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
摘要：
本文研究了智能反射面辅助OFDM系统的资源分配问题。通过联合优化子载波分配、基站发射波束形成和IRS相移，建立了系统和速率最大化框架。考虑到优化变量的连续和离散混合作用空间特性，提出了一种结合多深度Q网络（MDQN）和深度确定性策略梯度（DDPG）的高效资源分配算法.在算法中，MDQN被用来解决大的离散动作空间的问题，而DDPG被引入来解决连续动作分配。与传统的方法相比，我们提出的MDQN-DDPG算法的优势，通过学习环境的持续行为改进。仿真结果表明，与基准方案相比，我们的设计在系统和速率方面具有优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
