Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks.

翻译：智能反射面辅助的移动边缘计算系统在降低时延、提升数据速率和改善能效方面展现出显著优势。然而，用户间的资源竞争会导致分配不均、时延增加和吞吐量下降。速率分割多址技术为管理MEC系统干扰和优化资源分配提供了有效解决方案。本文研究基于速率分割多址的IRS辅助MEC系统，从新型上行传输视角出发，联合优化IRS无源波束成形、基站有源波束成形、任务卸载分配、用户发射功率、公共与私有信息分配比例以及速率分割多址解码顺序，以最小化系统平均时延。由于该问题具有非凸性且优化变量高度耦合，本文提出基于分层深度强化学习的算法，实现对连续与离散变量的联合优化。为进一步提取信道特征，在算法策略网络与评估网络中设计了结合卷积神经网络与密集连接卷积网络的新型特征提取架构。仿真结果表明，所提算法不仅收敛性能优异，且在多类基准对比中均表现出优越性能。