DL-Based ISAC via Tensor Analysis in Massive MIMO-OFDM Systems With Spatial-Frequency Wideband Effects

In this article, we propose a novel integrated sensing and communication (ISAC) algorithm for massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with spatial-frequency wideband (SFW) effects. To obtain high accuracy of channel state information (CSI), the proposed algorithm initially utilizes a deep neural network (DNN) for channel estimation. Then, the estimated channel is expressed as a third-order low-rank tensor model, on which the canonical polyadic (CP) decomposition is performed to obtain three factor matrices. These factor matrices hold the information pertaining to channel parameters. By fitting the constructed tensor model, channel parameters, such as Angles of Departure (AoDs), Angles of Arrival (AoAs), time delay, and complex gains, can be extracted. Ultimately, the positions of mobile station (MS) and scattering points are determined by utilizing the mapping relationship between the channel parameters and position coordinates. In contrast to existing algorithms, the proposed algorithm delivers greater precision in both channel estimation and positioning. The simulation results demonstrate that the proposed algorithm maintains outstanding ISAC performance, persisting even with diminished compression rate. Furthermore, the proposed algorithm proves effective in more complex scenarios lacking a line-of-sight (LOS) path.