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(IRS)辅助移动边缘计算(MEC)系统在降低时延、提升数据速率和改善能效等方面展现出显著成效。然而，用户间的资源竞争将导致分配不均、时延增加及吞吐量下降。幸运的是，速率分拆多址接入(RSMA)技术已成为管理MEC系统干扰和优化资源分配的有效方案。本文研究了一种采用RSMA的IRS辅助MEC系统，旨在通过联合优化IRS无源波束赋形、基站有源波束赋形、任务卸载分配、用户发射功率、公共与私有信息分配比例及RSMA解码顺序，从新颖的上行传输视角最小化平均时延。由于所建问题具有非凸性且优化变量高度耦合，我们提出了一种基于分层深度强化学习的算法来同时优化问题的连续与离散变量。此外，为更好地提取信道特征，我们在所提算法的策略网络与评估网络中设计了一种新型网络架构，该架构融合了卷积神经网络与密集连接卷积网络进行特征提取。仿真结果表明，所提算法不仅具有优异的收敛性能，且优于多种基准方案。