Digital Twin Empowered Task Offloading for RIS-Assisted Edge Computing Networks
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摘要: 针对新兴的计算密集型应用对移动用户高计算性能需求问题，该文提出一种数字孪生(DT)结合智能反射面(RIS)辅助的移动边缘计算(MEC)任务卸载方案。首先，在满足用户传输功率、用户和资源设备能耗、计算资源限制条件下，通过联合优化用户卸载决策、用户传输功率、RIS 相移、波束成形矢量、计算资源分配，建立一个系统能耗最小化问题；其次，将该非凸组合优化问题分解为3个子问题，使用深度双Q网络(DDQN)方法确定用户卸载策略；然后对每个训练时间步进行一次求解，基于交替迭代方法得到问题的优化解。仿真结果表明，基于DDQN的算法训练速度较快，有效降低了系统总能耗。Abstract: In order to meet the high computing demands caused by emerging compute-intensive applications in Mobile Edge Computing (MEC), this paper proposes a Digital Twin (DT)-empowered task offloading scheme where Reconfigurable Intelligent Surface (RIS) is used to enhance the communication links and extend the coverage. Firstly, the joint optimization of user offloading strategy, RIS phase-shift vector, beamforming vector, transmit power of users and computation capacity allocation are investigated with the aim of minimizing the total energy consumption of users and resource devices under the constraints of communication and computing resources. Then, the formulated non-convex combinational optimization problem is decomposed into three sub-problems, including RIS phase-shift design, binary optimization of transmit power, and computing resource allocation. In addition, the Double Deep Q Network (DDQN) approach is invoked to determine the offloading decisions and an alternating iteration optimization algorithm is designed to achieve the optimal solution. Simulation results show that the DDQN-based algorithm is able to train quickly and reduce effectively the total energy consumption of the system.
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表 1 基于DDQN能耗最小化算法(算法1)
输入：最大回合数E，学习率$\beta $，折扣回报$\gamma $，用户数K，二分法精度$\varepsilon $。 步骤1 初始化主网络参数$\omega $，目标网络参数${\omega ^ - }$，经验数组，用户、基站和资源设备位置，用户任务信息$M_k^t$ 步骤2 for $t = 1:{E}$ 更新$t$时刻用户位置和任务信息； for $ {\text{step}} = {\text{1}}:K $ 根据当前状态$s\left[ t \right]$和策略${\pi _\omega }$选择动作$a\left[ t \right]$； 根据3.2,3.3节讨论和动作$a\left[ t \right]$，使用交替迭代法和二分法求解$ {\theta _{k,n}},{{\mathbf{w}}_k},{p_k},f_k^l,f_{k,q}^r,f_k^e $； 根据奖励函数计算$r\left( t \right)$，观察下一个状态$s\left[ {t + 1} \right]$，并存储$\left\langle {s\left[ t \right],a\left[ t \right],r\left[ t \right],s\left[ {t + 1} \right]} \right\rangle $到经验数组； 从经验回放数组随机取出一组经验，根据式(7)计算损失函数，并更新当前主网络参数$\omega $； 每隔$Z$步更新目标网络参数${\omega ^ - }$； 步骤3 输出任务卸载策略网络参数$\omega $。 
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表 2 DDQN训练参数
参数 值 参数 值 隐藏层数量$L$ 3 惩罚值$ C $ 100 折扣回报$\gamma $ 0.9 开销值$\vartheta $ (0,1,5) 最大回合数E 100 k 二分法精度$\varepsilon $ 10–6 学习率$\beta $ 5×10–3 经验回放数组大小 215 目标网络更新频率$Z$ 320 贪婪策略比例 0.1
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