Aerial networks have been widely considered a crucial component for ubiquitous coverage in the next-generation mobile networks. In this scenario, mobile edge computing (MEC) and rate splitting multiple access (RSMA) are potential technologies, which are enabled at aerial platforms for computation and communication enhancements, respectively. Motivated from this vision, we proposed a high altitude platform-mounted MEC (HAMEC) system in such an RSMA environment, where aerial users (e.g., unmanned

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... aerial vehicles) can efficiently offload their tasks to the HAMEC for external computing acquisition. To this end, a joint configuration of key parameters in HAMEC and RSMA (referred to as HAMEC-RSMA) such as offloading decision, splitting ratio, transmit power, and decoding order was optimally designed for a processing cost minimization in terms of response latency and energy consumption. Subsequently, the optimization problem was transformed into a reinforcement learning model, which is solvable using the deep deterministic policy gradient (DDPG) method. To improve the training exploration of the algorithm, we employed parameter noises to the DDPG algorithm to enhance training performance. Simulation results demonstrated the efficiency of the HAMEC-RSMA system with superior performances compared to benchmark schemes. INDEX TERMS 6G, rate splitting multiple access, edge computing, unmanned aerial vehicle, deep reinforcement learning.