Noise Reduction via Low Rank Tensor Decomposition for MIMO ISAC Systems

Sensing function in integrated sensing and communication (ISAC) system concentrates on collecting and extracting information of the targets from noisy observations, which can assist positioning the users and enable a precise directional communication link. This paper deals with noise reduction via tensor ring (TR) decomposition and total variation (TV) for linear frequency modulated continuous-wave (FMCW) signals in the multiple-input multiple-output ISAC system. Specifically, TR decomposition is used to exploit the low-rankness and describe the global correlation among different dimensions of the high-order received signal. The noise suppression is addressed by the integration of a TV regularization and a Frobenius norm term to ensure sufficient signal-to-noise ratio (SNR). The corresponding optimization problem is solved using augmented Lagrange multiplier (ALM) and proximal alternating minimization. Simulation results illustrate that the proposed method improves denoising performance, leading to a higher output SNR of the target and a better detection probability.