  Rate-Splitting Multiple Access (RSMA) has recently found favour in the
multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of
Channel State Information at the Transmitter (CSIT), while in achieving high
spectral efficiency and providing security guarantees. These benefits are
particularly important in high-velocity vehicular platoons since their high
Doppler affects the estimation accuracy of the CSIT. To tackle this challenge,
we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly
considers platoon control and FEderated Edge Learning (FEEL) in the downlink.
Specifically, the proposed framework is designed for transmitting the unicast
control messages within the IoV platoon, as well as for privacy-preserving
FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given
this sophisticated framework, a multi-objective optimization problem is
formulated to minimize both the latency of the FEEL downlink and the deviation
of the vehicles within the platoon. To efficiently solve this problem, a Block
Coordinate Descent (BCD) framework is developed for decoupling the main
multi-objective problem into two sub-problems. Then, for solving these
non-convex sub-problems, a Successive Convex Approximation (SCA) and Model
Predictive Control (MPC) method is developed for solving the FEEL-based
downlink problem and platoon control problem, respectively. Our simulation
results show that the proposed RSMA-based IoV system outperforms the
conventional systems.
