Development of a Low-Complexity Fuzzy Logic-Based Packet Scheduler for URLLC in 5G New Radio Networks
Keywords:
5G new radio, Computational complexity, Fuzzy logic, Packet scheduling, URLLCAbstract
Ultra-Reliable Low-Latency Communication (URLLC) obliges the 5G New Radio Medium Access Control (MAC) scheduler to finish resource allocation within roughly 50 microseconds (µs) of every Transmission Time Interval. Existing fuzzy schedulers reported in the literature do not measure their per-decision computational cost against this budget, and are tuned to delay thresholds an order of magnitude more relaxed than the 1 millisecond (ms) URLLC deadline. This study introduces a Low-Complexity Fuzzy Logic-Based Packet Scheduler (LCFS) built on two normalized inputs, six triangular membership functions, a nine-rule Mamdani inference engine, and binary priority classification, capped at 61 arithmetic operations per user per decision. Tested in a single-cell 5G New Radio downlink with Poisson arrivals and Rayleigh fading against Modified Earliest Deadline First (EDF), Exponential Proportional Fair (EXP/PF), and a Conventional Fuzzy Logic Scheduler (CFLS) built from the published FUSE scheme, over 12 scenarios (100 Monte Carlo trials apiece), the proposed scheduler missed the deadline for 0.0870% of packets on average (reliability 99.913%), and its delivered packets had 0.2655 ms mean delay and 0.6862 ms mean 99th-percentile delay; because delay is measured over delivered packets only, the deadline miss ratio is the main basis for comparison. Its median scheduling time per TTI, averaged over the 12 scenarios, was 35.2 µs, against 54.9 µs for EXP/PF and 66.6 µs for the CFLS. Analysis of variance with Tukey post-hoc testing showed significantly lower packet delay than Modified EDF in every scenario and no significant difference in delay from EXP/PF or the CFLS. At the heaviest load, the proposed scheduler missed about 21 deadlines per 10,000 packets, against 28 for EXP/PF and 39 for the CFLS (25.6% and 45.8% fewer), but these differences were significant only in the two heaviest-load scenarios; under lighter loads, the three schedulers performed alike. Because the two fuzzy schedulers differ in inputs, classification and ranking as well as in delay threshold, the gain over the CFLS cannot be attributed to the deadline setting alone. A sensitivity analysis found no consistent effect of the scheduling-metric weight in the scenarios examined. The simulation includes no radio errors or retransmissions, so the reliability figures describe scheduling alone; the five-nines target was not reached even under this assumption.
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