An Optimization of Feedback Signal Transmission and Reconstruction for Tactile Internet: A Multi-Objective Bayesian Approach

In this paper, we present a novel multi-objective Bayesian optimization approach for tactile feedback transmission and reconstruction in the NFV-based tactile Internet (NFV-based TI). For such a system, guaranteeing real-time and precise feedback performance is crucial for achieving seamless remote interactions. By optimizing the virtual network function (VNF) placement, routing paths, admission control strategies for each service function chain (SFC), and wireless resource allocation (e.g., resource blocks), this paper aims to optimize the feedback signal transmission and reconstruction, thereby ensuring a high-fidelity user experience. In contrast to existing work, we investigate the dynamic adjustment under uncertain network conditions and propose a multi-objective Bayesian optimization approach tailored for hybrid black-white box problems. Specifically, we design a hybrid kernel function surrogate model, which is combined with novel sampling strategies explicitly developed for the dynamic network conditions of NFV-based TI. Moreover, our proposed approach can adaptively adjust the parameters of tactile feedback transmission and reconstruction according to the current network conditions and changes in user requirements. This enables the system to find an optimal trade-off between tactile feedback delay and tactile feedback fidelity, ultimately achieving Pareto-optimal solutions. Simulation results validate the proposed solution and demonstrate its superiority over counterparts.