J. Kim, G. Lee, S. Kim, T. Taleb, S. Choi and S. Bahk, "Two-Step Random Access for 5G System: Latest Trends and Challenges," in IEEE Network, vol. 35, no. 1, pp. 273-279, January/February 2021, doi: 10.1109/MNET.011.2000317
Two-step random access for 5G system : latest trends and challenges
|Author:||Kim, Junseok1; Lee, Goodsol2; Kim, Seongwon3;|
1System LSI, Samsung Electronics, Gyeonggi-do, Korea
2Department of ECE and INMC, Seoul National University, Seoul, Korea
3Vision AI Labs in SK Telecom, Seoul, Korea
4Department of CCommunications and Networking, School of Electrical Engineering, Aalto University, Finland
5Faculty of Information Technology and Electrical Engineering, Oulu University
6Department of Computer and Information Security, Sejong University, Seoul, Korea
7Advanced Communications Research Center at Samsung Research, Samsung Electronics, Seoul, Korea
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022012610336
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-01-26
The 3rd Generation Partnership Project (3GPP) finalized Release 15 specifications for the 5th Generation New Radio (5G NR) in June 2018. In Release 16, the 3GPP worked on not only technical improvements over the previous release but also the introduction of new features. One of the new features is the use of Two-step Random Access Channel (2-step RACH) that enhances 4-step random access with respect to radio resource control connection setup and resume procedures. In this article, we first look into details of 2-step random access defined in the 3GPP Release 16, and briefly introduce recent literature related to 2-step random access. Second, we present challenges of the above random access schemes. Among the challenges, we focus on how a User Equipment (UE) performs self-uplink synchronization with the next-generation Node B (gNB) to resolve preamble collisions, which occur when multiple UEs transmit the same preamble. Specifically, we propose a framework that helps the UE estimate the Timing Advance (TA) command using a deep neural network model and to determine the TA value. Finally, we evaluate the proposed framework in terms of the accuracy of TA command estimation, the inference time, and the battery consumption.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was partially supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2018-0-00815, Development of wireless LAN platform with smart cloud based multi radio structure); and in part by Sam- sung Research in Samsung Electronics. This work was also partially supported by the Academy of Finland Project CSN, under Grant Agreement 311654 and the 6Genesis project under Grant No. 318927.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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