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效率提升方面已展现出显著优势，例如降低时延、提高数据速率及改善能效。然而，用户间的资源竞争会导致分配不均、时延增加与吞吐量下降。值得关注的是，速率分割多址技术已成为管理MEC系统干扰和优化资源分配的有效解决方案。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的无源波束成形、基站的有源波束成形、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的解码顺序，以最小化系统平均时延。由于所构建的问题具有非凸性且优化变量高度耦合，本文提出一种基于分层深度强化学习的算法，以同步优化问题中的连续变量与离散变量。此外，为更有效地提取信道特征，我们在所提算法的策略网络与评估网络中设计了结合卷积神经网络与密集连接卷积网络的新型网络架构。仿真结果表明，所提算法不仅具有优异的收敛性能，且在多类基准对比中均表现出优越性。