  Intelligent reflecting surface (IRS) is a novel burgeoning concept, which
possesses advantages in enhancing wireless communication and user localization,
while maintaining low hardware cost and energy consumption. Herein, we
establish an IRS-aided mmWave-MIMO based joint localization and communication
system (IMM-JLCS), and probe into its performance evaluation and optimization
design. Specifically, first, we provide the signal, channel and estimation
error models, and contrive the working process of the IMM-JLCS in detail. Then,
by configuring appropriate IRS phase shifts, we derive the closed-form
expressions of the Cramer-Rao Lower Bound (CRLB) of the position/orientation
estimation errors and the effective achievable data rate (EADR), with respect
to the time allocation ratio of the beam alignment and localization stage
(BALS). Subsequently, we investigate the trade-off between the two performance
metrics, for which we propose a joint optimization algorithm. Finally, we carry
out simulations and comparisons to view the trade-off and validate the
effectiveness of the proposed algorithm, in the presence of distinct levels of
estimation uncertainty and user mobility. Our results demonstrate that the
proposed algorithm can find the joint optimal solution for the
position/orientation estimation accuracy and EADR, with its optimization
performance being robust to slight localization or channel estimation errors
and user mobility.
