  The sixth generation (6G) systems will likely employ orthogonal frequency
division multiplexing (OFDM) waveform for performing the joint task of sensing
and communication. In this paper, we design an OFDM system for integrated
sensing and communication (ISAC) and propose a novel approach for passive
target detection in an indoor deployment using a data driven AI approach. The
delay-Doppler profile (DDP) and power delay profile (PDP) is used to train the
proposed AI-based detector. We analyze the detection performance of the
proposed methods under line of sight (LOS) and non-line of sight (NLOS)
conditions for various training strategies. We show that the proposed method
provides 10 dB performance improvement over the baseline for 80% target
detection under LOS conditions and the performance drops by 10-20 dB for NLOS
depending on the usecase scenarios.
