Predictive control and communication co-design via two-way Gaussian process regression and AoI-aware scheduling
Girgis, Abanoub M.; Park, Jihong; Bennis, Mehdi; Debbah, Mérouane (2021-07-26)
A. M. Girgis, J. Park, M. Bennis and M. Debbah, "Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling," in IEEE Transactions on Communications, vol. 69, no. 10, pp. 7077-7093, Oct. 2021, doi: 10.1109/TCOMM.2021.3099156
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2021122162715
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Abstract
This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only one control system is controlled, while the rest of the control systems are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control 2× more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is 18× larger than a round-robin scheduling baseline.
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