  This paper investigates the uplink cascaded channel estimation for
intelligent-reflecting-surface (IRS)-assisted multi-user
multiple-input-single-output systems. We focus on a sub-6 GHz scenario where
the channel propagation is not sparse and the number of IRS elements can be
larger than the number of BS antennas. A novel channel estimation protocol
without the need of on-off amplitude control to avoid the reflection power loss
is proposed. In addition, the pilot overhead is substantially reduced by
exploiting the common-link structure to decompose the cascaded channel
coefficients by the multiplication of the common-link variables and the
user-specific variables. However, these two types of variables are highly
coupled, which makes them difficult to estimate. To address this issue, we
formulate an optimization-based joint channel estimation problem, which only
utilizes the covariance of the cascaded channel. Then, we design a
low-complexity alternating optimization algorithm with efficient initialization
for the non-convex optimization problem, which achieves a local optimum
solution. To further enhance the estimation accuracy, we propose a new
formulation to optimize the training phase shifting configuration for the
proposed protocol, and then solve it using the successive convex approximation
algorithm. Comprehensive simulations verify that the proposed algorithm has
supreme performance compared to various state-of-the-art baseline schemes.
