Resource Allocation in Wireless Control Systems via Deep Policy Gradient

• Published in: SPAWC : signal processing advances in wireless communications pp. 1 - 5
• Main Authors: Lima, Vinicius, Eisen, Mark, Gatsis, Konstantinos, Ribeiro, Alejandro
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2020
• Subjects: Control systems; Gradient methods; Reliability; Resource management; Scheduling; Wireless communication; Wireless sensor networks
• ISSN: 1948-3252
• DOI: 10.1109/SPAWC48557.2020.9154311

In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limited resources, e.g. transmission power, across plants is critical for maintaining reliable operation. In this paper, we formulate the design of an optimal resource allocation policy that uses current plant states and wireless channel conditions to assign resources used to send control actuation information back to plants. While this problem is challenging due to its infinite dimensionality and need for explicit system model and state knowledge, we propose the use of deep reinforcement learning techniques to find data-driven resource allocation policies. In particular, we use model-free policy gradient methods to directly learn continuous power allocation policies without knowledge of plant dynamics or communication models. Numerical simulations demonstrate the strong performance of learned policies relative to baseline resource allocation methods.