非正交多址认知无线电网络功率分配算法
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国家自然科学基金(61671096);重庆市基础科学与前沿技术研究重点项目(cstc2017jcyjBX0005);重庆市“科技创新领军人才支持划”(CSTCCXLJRC201710);重庆市教委科学技术研究项目(KJQN201800642);2016年博士研究生高端人才培养项目(BYJS2016009)


Power allocation algorithm in NOMA-based cognitive radio networks
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The National Natural Science Foundation of China (61671096); The Chongqing Research Program of Basic Science and Frontier Technology (cstc2017jcyjBX0005); The Chongqing Science and Technology Innovation Leading Talent Support Program (CSTCCXLJRC201710); The Science and Technology Research Program of Chongqing Education Commission (KJQN201800642); The Doctoral Student Training Program (BYJS2016009)

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    摘要:

    非正交多址和认知无线电技术能有效提高频谱效率,是新一代移动通信系统的关键技术。针对功率域非正交多址认知无线电网络的能效优化问题,建立了满足次用户最小系统吞吐量和主用户最大干扰的次用户功率分配模型,将子信道吞吐量公式进行分解,得到子信道功率分配系数和子信道功率消耗率2个子问题。针对第1个问题,采取凸差(difference of convex,DC)规划算法将目标函数等效为2个凸函数差形式,并应用一阶泰勒展开式进行连续近似,将非凸问题转换为凸优化问题,从而得到子信道复用次用户最优功率分配系数;针对第2个问题,采用Dinkelbach算法和次梯度算法,利用拉格朗日函数,得到最优子信道功率消耗率。仿真结果表明,所提功率分配算法收敛速度快,时间复杂度低,其平均系统能效性能远优于分数功率分配算法。

    Abstract:

    Non-orthogonal multiple access (NOMA) and cognitive radio technology are considered to be the two most promising techniques of the new generation mobile communication system for improving spectral efficiency. In this paper, to optimize the efficiency of the power domain NOMA cognitive radio network, a sub-user power allocation model is established to meet the minimum system throughput and the maximum interference of the main user. Then, the sub-channel throughput formula is decomposed to obtain the sub-channel power allocation coefficient and sub-channel power consumption rate. For the first problem, the objective function is equivalent to two convex function difference forms by using the difference of convex (DC) programming algorithm, and the non-convex problem is transformed into the convex optimization problem by using the first-order Taylor expansion continuous approximation, to obtain the optimal power distribution coefficient of the sub-user of the subchannel multiplexing. To solve the second problem, Dinkelbach, subgradient algorithm and Lagrange function are used to obtain the optimal subchannel power consumption rate. The simulation results show that the proposed power allocation algorithm has fast convergence speed and low time complexity, and its average system energy efficiency performance is far better than the fractional power allocation algorithm.

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王江涛,周梦园,陈东,蔡丽娟.非正交多址认知无线电网络功率分配算法[J].重庆邮电大学学报(自然科学版),2020,32(6):945-953.

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  • 收稿日期:2019-01-24
  • 最后修改日期:2020-04-28
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  • 在线发布日期: 2020-12-23

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