Rate Splitting Enabled Uplink-Downlink Decoupled Association in UAV-assisted Cellular Networks

In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in unmanned aerial vehicles (UAVs)-assisted cellular networks in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicast can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we develop a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation at the receiver. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL. Since the resultant problem is non-convex with complex states, we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. Simulation results show the superiority of the proposed decoupled association policy with RSMA and the MADRL learning algorithm.