Enhancing URLLC Performance in Teleoperation Systems Through Dual Prediction and Resource Optimization

Ultra-reliable and low-latency communication (URLLC) is a key enabler for mission-critical applications in next-generation wireless networks. However, achieving both ultra-high reliability and ultra-low latency simultaneously remains a major challenge. This paper proposes a novel Dual Prediction Scheme (DPS) for URLLC-based teleoperation systems, where predictive algorithms are deployed at both the transmitter and the receiver to jointly mitigate latency and enhance reliability. In the proposed framework, the receiver reconstructs delayed or lost packets through local prediction, while the transmitter proactively encapsulates multiple predicted future states into a single short packet to safeguard against consecutive losses. To evaluate system reliability and energy efficiency, we formulate a joint optimization problem that minimizes the average transmit power subject to URLLC constraints by jointly optimizing bandwidth allocation and prediction horizons. The resulting problem is non-convex and is efficiently solved via an iterative algorithm. Simulation results verify that the proposed DPS significantly reduces transmit power while satisfying URLLC requirements, demonstrating its strong potential for real-time and energy-efficient wireless teleoperation systems.