Environment-Aware Scheduling of URLLC and Sensing Services for Smart Industries

TL;DR

The paper tackles ISAC resource management in indoor factories where dynamic clutter and fluctuating URLLC traffic challenge joint AGV detection and downlink reliability. By formulating a constrained optimization to maximize AGV detection probability $P_d$ under URLLC delay constraints and solving it with a Nash Bargaining Solution, it derives a closed-form time-sharing rule that adaptively allocates sensing time $n_{sym}^s$ and communication time $n_{sym}^c$ within the total available slots $N_{sym}^{tot}$. A Python SimPy-based link-level simulator evaluates the approach under 3GPP TR 38.901–based InF channels, showing that the Nash Bargaining scheme improves $P_d$ while keeping URLLC end-to-end delays below the required survival time, outperforming round-robin baselines. The work provides a practical, environment-aware mechanism for ISAC in smart industries, with explicit expressions for sensing-time requirements and a tractable optimal allocation that responds to clutter density and traffic demand.

Abstract

In this paper, we address the problem of scheduling sensing and communication functionality in an integrated sensing and communication (ISAC) enabled base station (BS) operating in an indoor factory (InF) environment. The BS is performing the task of detecting an AGV while managing downlink transmission of ultra-reliable low-latency communication (URLLC) data in a time-sharing manner. Scheduling fixed time slots for both sensing and communication is inefficient for the InF environment, as the instantaneous environmental changes necessitate a higher frequency of sensing operations to accurately detect the AGV. To address this issue, we propose an environment-aware scheduling scheme, in which we first formulate an optimization problem to maximize the probability of detection of AGV while considering the survival time constraint of URLLC data. Subsequently, utilizing the Nash bargaining theory, we propose an adaptive time-sharing scheme that assigns sensing duration in accordance with the environmental clutter density and distributes time to URLLC depending on the incoming traffic rate. Using our own Python-based discrete-event link-level simulator, we demonstrate the effectiveness of our proposed scheme over the baseline scheme in terms of probability of detection and downlink latency.

Figures (5)

• Figure 1: Illustration of smart InF environment.
• Figure 2: System concept.
• Figure 3: Distribution of URLLC packet delay.
• Figure 4: Probability of detection for different false alarm rates and schedulers.
• Figure 5: Comparison of the number of dedicated resources for sensing and communication.