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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM46438
Volume:
9 |
4 |
Apr 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
This paper presents an exhaustive investigation of latency optimization in 5G Ultra-Reliable Low-Latency Commu- nication (URLLC) systems. The emergence of 5G Ultra-Reliable Low-Latency Communication (URLLC) has revolutionized wire- less systems by enabling mission-critical applications demanding sub-1ms latency with 99.999% reliability. This paper presents a comprehensive analysis of URLLC latency optimization through three key dimensions: mini-slot scheduling, Hybrid Automatic Repeat Request (HARQ) enhancements, and duplexing method selection (FDD/TDD). Using MATLAB simulations and a 5G network emulator, we evaluate latency performance under di- verse configurations, including 2/4/7-symbol mini-slots, variable UE processing capabilities (1.2ms vs 2.4ms), and bandwidth allo- cations (40MHz/100MHz). Our results demonstrate that 2-symbol mini-slots achieve 52% lower latency (1.8ms in FDD, 3.2ms in TDD) compared to traditional slot-based scheduling, albeit with a 30% throughput tradeoff. The study reveals that UE processing heterogeneity introduces ±0.8ms latency variation in FDD and ±1.2ms in TDD, underscoring the need for standardized UE capabilities. Furthermore, TDD systems incur 1.8ms additional latency due to frame structure constraints, necessitating dynamic slot format adaptation. The paper also quantifies the impact of HARQ timing parameters (K0/K1/K2) and shows that grant- free uplink access reduces control plane latency by 40%. These findings are validated through real-world test scenarios including industrial automation and vehicular communications. We provide actionable recommendations for network operators, including optimal mini-slot configurations for specific use cases and a framework for AI-driven dynamic scheduling. The study bridges the gap between theoretical 3GPP specifications and practical deployment challenges, offering insights for 5G-Advanced and 6G URLLC evolution.
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Publication Date
4/29/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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