Cooperative ISAC for Localization and Velocity Estimation Using OFDM Waveforms in Cell-Free MIMO Systems

In this paper, we present a cooperative integrated sensing and communication (ISAC) framework in cell-free multiple-input multiple-output (MIMO) systems, where multiple access points (APs), under the control of a central processing unit (CPU), collaboratively perform target sensing by using the reflected echo signals. Most of the existing works first estimate the sensing parameters (e.g., range, angle, relative velocity) observed by each AP and then use these estimated parameters for sensing tasks such as localization and velocity estimation. However, this approach may suffer from performance degradation due to errors in the estimated parameters. We propose a deep neural network (DNN)-based scheme to jointly process the echo signals received across the distributed APs and directly estimate the location and velocity of the targets. The proposed scheme bypasses the sensing parameter estimation stage and enhances the sensing performance. Simulation results show that our proposed scheme significantly reduces the localization and velocity estimation error when compared with a state-of-the-art approach.