  The emerging immersive and autonomous services have posed stringent
requirements on both communications and localization. By considering the great
potential of reconfigurable intelligent surface (RIS), this paper focuses on
the joint channel estimation and localization for RIS-aided wireless systems.
As opposed to existing works that treat channel estimation and localization
independently, this paper exploits the intrinsic coupling and nonlinear
relationships between the channel parameters and user location for enhancement
of both localization and channel reconstruction. By noticing the non-convex,
nonlinear objective function and the sparser angle pattern, a variational
Bayesian learning-based framework is developed to jointly estimate the channel
parameters and user location through leveraging an effective approximation of
the posterior distribution. The proposed framework is capable of unifying
near-field and far-field scenarios owing to exploitation of sparsity of the
angular domain. Since the joint channel and location estimation problem has a
closed-form solution in each iteration, our proposed iterative algorithm
performs better than the conventional particle swarm optimization (PSO) and
maximum likelihood (ML) based ones in terms of computational complexity.
Simulations demonstrate that the proposed algorithm almost reaches the Bayesian
Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by
comparing to the PSO and the ML algorithms.
