Efficient Ultra-Reliable and Low Latency Communications and Massive Machine-Type Communications in 5G New Radio

Abstract

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.