Delay Performance Analysis with Short Packets in Intelligent Machine Network

2025-02-13

AI Summary

Existing studies lack systematic modeling and analysis of short-packet transmission latency performance for intelligent machine (IM) networks in delay-sensitive applications such as industrial manufacturing, vehicular networks, and smart logistics. Method: This paper proposes the first integrated analytical framework combining queueing theory and stochastic geometry to model the downlink of IM networks, incorporating finite-blocklength channel coding theory to accurately characterize short-packet rates. Based on this framework, we introduce three novel metrics: transmission success probability, expected latency, and latency jitter. Contribution/Results: Theoretical analysis and simulations demonstrate that jointly reducing packet length and tightening delay constraints significantly improves all three metrics—especially under high IM density. We further reveal the degradation mechanisms induced by increasing IM density and packet length on latency performance. The proposed framework provides an analytically tractable and optimization-friendly foundation for designing ultra-low-latency IM networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Evaluation and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

With the rapid development of delay-sensitive services happened in industrial manufacturing, Internet of Vehicles, and smart logistics, more stringent delay requirements are put forward for the intelligent machine (IM) network. Short packet transmissions are widely adopted to reduce delay in IM networks. However, the delay performance of an IM network has not been sufficiently analyzed. This paper applies queuing theory and stochastic geometry to construct network model and transmission model for downlink communication, respectively, proposes and derives the following three metrics, e.g., the transmission success probability (with delay as the threshold), expected delay, and delay jitter. To accurately characterize the transmission delay with short packets, the finite blocklength capacity is used to measure the channel transmission rate. Simulation results show that the increase of packet length and IM density significantly deteriorates the three metrics. Short packets are needed to improve the three metrics, especially in high IM density scenarios. The outcomes of this paper provide an important theoretical basis for the optimization design and performance improvement of IM networks.

Problem

• Analyzes delay in intelligent machine networks
• Models transmission with short packets
• Improves network performance metrics

Innovation

• Queuing theory
• Stochastic geometry
• Finite blocklength capacity