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Rate-Splitting Multiple Access (RSMA) has recently found favour in themulti-antenna-aided wireless downlink, as a benefit of relaxing the accuracy ofChannel State Information at the Transmitter (CSIT), while in achieving highspectral efficiency and providing security guarantees. These benefits areparticularly important in high-velocity vehicular platoons since their highDoppler affects the estimation accuracy of the CSIT. To tackle this challenge,we propose an RSMA-based Internet of Vehicles (IoV) solution that jointlyconsiders platoon control and FEderated Edge Learning (FEEL) in the downlink.Specifically, the proposed framework is designed for transmitting the unicastcontrol messages within the IoV platoon, as well as for privacy-preservingFEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Giventhis sophisticated framework, a multi-objective optimization problem isformulated to minimize both the latency of the FEEL downlink and the deviationof the vehicles within the platoon. To efficiently solve this problem, a BlockCoordinate Descent (BCD) framework is developed for decoupling the mainmulti-objective problem into two sub-problems. Then, for solving thesenon-convex sub-problems, a Successive Convex Approximation (SCA) and ModelPredictive Control (MPC) method is developed for solving the FEEL-baseddownlink problem and platoon control problem, respectively. Our simulationresults show that the proposed RSMA-based IoV system outperforms theconventional systems.

Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization