基于稀疏贝叶斯学习的AFDM-ISAC系统数据辅助信道估计与感知
Data-aided Channel Estimation and Sensing With Sparse Bayesian Learning for AFDM-ISAC System
AI总结：
针对AFDM-ISAC系统在双色散信道中提高频谱效率与获取准确参数的挑战，提出数据辅助网格演化稀疏贝叶斯学习方案，含高效迭代接收机、网格演化程序及低复杂度算法，数值结果验证了方案有效性与优越性。
AI中文摘要：
仿射频分复用（AFDM）已成为下一代集成感知与通信（ISAC）系统中一种很有前景的波形。然而，在同时获得准确信道和感知相关参数的情况下提高频谱效率具有挑战性，尤其是在具有分数延迟和分数多普勒频移的双色散信道中。为应对这一挑战，我们将信道估计任务表述为多测量向量（MMV）离网格稀疏恢复问题，提出了一种数据辅助网格演化稀疏贝叶斯学习（D-GESBL）方案。具体而言，开发了一种高效的数据辅助迭代接收机，将可靠解码的数据符号作为额外的伪导频信息反馈以辅助信道估计和感知。为减轻离网格失配并提高整体估计精度，还开发了一种网格演化程序。此外还提出了低复杂度的数据辅助基于GAMP的网格演化SBL（D-GAMP-GESBL）算法。数值结果验证了所提方案的有效性并展示了其优于现有方法的优势。
英文摘要：
Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for next-generation integrated sensing and communication (ISAC) systems. However, it becomes challenging to improve spectral efficiency while simultaneously obtaining accurate channel and sensing-related parameters, particularly in doubly-dispersive channels with fractional delays and fractional Doppler shifts. To tackle this challenge, by formulating the channel estimation task as a multiple measurement vector (MMV) off-grid sparse recovery problem, we propose a data-aided grid-evolution sparse Bayesian learning (D-GESBL) scheme for channel estimation and sensing under a superimposed pilot framework. Specifically, we develop an efficient data-aided iterative receiver, in which reliably decoded data symbols are fed back as additional pseudo-pilot information to assist channel estimation and sensing. To mitigate off-grid mismatch and improve the overall estimation accuracy, we develop a grid evolution procedure that iteratively adjusts the virtual grids in the discrete affine Fourier (DAF) domain according to the estimated off-grid components. Furthermore, by integrating the generalized approximate message passing (GAMP) algorithm into the proposed SBL framework, we also develop a low-complexity data-aided GAMP-based grid-evolution SBL (D-GAMP-GESBL) algorithm. Finally, the numerical results validate the effectiveness of our proposed schemes and demonstrate their superiority over existing state-of-the-art methods.
