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Titolo: Group-Oriented Broadcasting of Augmented Reality Services over 5G New Radio
Autori: 
Data di pubblicazione: 2019
Abstract: Augmented Reality applications represent the current emerging trend of broadcast services whereby users require the same data content. Broadcast applications in smart environments ask for low-latency data transmission, low-energy communication, and location- and customer-based group creation procedures. The standardization efforts done from the 3rd Generation Partnership Project (3GPP) to make the 5th generation (5G) a reality have involved also the existing Long Term Evolution (LTE) radio access technology leading to the 5G New Radio (5G-NR) standard. One of the main innovation is the definition of three different Modulation and Coding Scheme (MCS) tables to allow a differentiation according to the class of devices, grouped into five different categories based on their basic characteristics. 5G broadcast/multicast is one of the topics that is under discussion at 3GPP for 5G phase II (release 17). In this paper, authors focused on a 5G-ready LTE system, referring to real broadcast mobile urban scenarios where users are interested in Augmented Reality (AR) applications. The authors analyzed the Subgrouping Optimal Aggregate Data Rate (SubOptADR) algorithm whereby broadcast destinations are grouped into different subgroups depending on the perceived user equipment (UE) channel quality in order to maximize the Aggregate Data Rate (ADR), which is the sum of data rate values obtained by all the broadcast members. The capabilities of the algorithm are evaluated comparing LTE and 5G-NR networks, focusing on different user classes and type of devices. The paper details three envisaged AR application scenarios, describing the subgrouping optimization algorithm in 5G-NR and showing how group-oriented communications can improve spectrum efficiency in the broadcast of AR services.
Handle: http://hdl.handle.net/11584/284736
ISBN: 978-1-7281-2784-2
Tipologia:4.1 Contributo in Atti di convegno

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