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Energy Efficiency of Distributed Antenna Systems with D2D Communications under Imperfect CSI

机译:CSI不完善下具有D2D通信的分布式天线系统的能效

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In this paper, we investigate the distributed antenna systems (DAS) based on device to device (DAS-DID) communications under the imperfect channel state information (CSI). Our aim is to maximize the energy efficiency (EE) of the D2D users equipment (DUE) under the constraints of the maximum transmission power of D2D pairs and the quality of service (QoS) requirements of the cellular user equipment (CUE). The worst-case design is considered so that the QoS of the CUE can be guaranteed for every realization of the CSI error in the ellipsoid region. The EE objective function of the optimization problem is non-convex and non-linear, and thus this problem cannot be solved by the traditional optimization methods. To solve this problem, first we transform it to an EE maximization problem without uncertain parameters by exploiting the Markov and Cauchy-Schwartz inequality. Then using the fractional programming theory and difference of convex functions optimization method, the robust EE maximization algorithms based on the hard and soft protection method are developed to maximize the system's EE performance, respectively. However, these two algorithms are designed at the cost of the reduced EE of the DUE. Therefore, in order to further improve the EE performance and make a trade-off between the EE performance and the robustness, the iterative update algorithms for the total power constraint and average interference constraint are developed to maximize the system's EE performance, respectively. Simulation results demonstrate the effectiveness of the four proposed EE algorithms and illustrate the trade-off between the EE performance and robustness for the iterative update algorithms.
机译:在本文中,我们研究了在不完美信道状态信息(CSI)下基于设备到设备(DAS-DID)通信的分布式天线系统(DAS)。我们的目标是在D2D对的最大传输功率和蜂窝用户设备(CUE)的服务质量(QoS)要求的约束下,最大化D2D用户设备(DUE)的能效(EE)。考虑了最坏情况的设计,从而可以确保椭圆体区域中CSI错误的每次实现都能保证CUE的QoS。优化问题的EE目标函数是非凸和非线性的,因此该问题无法通过传统的优化方法来解决。为了解决这个问题,首先我们通过利用马尔可夫和柯西-舒瓦兹不等式将其转换为没有不确定参数的EE最大化问题。然后利用分数规划理论和凸函数优化方法的差异,分别开发了基于硬保护和软保护方法的鲁棒EE最大化算法,以最大化系统的EE性能。但是,这两种算法的设计是以降低DUE的EE为代价的。因此,为了进一步提高EE性能并在EE性能和鲁棒性之间进行权衡,开发了总功率约束和平均干扰约束的迭代更新算法,分别使系统的EE性能最大化。仿真结果证明了四种提出的EE算法的有效性,并说明了EE性能与迭代更新算法的鲁棒性之间的权衡。

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