Target Sensing in Full-Dimensional MIMO-OFDM ISAC Systems Via Coupled Tensor Decomposition

Abstract

Integrated sensing and communication (ISAC) has been recognized as a key enabler for future wireless communication systems. However, existing ISAC designs based on MIMO-OFDM generally fail to fully exploit the spatial degrees of freedom and the communication data payload, resulting in restricted target sensing performance. To cope with this issue, in this paper we propose a novel target sensing method for full-dimensional (FD) MIMO-OFDM ISAC systems based on coupled tensor decomposition. We first characterize the system model of FD MIMO-OFDM ISAC, where the base station employs a large number of antennas in two dimensions and facilitates both sensing and communication via the data payload of OFDM signals. Then, we formulate the problem of estimating target parameters as a coupled canonical polyadic decomposition (CPD) problem, which jointly leverages the echo signals of communication data payload over multiple subcarriers in the frequency domain. The theoretical analysis indicates that the formulated coupled CPD problem possesses a more relaxed uniqueness condition compared with the ordinary CPD problem, thereby enabling more stable target sensing performance through the pure communication data payload in FD MIMO-OFDM ISAC. In addition, we develop a coupled CPD-based target sensing algorithm, where the target parameters, including azimuth and elevation angles, ranges, and velocities, are extracted from the decomposed factor matrices. Numerical results show that the proposed target sensing method achieves a higher success rate and more accurate parameter estimation than existing tensor decomposition methods.