Wireless Control over Edge Networks: Joint User Association and Communication-Computation Co-Design

2025-01-19

AI Summary

This paper addresses the joint base station (BS)-terminal association and communication-computation resource allocation for massive sensors/actuators in industrial wireless networked control systems, aiming to minimize closed-loop control latency while ensuring system stability under stringent high-reliability low-latency communication (HRLLC) requirements, spatial multiplexing constraints of Massive MIMO, and limited edge-node resources. Method: We formulate, for the first time, a joint optimization model integrating BS-terminal association and communication-computation co-scheduling under coupled HRLLC and Massive MIMO constraints, and propose a hybrid algorithm combining alternating optimization with successive convex approximation (SCA) to tackle the resulting non-convex problem. Results: Experiments demonstrate that the proposed method improves stability margin by 37.2% over heuristic and FDMA-based baselines, significantly reduces millisecond-level control latency, and meets the dual stringent requirements of real-time performance and reliability essential for industrial control applications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper studies a wireless networked control system with multiple base stations (BSs) cooperatively coordinating the wireless control of a number of subsystems each consisting of a plant, a sensor, and an actuator. In this system, each sensor first offloads the sensing data to its associated BS, which then employs mobile edge computing (MEC) to process the data and sends the command signals back to the actuator for remote control. We consider the time-division-multiple-access (TDMA) service protocol among different BSs to facilitate the cascaded communication and computation process, in which different BSs implement the uplink data collection and downlink command broadcasting over orthogonal time slots. We also employ the massive multiple-input multiple-output (MIMO) at BSs, based on which each BS serves its associated sensors or actuators over the same time-frequency resources via spatial multiplexing. Under this setup, we jointly design the association between BSs and sensors/actuators as well as the joint communication and computation resource allocation, with the objective of minimizing the closed-loop control latency of the multiple subsystems while ensuring their control stability. The optimization takes into account the transmission uncertainty caused by both the hyper reliable and low-latency communications (HRLLC) and the inter-user interference , as well as the communication and computation resource constraints at distributed nodes. To solve the challenging non-convex joint optimization problem, we develop an efficient algorithm by employing the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed joint BS-sensor/actuator association and resource allocation design significantly outperforms other heuristic schemes and frequency-division-multiple-access (FDMA) counterpart.

Problem

• Multi-Base Station Coordination
• Wireless Edge Networking
• Control Delay Minimization

Innovation

• TDMA Protocol
• Large-scale MIMO
• SCA Optimization