APPROACHES TO THE JOINT SCHEDULING OF URLLC AND EMBB TRAFFIC TRANSMISSION IN WIRELESS 5G NETWORKS

Fifth generation networks are currently being deployed around the world. 5G technology is designed to solve such problems as the growth of mobile traffic, the increase in the number of devices connected to the network, the reduction of delays in the introduction of new services and the lack of frequency spectrum. For these purposes, the following main classes of services that are provided in 5G networks are distinguished: enhanced mobile broadband (eMBB), machinetype mass communication (mMTC) and ultra-reliable low latency communication (URLLC). However, the main problem for researchers is how to plan the joint data transmission of different services, for example, eMBB and URLLC. This challenge poses so many questions due to the critically different stringent requirements of each of the services. So, URLLCs require ultra-reliable communication with minimal delays, which are ~ 1 ms. And eMBBs require very high speed for large amounts of data. This topic is quite new and researchers still cannot come to a common solution. Some involve slicing the cellular network, others use multiple access technologies. In this paper, we will give a detailed overview of the proposed approaches, identify problems in knowledge in the issue of providing joint service for devices of two types of traffic, eMBB and URLLC. To study this issue and present the results, the CARS model and the PRISMA approach are used. As a result, the most influential publications on this topic were identified, the limitations of research were identified, and directions for further tasks were also noted.

Authors
Publisher
Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Московский государственный университет пищевых производств"
Language
English
Pages
410-420
Status
Published
Year
2021
Organizations
  • 1 Peoples' Friendship University of Russia (RUDN University)
Keywords
5g; artificial intelligence; machine learning methods; priority service
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