Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, we develop a structure-aware sparse Bayesian learning framework, where decoded data symbols serve as side information to enhance sensing performance and increase SE. To meet the low-latency requirements of emerging ISAC applications, we further propose a low-complexity simultaneous communication and localization algorithm for multiple users. This algorithm employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization while attaining up to $96\%$ of the SE of conventional communications without sensing capabilities. Moreover, compared to other typical ISAC schemes, the proposed superimposed symbol scheme can provide an effective throughput improvement over $133\%$.

翻译：可重构智能表面（RIS）在提升通感一体化（ISAC）系统性能方面潜力巨大，尤其在基站与用户间视距路径受阻的场景中。然而，RIS辅助ISAC上行传输中，为感知能力而付出的导频开销负担可能会显著降低频谱效率（SE）。本文通过提出叠加符号方案来突破这一瓶颈，该方案将感知导频与数据符号叠加在相同的时频资源上。具体而言，我们开发了一种结构感知的稀疏贝叶斯学习框架，其中解码后的数据符号作为辅助信息，用于增强感知性能并提升SE。为满足新兴ISAC应用的低延迟需求，我们进一步提出一种用于多用户的低复杂度协同通信与定位算法。该算法在贝叶斯学习框架中采用酉近似消息传递进行初始角度估计，随后通过降维矩阵计算实现迭代优化。此外，稀疏码多址接入技术被融入此迭代框架以实现精确的数据检测，同时辅助定位。数值结果表明，所提出的基于叠加符号的方案借助该算法，在实现厘米级定位精度的同时，可达传统无感知能力通信系统SE的96%。此外，与其他典型ISAC方案相比，所提叠加符号方案的有效吞吐量提升超过133%。