, since traditional users use a cellular signal to find peers, leading to incurring interference to other cellular users. In this paper, we propose ROOMMATEs, a novel approach for indoor peer discovery process, which is the enabler for indoor D2D communications in long term evolution (LTE) networks. It is a centralized approach utilizing, but not limited to, the ubiquitous WiFi network/femtocell network, combining with eNodeB in order to deliver the best results. ROOMMATEs is an unsupervised, yet energy efficient algorithm that can find surrounding user equipments (UEs) while minimizing interference and consuming much less energy. Based on the results, ROOMMATEs proves to be highly energy efficient, saving on average 24% per UE and improving signal-to-interference-plus-noise ratio in order of tens of decibels compared to other approaches. Moreover, ROOMMATEs provides indoor place identification for UEs with high accuracy using a small number of observations. ROOMMATEs is robust to missing and noisy data, works with various UEs' brands/models, and requires no user interactions.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2018.2832223