论文标题
IRS辅助OFDM通信的智能资源分配：一种混合MDQN-DDPG方法
Intelligent Resource Allocations for IRS-Assisted OFDM Communications: A Hybrid MDQN-DDPG Approach
论文作者
论文摘要
在本文中，我们研究了智能反射表面（IRS）辅助系统系统的资源分配问题。系统总和速率最大化框架是通过共同优化子载波分配，基站传输波束形成和IRS相移来制定的。考虑到优化变量的连续和离散的混合动作空间特征，我们提出了一种有效的资源分配算法，该算法结合了多个深Q网络（MDQN）和深层确定性的政策梯度（DDPG）来处理此问题。在我们的算法中，使用MDQN来解决大型离散动作空间的问题，而引入了DDPG来解决连续的动作分配。与传统方法相比，我们提出的基于MDQN-DDPG的算法具有通过从环境中学习的持续行为改进的优势。模拟结果表明，与基准方案相比，根据系统总和速率，我们的设计表现出色。
In this paper, we study the resource allocation problem for an intelligent reflecting surface (IRS)-assisted OFDM system. The system sum rate maximization framework is formulated by jointly optimizing subcarrier allocation, base station transmit beamforming and IRS phase shift. Considering the continuous and discrete hybrid action space characteristics of the optimization variables, we propose an efficient resource allocation algorithm combining multiple deep Q networks (MDQN) and deep deterministic policy-gradient (DDPG) to deal with this issue. In our algorithm, MDQN are employed to solve the problem of large discrete action space, while DDPG is introduced to tackle the continuous action allocation. Compared with the traditional approaches, our proposed MDQN-DDPG based algorithm has the advantage of continuous behavior improvement through learning from the environment. Simulation results demonstrate superior performance of our design in terms of system sum rate compared with the benchmark schemes.
