翻译一下:1)在移动设备与边缘服务器处于直连通信状态下,基于非正交多址技术通信方式,研究了利用深度强化学习算法进行通信资源分配策略。为了满足移动设备卸载计算密集型任务的要求,本策略通过联合优化多用户共享子信道的匹配和用户传输功率的分配,构建了由单基站和多子信道组成的移动边缘计算通信架构,确立了系统和速率最大化的目标。由于提出的目标问题具有非凸性,并且在线通信环境无法预测,因此设计了禁忌深度Q网络-深度确定性策略梯度联合算法(Tabu Tag Deep Q Network- Deep Deterministic Policy Gradient,TTDQN-DDPG)来求解目标模型。仿真结果表明,相比较传统通信技术和基于普通的深度强化学习通信算法,本文设计的 TTDQN-DDPG算法能够明显提高移动边缘计算通信的系统和速率。
时间: 2023-05-24 09:06:32 浏览: 90
1) In the direct communication between mobile devices and edge servers based on non-orthogonal multiple access technology, a communication resource allocation strategy utilizing deep reinforcement learning algorithms was studied. In order to meet the demands of offloading computation-intensive tasks from mobile devices, this strategy constructed a mobile edge computing communication architecture consisting of a single base station and multiple sub-channels, by jointly optimizing the matching of multiple user-shared sub-channels and the allocation of user transmission power, and established the objectives of maximizing the system and rate. Due to the non-convexity of the proposed objective problem and the unpredictable online communication environment, a Tabu Tag Deep Q Network- Deep Deterministic Policy Gradient (TTDQN-DDPG) joint algorithm was developed to solve the objective model. Simulation results indicate that the TTDQN-DDPG algorithm designed in this paper can significantly improve the system and rate of mobile edge computing communication compared to traditional communication technologies and ordinary deep reinforcement learning communication algorithms.