Gridless and Fast 4-D ISAC Parameter Estimation for MU-MIMO-OFDM Based on FFOMP-OGR

Abstract

With the rise of sixth-generation wireless systems, Integrated Sensing and Communication (ISAC) increasingly adopts MU-MIMO-OFDM for four-dimensional joint parameter estimation. However, existing approaches face two key challenges: the computational complexity of super-resolution grows rapidly with grid refinement, and on-grid methods are limited in accuracy and prone to spurious estimates. This paper proposes the Fast Fourier Orthogonal Matching Pursuit with Off-Grid Refinement (FFOMP-OGR) algorithm to address both issues. First, FFOMP-OGR exploits the Vandermonde structure in 4D MU-MIMO-OFDM signals to perform FFT-based dictionary matching within OMP, reducing complexity without being constrained by on-grid resolution. Second, it augments OMP with a nonlinear least-squares refinement that jointly optimizes atom positions and amplitudes, achieving gridless, arbitrary-precision estimates while preserving enhanced orthogonality. Simulation results demonstrate that the FFOMP-OGR algorithm significantly reduces computational complexity while ensuring estimation accuracy, providing an effective solution for the practical implementation of ISAC systems.