Off-Grid Parameter Estimation for OFDM-Based ISAC Systems with Incomplete Data

In the 6G environment, addressing the challenges of data loss and off-grid issues during target parameter estimation poses a significant challenge for the Integrated Sensing and Communication (ISAC) system. In the ISAC framework, a commonly used method for parameter estimation is compressive sensing. However, compressive sensing may encounter off-grid issues in continuous parameter estimation. In contrast, the atomic norm proves effective in addressing off-grid problems, making it more suitable for continuous parameter estimation. We explore the application of the atomic norm in ISAC and further derive an ISAC model based on OFDM (Orthogonal Frequency Division Multiplexing) utilizing the atomic norm under conditions of incomplete data. To ensure improved convergence speed and accuracy of our algorithm, we employ the Alternating Direction Method of Multipliers (ADMM) for iterative implementation. Experimental results demonstrate that our proposed AN algorithm accurately estimates target parameters in the presence of data loss, exhibiting higher precision and robustness compared to traditional methods.