Joint Sensing Adaptation and Model Placement in 6G Fabric Computing

Sensing and computing based on intelligent fabrics can meet the ultra-reliable and low-latency communication (URLLC) needs of sixth-generation wireless (6G) by integrating sensing units into fabric fibers to perceive user data. Although some researchers have designed sensing or computing solutions, such solutions have not been well explored. In this paper, we consider the joint sensing adaptation and model placement in a 6G fabric space. We first propose an intelligent-fiber-driven 6G fabric computing network to minimize acquisition latency while ensuring accuracy. Then, we formulate an optimization model that takes the fabric sampling rate, sampling density, and model placement as variables. To solve the model, we propose an effective learning algorithm based on deep reinforcement learning. That is, by transforming the optimization problem into a state space, action space, and reward function, we design an optimal sensing and placement scheme. The simulation results show that our proposed scheme can achieve optimal sensing and computing compared with several baseline algorithms.