arXiv:2611.09544v1 Announce Type: new Abstract: Integrated sensing and communication (ISAC) has emerged as a promising paradigm for future wireless networks. However, the extensive occupation of time-frequency resources for sensing limits the communication capacity. To address the resource conflict, this paper proposes a novel sparse sensing frame structure aligned with the 5G new radio (5G NR) resource grid at the slot and physical resource block (PRB) levels through a predefined sensing-pattern block. Specifically, inspired by the design of two-dimensional (2D) sparse antenna arrays, we map the sparse spatial geometries onto the time-frequency resource grid. By leveraging the hole-free property of the difference coarray, we eliminate the range-velocity ambiguity and cast the sensing signals into a reconstructed higher-order tensor representation. For efficient parameter estimation, we propose a novel binary-tree-based tensor train decomposition (BT-TTD) algorithm that extracts lower-dimensional tensor train cores (TT-cores) and executes a Vandermonde-prior-guided rectified alternating least squares (RecALS) algorithm. Simulation results validate that the proposed scheme achieves competitive estimation accuracy with reduced complexity. Furthermore, we investigate the topological properties of various sparse patterns, revealing the inherent trade-off among sensing accuracy, resource occupancy, and computational complexity.