Delay Performance Analysis with Short Packets in Intelligent Machine Network

TL;DR

This paper analyzes delay performance in intelligent machine networks using queuing theory and stochastic geometry, focusing on short packet transmissions and their impact on delay metrics.

Contribution

It introduces a comprehensive model for IM networks, deriving key delay metrics with finite blocklength capacity, which was previously underexplored.

Findings

Increasing packet length and IM density worsens delay metrics.

Short packets significantly improve delay performance in high-density scenarios.

The results provide a theoretical basis for optimizing IM network design.

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.