Downlink MIMO OFDM Communication-Enabled Vehicle State Detection towards 6G ISAC

We focus on the target state detection (TSD) based on multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) communications. Yet, communication-based TSD is difficult, due to its complex signal structure and OFDM symbol randomness. To address this challenge, we first exploit structured features of low-speed and narrow-band system models to decouple the target state parameters and random OFDM symbols, and then use coherent detection to remove the random OFDM symbols. As such, a simplified sensing channel model is obtained, which preserves an explicit harmonic structure in the frequency, time and space domains, with respect to (w.r.t.) target speed, distance and direction angle, respectively. Then, an efficient MIMO OFDM waveform-based TSD method is devised by extracting such harmonic features. It is verified that the proposed TSD method outperforms state-of-the-art baselines, due to our problem-specific algorithm design (i.e., structured feature extraction, parameter decoupling, random symbol removing and harmonic structure exploitation), which builds a technical foundation for environment sensing-assisted communications.