Hybrid Intelligent Reflecting Surface and Cell-free Massive MIMO-Aided Over-the-Air Computation for Digital Twin

This paper investigates the over-the-air computation (AirComp) problem in a hybrid intelligent reflecting surface (IRS) and cell-free massive multiple-input multiple-output (CF-mMIMO) assisted digital twin system, where multiple users offload their data to access points (APs) and central processing unit (CPU) via the IRS for data aggregation. We formulate a joint beamforming design, IRS phase shift optimization, and power allocation problem to minimize the mean squared error (MSE) of data aggregation. We solve the resultant non-convex optimization problem in three steps. First, we transform the original problem into two sub-problems. Then, we exploit a convex optimization framework to respectively determine the beamforming design, IRS phase shift optimization, and power allocation. Last, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.