A Tensor-Based Signal Processing for ISAC Using C-DRCNN in RIS-Assisted mmWave MIMO-OFDM Systems

In the sixth-generation (6G) Internet of Everything (IoE) environment, integrated sensing and communication (ISAC) can improve the utilization of radio resources. The application of reconfigurable intelligent surface (RIS) and millimeter wave (mmWave) can improve the performance of the ISAC. In this paper, we propose an ISAC algorithm for RIS-assisted multi-user mmWave multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The proposed ISAC algorithm can achieve simultaneous channel estimation, positioning and environment mapping. Considering the limited robustness of the traditional algorithms to noise, the proposed algorithm first uses a complex-valued depth residual convolution neural network (C-DRCNN)-assisted channel estimation algorithm by using the powerful computational power of deep learning. Further, the sparsity of the mmWave channel can be utilized by the parallel factor (PARAFAC) tensor decomposition for obtaining the factor matrices, which contain the channel parameters such as direction-of-arrival (DOA), direction-of-departure (DOD), time delay (TD), and complex path gain. Finally, the multi-user positioning and environment mapping are realized according to the geometric relationship between the position and channel parameters. The simulation results demonstrate that the proposed algorithm achieves better channel estimation, multi-user positioning and environment mapping performance compared with the state-of-the-art algorithm. In addition, since the proposed algorithm integrates the deep neural network and tensor decomposition, it still has excellent ISAC performance even at low signal-to-noise ratio (SNR).