High-Mobility Multi-Modal Sensing for IoT Network via MIMO AirComp: A Mixed-Timescale Optimization Approach

Abstract

Multiple-input-multiple-output (MIMO) over-the-air Computation (AirComp) is a promising technique for implementing high-mobility multi-modal sensing (HMS) to compute multiple target functions of distributed data by leveraging the superposition property of wireless multi-access channel, thereby significantly improving the computation efficiency as compared to the traditional orthogonal multi-access scheme. However, two obstacles constraining the potential gain of MIMO AirComp are high hardware cost and power consumption, and huge channel state information (CSI) signaling overhead. To overcome these challenges, we propose a novel mixed-timescale hybrid combining (MHC) scheme to minimize the average mean-square error (MSE) under some practical constraints in the uplink of a IoT network. Specifically, the radio frequency (RF) combiner is adapted to the long-term statistical CSI, while the baseband combiner is adapted to the real-time effective CSI. In order to tackle the resulting stochastic nonconvex optimization problem, we devise a mixed-timescale stochastic successive convex approximation (MSCA) algorithm. Simulation results are presented to verify the effectiveness of our proposed scheme over the state-of-the-art baselines.