A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction

Abstract

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10 th user throughput percentile by up to 125% for eMBB use case and decreases the 90 th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

VarOLLA: Maximizing Throughput for 5G-RedCap Devices in IIoT Networks

“…The channel model employed in the simulations is generated using the quasi-deterministic radio channel generator (QuaDRiGa) [19], utilizing the propagation parameters specifically designed for industrial indoor scenarios [20]. This setup has been used in our previous research on industrial networks as well [4]. The simulation parameters are summarized in Table I.…”

“…OLLA intelligently varies the MCS chosen by the BS based on the feedback received from the UE. However, OLLA is known to exhibit sub-optimal throughput, particularly in industrial scenarios [4].…”

Assessment of 5G RAN Features for Integrated Services Provisioning in Smart Cities

“…To map a set of PRB specific SINRs to a single effective SINR value for the full set of PRBs, we use the Mutual Information Effective SINR Mapping (MIESM) method [24]. Finally, an Outer-Loop Link Adaptation (OLLA) scheme is used to modify the mapping of the SINR to an MCS due to imperfections in channel quality reporting [25].…”