  In this work, we address the challenge of accurately obtaining channel state
information at the transmitter (CSIT) for frequency division duplexing (FDD)
multiple input multiple output systems. Although CSIT is vital for maximizing
spatial multiplexing gains, traditional CSIT estimation methods often suffer
from impracticality due to the substantial training and feedback overhead they
require. To address this challenge, we leverage two sources of prior
information simultaneously: the presence of limited local scatterers at the
base station (BS) and the time-varying characteristics of the channel. The
former results in a redundant angular sparsity of users' channels exceeding the
spatial dimension (i.e., the number of BS antennas), while the latter provides
a prior non-uniform distribution in the angular domain. We propose a weighted
optimization framework that simultaneously reflects both of these features. The
optimal weights are then obtained by minimizing the expected recovery error of
the optimization problem. This establishes an analytical closed-form
relationship between the optimal weights and the angular domain
characteristics. Numerical experiments verify the effectiveness of our proposed
approach in reducing the recovery error and consequently resulting in decreased
training and feedback overhead.
