Adaptive Transmission Parameters Selection Algorithm for URLLC Traffic in Uplink

TL;DR

The paper tackles uplink URLLC over grant-free access, where long-term transmission parameters must adapt to time-varying channels. It introduces an adaptive MCS and K-selection algorithm that uses gNB-provided SNR statistics and an EESM-based BLER mapping to estimate per-configuration PLRs, updating these estimates with an exponential moving average over a window $W$. Configurations are deemed valid or invalid via thresholds $PLR_ ext{low}$ and $PLR_ ext{high}$, and the gNB selects the configuration with minimum resource use, $M_ ext{MCS} imes K$, updating the UE only when needed. NS-3 simulations show the approach reduces channel resource consumption by more than a factor of two relative to fixed robust settings and attains near-optimal performance for $ ext{SNR}_{wb} \\ge -1$ dB, while enabling infrequent reconfigurations and thus lower control overhead. The method enables long-term parameter adaptation, offering practical benefits for URLLC in dynamic wireless environments.

Abstract

Ultra-Reliable Low-Latency Communications (URLLC) is a novel feature of 5G cellular systems. To satisfy strict URLLC requirements for uplink data transmission, the specifications of 5G systems introduce the grant-free channel access method. According to this method, a User Equipment (UE) performs packet transmission without requesting channel resources from a base station (gNB). With the grant-free channel access, the gNB configures the uplink transmission parameters in a long-term time scale. Since the channel quality can significantly change in time and frequency domains, the gNB should select robust transmission parameters to satisfy the URLLC requirements. Many existing studies consider fixed robust uplink transmission parameter selection that allows satisfying the requirements even for UEs with poor channel conditions. However, the more robust transmission parameters are selected, the lower is the network capacity. In this paper, we propose an adaptive algorithm that selects the transmission parameters depending on the channel quality based on the signal-to-noise ratio statistics analysis at the gNB. Simulation results obtained with NS-3 show that the algorithm allows meeting the URLLC latency and reliability requirements while reducing the channel resource consumption more than twice in comparison with the fixed transmission parameters selection.

Figures (6)

• Figure 1: SNR-BLER curves for 25 iterations of a decoder.
• Figure 2: Valid configurations marking.
• Figure 3: Comparison of fixed and adaptive MCS selection.
• Figure 4: Channel resource consumption of the proposed algorithm.
• Figure 5: Optimal PLR$_{\text{low}}$ and PLR$_{\text{high}}$ thresholds.