Ansari, Arezoo (2026) Enhancing URLLC Performance in Teleoperation Systems through Dual Prediction and Resource Optimization. Masters thesis, Concordia University.
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Abstract
Ultra-reliable and low-latency communication (URLLC) is a key enabler for mission-critical applications in next-generation wireless networks. However, simultaneously achieving ultra-high reliability and ultra-low latency remains a major challenge. In this work, we propose 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.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Ansari, Arezoo |
| Institution: | Concordia University |
| Degree Name: | M.A. |
| Program: | Electrical and Computer Engineering |
| Date: | 3 March 2026 |
| Thesis Supervisor(s): | Cai, Jun |
| Keywords: | Ultra-reliableandlow-latencycommunications, URLLC, predictionandcommunicationco-design, dualpredic tionscheme,DPS, teleoperations, tactile internet (TI). |
| ID Code: | 996795 |
| Deposited By: | Arezoo Ansari |
| Deposited On: | 29 Jun 2026 14:40 |
| Last Modified: | 29 Jun 2026 14:40 |
| Additional Information: | Thesis file |
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