摘要
In the 6G environment,addressing challenges like missing data,demodulation errors,and offgrid issues during target parameter estimation is a significant hurdle for integrated sensing and communication(ISAC)systems.In the ISAC framework,a commonly used method for parameter estimation is compressive sensing.However,it often struggles with off-grid problems in continuous parameter estimation.In contrast,the atomic norm has been proven effective in overcoming these off-grid issues,making it a more suitable approach for continuous parameter estimation.In this paper,we investigate the application of atomic norm in ISAC and propose an ISAC model based on orthogonal frequency division multiplexing(OFDM)for parameter estimation.We utilize the atomic norm under conditions of incomplete data and demodulation errors.To enhance the convergence speed and accuracy of our algorithm,we implement the alternating direction method of multipliers(ADMM)for iterative processing.We refer to this algorithm as ANMI.Building on this foundation,we develop a deep unfolding network algorithm,ANMIADMM-Net,which further mitigates the impact of missing data and demodulation errors on target parameter estimation by training optimal parameters.Experimental results demonstrate that our proposed ANMI and ANMI-ADMM-Net accurately estimate target parameters even in the presence of missing data and demodulation errors,exhibiting superior precision and robustness compared to traditional methods.
基金
supported in part by Natural Science Foundation of China under Grant 62571133 and 62571135
Natural Science Foundation of Fujian Province under Grant 2025J01459.
