Partial Computation Offloading for Double-RIS Assisted Multi-User Mobile Edge Computing Networks
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摘要: 针对移动边缘计算(MEC)任务卸载性能易受障碍物阻挡影响的问题，该文提出一种双智能超表面(RIS) 赋能的移动边缘计算任务部分卸载框架。首先，分析两个RIS之间的反射对链路增益的影响。其次，联合考虑终端用户的发射功率、终端用户的卸载速率、任务卸载量、卸载时间的分配以及RIS相移约束，旨在建立一个能耗最小化优化问题。最后，采用交替迭代算法，将原非凸问题分解为两个子问题，并利用Dinkelbach方法和最优性条件进行求解。仿真结果验证了所提算法的快速收敛特性以及在降低系统能耗方面的有效性。Abstract: In order to compensate the performance loss caused by obstacle blocking in Mobile Edge Computing (MEC) system, a partial task offloading framework supported by Reconfigurable Intelligent Surface (RIS) is proposed. Firstly, the influence of the reflection between double-RIS on channel gain is analyzed. Then, a non-convex and multivariable coupling problem for minimization of total energy consumption of all users is formulated by the joint design of the transmit power of users, the offloading rate of users, the amount of offloading task of users, the time slot and the phase shift of RISs. To solve this problem, the alternating optimization technique is invoked to decouple the original non-convex problem into two subproblems which are solved by leveraging the Dinkelbach method and optimally conditions. Numerical results demonstrate that the proposed algorithm converges swiftly and reduces effectively the system energy consumption.
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表 1 交替优化算法(算法1)
输入：初始化$\left( {{\mathbf{v}}_1^k,{\mathbf{v}}_2^k,{{\mathbf{w}}_k},{p_k},{l_k},{t_k}} \right)$ 步骤1：for $i = 1:{I_0}$ 根据式(11)计算${\mathbf{v}}_2^k$； 根据式(13)计算${\mathbf{v}}_1^k$； 根据式(14)计算${{\mathbf{w}}_k}$； 步骤2：for $i = 1:{I_1}$ 根据式(17)计算${p_k}$； 根据定理2计算${l_k}$； 更新${\eta ^{(i)}} = {\log _2}\left( {1 + p_k^{(i)}{a_k}} \right)/p_k^{(i)}$； 更新$t_k^{(i)} = ({L_k} - l_k^{(i)})/R_k^{(i)}$； 步骤3：输出$\left( {{\mathbf{v}}_1^k,{\mathbf{v}}_2^k,{{\mathbf{w}}_k},{p_k},{l_k},{t_k}} \right)$。 
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