  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 multi-node ISAC 4D environment
reconstruction method with 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 multi-node
cooperation and Uplink-Downlink (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 Multi-View Sensing Fusion Network (MVSFNet) to enhance the density
of 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 multi-level fusion
strategy incorporating both data-level and feature-level fusion to fully
leverage the advantages of multi-node cooperation. Experimental results
demonstrate that the environmental reconstruction performance of this method
significantly outperforms other comparative method, enabling high-precision
environmental reconstruction using ISAC system.
