TY - GEN
T1 - Efficient ultra-reliable and low latency communications and massive machine-Type communications in 5G new radio
AU - Lien, Shao Yu
AU - Hung, Shao Chou
AU - Deng, Der Jiunn
AU - Wang, Yueh Jir
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Different from the International Mobile Telecommunications Advanced (IMT-Advanced) system solely enhancing the transmission data rates regardless the variety of emerging wireless traffic, the IMT-2020 system supports enhanced mobile broadband (eMBB), massive machine-Type communications (mMTC) and ultra-reliable and low latency communications (URLCC) to fully capture diverse wireless services in 2020. To satisfactorily gratify the scope of IMT-2020, 3GPP has launched the standardization activity of the fifth generation (5G) New Radio (NR) to deploy the first phase (Release 15) system in 2018 and the ready (Release 16) system in 2020. As eMBB is a legacy system from IMT-Advanced, URLLC jointly demanding low latency and high reliability, and mMTC emphasizes on high reliability may consequently induce significant impacts on the designs of NR air interface. On the advert of the conventional feedback based transmission in LTE/LTE-A designed for eMBB imposing potential inefficiency in the support of URLLC and mMTC, in this paper, we revisit the feedbackless transmission framework, and reveal a tradeoff between these two transmission frameworks. A multi-Armed bandit (MAB) based reinforcement learning approach is therefore proposed to achieve the optimum harmonization of feedback and feedbackless transmissions. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC, to justify the potential of our approach in the design of 5G NR.
AB - Different from the International Mobile Telecommunications Advanced (IMT-Advanced) system solely enhancing the transmission data rates regardless the variety of emerging wireless traffic, the IMT-2020 system supports enhanced mobile broadband (eMBB), massive machine-Type communications (mMTC) and ultra-reliable and low latency communications (URLCC) to fully capture diverse wireless services in 2020. To satisfactorily gratify the scope of IMT-2020, 3GPP has launched the standardization activity of the fifth generation (5G) New Radio (NR) to deploy the first phase (Release 15) system in 2018 and the ready (Release 16) system in 2020. As eMBB is a legacy system from IMT-Advanced, URLLC jointly demanding low latency and high reliability, and mMTC emphasizes on high reliability may consequently induce significant impacts on the designs of NR air interface. On the advert of the conventional feedback based transmission in LTE/LTE-A designed for eMBB imposing potential inefficiency in the support of URLLC and mMTC, in this paper, we revisit the feedbackless transmission framework, and reveal a tradeoff between these two transmission frameworks. A multi-Armed bandit (MAB) based reinforcement learning approach is therefore proposed to achieve the optimum harmonization of feedback and feedbackless transmissions. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC, to justify the potential of our approach in the design of 5G NR.
UR - http://www.scopus.com/inward/record.url?scp=85046354620&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2017.8254211
DO - 10.1109/GLOCOM.2017.8254211
M3 - Conference contribution
AN - SCOPUS:85046354620
T3 - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
Y2 - 4 December 2017 through 8 December 2017
ER -