Task-Aware Closed-Loop Communication Delay Analysis and Optimization for Intelligent Machine Networks

Intelligent machine networks provide wireless connectivity for automated guided vehicles (AGVs) in flexible final-assembly workshops. AGVs switch between assembly and non-assembly tasks with different closed-loop interaction periods, which changes packet-generation rates and creates time-varying uplink and downlink traffic demands. Such traffic asymmetry makes fixed or periodic time-division duplexing (TDD) configurations difficult to adapt to task variations. This paper proposes a task-aware closed-loop communication delay analysis and dynamic TDD resource allocation framework for multi-AGV networks. First, the relationship between AGV task states, interaction periods, and uplink/downlink packet arrival rates is established. Finite-blocklength transmission theory and M/M/1 queueing approximations are then employed to characterize transmission and queueing delays and obtain the overall closed-loop communication delay. Based on the task-dependent traffic demands, a reference TDD slot ratio is determined to reflect the required uplink and downlink resources. An event-triggered allocation method is further developed under a limited TDD reconfiguration budget, where the slot ratio is updated only when its deviation from the reference ratio exceeds an offline-selected threshold. To further enhance resource allocation adaptability, a PPO-based method is introduced to dynamically adjust the uplink–downlink slot ratio according to traffic demands, queue conditions, and the remaining reconfiguration budget. Numerical results demonstrate that both proposed methods reduce the average closed-loop communication delay compared with offline-optimized fixed-ratio and periodic allocation. At N=21, PPOachieves 1.04770 ms, compared with 1.112 ms for the additional advantage actor–critic (A2C) baseline, corresponding to a 5.78% reduction. PPO also reduces the delay by 19.65% relative to Optimization-Fixed. These results show that adapting TDD resources to task-induced traffic variations can improve multi-AGV closed-loop communication performance under the evaluated settings and limited reconfigurations.