  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 $133\%$.
