This paper presents a novel and effective deep reinforcement learning
(DRL)-based approach to addressing joint resource management (JRM) in a
practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where
hardware sensitivity and imperfect successive interference cancellation (SIC)
are considered. We first formulate the JRM problem to maximize the weighted-sum
system throughput. Then, the JRM problem is decoupled into two iterative
subtasks: subcarrier assignment (SA, including user grouping) and power
allocation (PA). Each subtask is a sequential decision process. Invoking a deep
deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM)
approach jointly performs the two subtasks, where the optimization objective
and constraints of the subtasks are addressed by a new joint reward and
internal reward mechanism. A multi-agent structure and a convolutional neural
network are adopted to reduce the complexity of the PA subtask. We also tailor
the neural network structure for the stability and convergence of DRL-JRM.
Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior
to existing alternatives in terms of system throughput and resistance to
interference, especially in the presence of many users and strong inter-cell
interference. DRL-JRM can flexibly meet individual service requirements of
users.

本文提出了基于深度强化学习的联合资源管理方法来应对多载波非正交多址系统中的硬件敏感性和不完美连续干扰消除，包括对子载波赋值和功率分配两个迭代子任务的决策过程。经过广泛实验验证，该方法在系统吞吐量和抗干扰能力方面较现有替代方案更加优越，并可以灵活满足用户的个性化服务要求。