PROJECT TITLE :
ROOMMATEs: An Unsupervised Indoor Peer Discovery Approach for LTE D2D Communications - 2018
Recently, there was an increasing interest in offloading the 3GPP LTE knowledge by using device-to-device (D2D) communications between devices. However, the peer discovering is challenging, especially in the indoor surroundings, since ancient users use a cellular signal to seek out peers, leading to incurring interference to other cellular users. During this Project, we have a tendency to propose ROOMMATEs, a unique approach for indoor peer discovery method, that is the enabler for indoor D2D communications in future evolution (LTE) networks. It's a centralized approach utilizing, however not limited to, the ever-present WiFi network/femtocell network, combining with eNodeB so as to deliver the most effective results. ROOMMATEs is an unsupervised, yet energy efficient algorithm that can realize surrounding user equipments (UEs) whereas minimizing interference and consuming a lot of less energy. Primarily based on the results, ROOMMATEs proves to be highly energy efficient, saving on average twenty four% per UE and improving signal-to-interference-plus-noise ratio in order of tens of decibels compared to different approaches. Moreover, ROOMMATEs provides indoor place identification for UEs with high accuracy employing a small range of observations. ROOMMATEs is strong to missing and noisy data, works with numerous UEs' brands/models, and needs no user interactions.
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