Deep-Learning-Based Multinode ISAC 4D Environmental Reconstruction With Uplink–Downlink Cooperation

Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for integrated sensing and communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning-based multinode ISAC 4D environment reconstruction method with the uplink-downlink (UL-DL) cooperation, which employs virtual aperture technology, constant false alarm rate (CFAR) detection, and mutiple signal classification (music) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving the multinode cooperation and UL-DL cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models attention gate gridding residual neural network (AGGRNN) and multiview sensing fusion network (MVSFNet) to enhance the density of the sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multilevel fusion strategy incorporating both the data-level and feature-level fusion to fully leverage the advantages of the multinode cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms the other comparative method, enabling high-precision environmental reconstruction using the ISAC system.