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Lagrangian Dual Decomposition for Joint Resource Allocation Optimization Problem in OFDMA Downlink Networks

机译:OFDMA下行网络中联合资源分配优化问题的拉格朗日对偶分解

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摘要

This paper proposes an efficient method for joint power and subcarrier allocation in a multicell multiuser OFDMA downlink network. The joint optimization problem is formulated with the objective of maximizing the energy efficiency subject to the constraints on the quality of service in sum transmission rates for each cell and the total transmit power for the network. Due to intercell cochannel interferences and multiple variable coupling, the problem is intractable in its original form. To relax the difficulties in coordinating cochannel interferences, we introduce the tolerable interferences constraints for interference channels. To cope with the multiple variable coupling, we decompose the joint optimization problem into two iterative processes of user scheduling and a parametric convex optimization problem, where the energy efficiency is treated as the parameter and approached by bisection search. Then, by double dual decomposition, the parametric convex problem is transformed into Lagrangian dual problems at two levels of cells and subcarriers, and a decentralized solution is obtained in closed form. Based on the reformulations, an iterative subgradient algorithm is presented for approaching the joint optimization problem with acceptable complexity. Computer simulations are conducted to validate the proposed algorithm and examine the effects of various system parameters.
机译:本文提出了一种在多小区多用户OFDMA下行链路网络中联合功率和子载波分配的有效方法。制定联合优化问题的目的是,在每个小区的总传输速率和网络的总发射功率的服务质量约束下,最大化能效。由于小区间共信道干扰和多变量耦合,该问题以其原始形式难以解决。为了缓解协调同信道干扰的困难,我们为干扰信道引入了可容忍的干扰约束。为了解决多变量耦合问题,我们将联合优化问题分解为用户调度的两个迭代过程和参数化凸优化问题,其中将能量效率作为参数并通过对分搜索来解决。然后,通过双重对偶分解,将参数凸问题在两个级别的单元和子载波上转换为拉格朗日对偶问题,并以封闭形式获得分散解。在此基础上,提出了一种迭代次梯度算法,以可接受的复杂度解决联合优化问题。进行计算机仿真以验证所提出的算法并检查各种系统参数的影响。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第14期|6875090.1-6875090.10|共10页
  • 作者单位

    Huaqiao Univ, Xiamen Key Lab Mobile Multimedia Commun, 668 Jimei Ave, Xiamen 361021, Peoples R China;

    Huaqiao Univ, Xiamen Key Lab Mobile Multimedia Commun, 668 Jimei Ave, Xiamen 361021, Peoples R China;

    Huaqiao Univ, Xiamen Key Lab Mobile Multimedia Commun, 668 Jimei Ave, Xiamen 361021, Peoples R China;

    Huaqiao Univ, Xiamen Key Lab Mobile Multimedia Commun, 668 Jimei Ave, Xiamen 361021, Peoples R China;

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