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

Abstract

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.