Energy-efficient Optimization Algorithm in Multi-tag Wireless-powered Backscatter Communication Networks
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摘要: 为了提高物联网(IoT)节点的运行周期和能量利用率，该文提出一种多标签无线供电反向散射通信网络能效最大化资源分配算法。考虑传输速率约束、能量收集约束以及发射功率约束，建立了基于系统能效最大化的资源分配模型。利用Dinkelbach理论、2次变换以及变量替换法，将原分式非凸问题转化为可求解的凸优化问题。通过拉格朗日对偶理论求得优化问题的全局最优解。仿真结果表明，该算法具有较好的收敛性和能效。Abstract: To improve the operation cycle and energy utilization of Internet of Things (IoT) nodes, an energy-efficient maximization resource allocation algorithm is proposed for a multi-tag wireless-powered backscatter communication network. Specifically, a resource allocation model is developed to maximize the system energy efficiency under the transmission rate constraints, energy harvesting constraints, and transmit power constraints. The original fractional non-convex problem is transformed into a solvable convex optimization problem by using Dinkelbach's theory, a quadratic transformation method, and the variable substitution method. The globally optimal solutions of the considered problem are obtained by using Lagrange dual theory. Simulation results show that the proposed algorithm has better convergence and energy efficiency.
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表 1 基于迭代的能效最大化资源分配算法
初始化系统参数$ K,{h_k},{g_k},h,{\sigma ^2},T{\text{,}}{P_{{\text{max}}}},R_k^{{\text{R}},\min },E_k^{\text{C}},E_k^{{\text{C,min}}} $；
给定初始化能效${\eta _{{\text{EE}}}}$，外层迭代次数$t = 0$；定义算法收敛精度$\varpi $，外层最大迭代次数为${T_{\max }}$； (1) while${\text{|} }\dfrac{ {R(t)} }{ { {E^{ {\text{total} } } }(t)} } - {\eta _{ {\text{EE} } } }{\text{(} }t - 1{\text{)|} } > \varpi$或$t \leq {T_{\max }}$, do (2) 初始化迭代步长和拉格朗日乘子，内层最大迭代次数$ {L_{\max }} $，
初始化内层迭代次数$ l{\text{ = }}0 $；(3) while 所有拉格朗日乘子的收敛精度大于$\varpi $，do (4) for $ k{\text{ = 1:}}K $ (5) 根据式(18)计算最优功率$P_k^*$； (6) 根据式(19)计算$\beta _k^*$； (7) 计算反射系数$\alpha _k^*$； (8) 根据式(20)—式(23)更新拉格朗日乘子
$ {\mu _k},{\omega _k},{\varepsilon _k},\nu $；(9) end for (10) 更新$l = l + 1$； (11) until 收敛或$l{\text{ = }}{L_{\max }}$； (12) end while
(13) 更新$ {\eta _{{\text{EE}}}}{\text{(}}t) = \dfrac{{\displaystyle\sum\limits_{k = 1}^K {\tau _k^{}} R(t - 1)}}{{{E^{{\text{total}}}}(t - 1)}} $和$ t = t + 1 $；(14) end while (15) 输出所需优化变量$P_k^*,\beta _k^*,\alpha _k^*$。 
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