Energy Efficiency Optimization Algorithm of Cooperative Non-Orthogonal Multiple Access joint Simultaneous Wireless Information and Power Transfer Based on Successive Convex Approximation
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摘要: 在传统的非正交多址(NOMA)系统中，通常将更多的功率分配给边缘用户以此来保证其通信质量，系统公平性以牺牲系统容量为代价。基于协作通信的NOMA系统虽可解决上述问题，但在协作阶段中心用户需承担中继的作用，这种方式必将给中心用户带来一定的负担。为了兼顾系统容量和公平性，该文提出一种基于协作通信和无线携能通信(SWIPT)的新型资源分配方案，该方案在满足边缘用户通信质量情况下，使用能量收集设备完成能量收集，通过连续凸逼近(SCA)求解目标问题最大化系统能效。仿真结果表明，与传统NOMA和协作式非正交多址接入系统(CNOMA)相比，CNOMA-SWIPT系统的能量效率得到了较大的提高，在基站最大发射功率为30 dBm时相比NOMA系统能达到60.8%的增益，相比CNOMA系统能达到比CNOMA系统高出约11.5%的增益，更符合绿色通信的发展理念。Abstract: In a traditional Non-Orthogonal Multiple Access (NOMA) system, more power is usually allocated to edge users to ensure its communication quality. However, the fairness of the system comes at the expense of system capacity. Introducing collaborative communication into the NOMA system, the central user also needs to assume the role of relay in the collaboration phase. This method will inevitably bring a certain burden to the central user. In order to balance system capacity and fairness, a new resource allocation scheme based on cooperative communication and Simultaneous Wireless Information and Power Transfer (SWIPT) is proposed. Energy harvesting equipment is used for energy harvesting, and maximizes the energy efficiency of the system by solving the target problem through Successive Convex Approximation (SCA). Compared with the traditional NOMA and Cooperative NOMA, the energy efficiency of the CNOMA-SWIPT system is greatly improved. When the maximum transmit power of the base station is 30 dBm, CNOMA-SWIPT can achieve a gain of 60.8% compared to the NOMA system and can achieve a gain of about 11.5% higher than that of the CNOMA system, which is more in line with the development concept of green communication.
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算法1 Dinkelbach分数规划 初始化：迭代次数n=0，准许误差$\epsilon={10}^{-4}$, $ {\lambda }^{n}=0 $ 若F($ \lambda $)=$ {R}_{\mathrm{s}\mathrm{u}\mathrm{m}} $–${\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m} }\ge \epsilon$，则执行： 用$ {\lambda }^{n} $求解max ($ {R}_{\mathrm{s}\mathrm{u}\mathrm{m}} $–$ {\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m}} $) ； 更新$ {R}_{\mathrm{s}\mathrm{u}\mathrm{m}} $–$ {\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m}} $, $ {\lambda }^{n} $, n=n+1; 否则：$ {{\eta }_{\mathrm{E}\mathrm{E}}}^{*}={\lambda }^{n} $ 
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算法2 基于SCA的P2求解算法 初始化：迭代次数j=1,${a}_{i} ^{j} {,b}_{i} ^{j}$=0(i=1,2,3)，$\varDelta $=1，准许误差
$ \epsilon={10}^{-4} $若$\varDelta\ge \epsilon$，则执行： 通过CVX工具箱求解P2，得到${q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j}$ 更新${z}_{i} ^{j} {,b}_{i} ^{j}$，${a}_{i} ^{j} {,b}_{i} ^{j}$ 更新：$\varDelta=|{F\left(\lambda \right)}^{j}-{F\left(\lambda \right)}^{j-1}|$； j=j+1；
否则： 输出$\lambda =\dfrac{ {R}_{\mathrm{s}\mathrm{u}\mathrm{m} }({ {q}_{1} }^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}{ {P}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}$
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算法3 基于SCA的CNOMA-SWIPT系统能效优化算法 初始化：迭代次数n=0，准许误差$ \epsilon={10}^{-4} $, $ {\lambda }^{n}=0 $ 若F($ {\lambda }^{n} $)$ \ge \epsilon $，则执行： 设j=0, ${a}_{i}^{j} {,b}_{i} ^{j}$=0(i=1,2,3)，$\varDelta=1 $； 若$\varDelta\ge \epsilon$，则执行： 通过CVX工具箱求解P2，得到${q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j}$； 更新${z}_{i} ^{j} {,b}_{i} ^{j}$, ${a}_{i} ^{j} {,b}_{i} ^{j}$, $\varDelta=|{F\left(\lambda \right)}^{j}-{F\left(\lambda \right)}^{j-1}|$； j=j +1； 直到$\varDelta < \epsilon$，执行：
F($ {\lambda }^{n} $)=${R}_{\mathrm{s}\mathrm{u}\mathrm{m} }\left( {q}_{1} ^{j}, {q}_{2} ^{j} {q}_{3} ^{j},{ {q}_{4} }^{j}, {q}_{5} ^{j}\right)-{\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m} }\left( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j}\right); {\lambda }^{n}=\dfrac{ {R}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}{ {P}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}$n=n+1; 若F($ {\lambda }^{n} $)<$ \epsilon $： 输出$\lambda =\dfrac{ {R}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}{ {P}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}$
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