Sparse Target Parameter and Channel Estimation in MIMO-OOFDM-Aided Visible Light Integrated Sensing and Communication Systems
A unified framework is developed for parameter estimation (PE) and channel estimation (CE) in monostatic multiple-input multiple-output (MIMO) direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM)-aided visible light integrated sensing and communication (VL-ISAC) systems. A common optical waveform supports sensing and downlink communication while accounting for target reflections, clutter, Doppler effects, and the communication channel. For sensing PE, one-dimensional (1D) and two-dimensional (2D) on-grid and off-grid sparse representations are formulated. The proposed 1D off-grid Bayesian learning (1D-OffGBL) algorithm employs a joint delay-Doppler (DD) dictionary to exploit coupled DD information and achieve high estimation accuracy. In contrast, the proposed 2D off-grid Bayesian learning (2D-OffGBL) algorithm sequentially estimates the Doppler and delay parameters, thereby avoiding a high-dimensional joint dictionary and reducing computational complexity. Both algorithms iteratively refine the continuous-valued delay and Doppler parameters to mitigate off-grid basis mismatch. Furthermore, 1D and 2D sparse channel representations and corresponding CE procedures are developed, followed by range and velocity estimation for both sensing models. Closed-form Bayesian Cramer-Rao bounds (BCRBs) are derived for PE, CE, range, and velocity estimation. Simulations demonstrate that the proposed algorithms outperform conventional sparse recovery schemes in terms of normalized mean-squared error (NMSE). In particular, 1D-OffGBL achieves the highest estimation accuracy, whereas 2D-OffGBL provides a favourable performance-complexity trade-off for practical MIMO DCO-OFDM VL-ISAC systems.