Data-Aided Bistatic Sensing and Communication for mmWave MIMO-OFDM ISAC Systems

A data-aided (DA) framework is proposed for joint wireless channel and bistatic target parameter estimation in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. The framework begins with the formulation of a multi-measurement vector (MMV)-based sparse reconstruction problem for beamspace communication channel estimation. To solve this, a pilot-aided MMV-based Bayesian learning (PA-MBL) algorithm is developed, enabling efficient estimation of the beamspace channel. These channel estimates provide coarse target parameters, including the direction of arrival (DoA), direction of departure (DoD), range, and reflection coefficient. While DoA and DoD are obtained from a predefined angular grid, a specialized approach is designed to estimate the range in a continuous domain. Furthermore, the proposed sensing-assisted communication method estimates the communication channel across all subcarriers while requiring only a small number of pilot subcarriers, enhancing spectral efficiency. A subsequent DA scheme is introduced to jointly refine the estimation of target parameters and data, leading to further improvements in accuracy. Extensive simulations validate the effectiveness of the proposed framework, demonstrating significant performance gains in terms of normalized mean squared error (NMSE), bit error rate (BER), and 2D imaging.