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The digital twin edge network (DITEN) aims to integrate mobile edge computing
(MEC) and digital twin (DT) to provide real-time system configuration and
flexible resource allocation for the sixth-generation network. This paper
investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid
computing system that can achieve mutual benefits for DT and MEC in the DITEN.
For the first time, this paper presents the opportunity to realize the
network-wide convergence of DT and MEC. In the considered system, specifically,
over-the-air computation (AirComp) is employed to monitor the status of the DT
system, while MEC is performed with the assistance of DT to provide low-latency
computing services. Besides, the IRS is utilized to enhance signal transmission
and mitigate interference among heterogeneous nodes. We propose a framework for
designing the hybrid computing system, aiming to maximize the sum computation
rate under communication and computation resources constraints. To tackle the
non-convex optimization problem, alternative optimization and successive convex
approximation techniques are leveraged to decouple variables and then transform
the problem into a more tractable form. Simulation results verify the
effectiveness of the proposed algorithm and demonstrate the IRS can
significantly improve the system performance with appropriate phase shift
configurations. Moreover, the results indicate that the DT assisted MEC system
can precisely achieve the balance between local computing and task offloading
since real-time system status can be obtained with the help of DT.