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Robust Energy-Efficient Downlink Resource Allocation in Heterogeneous Networks with Outage Probability Constraint

机译:具有中断概率约束的异构网络中的强大节能下行资源分配

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With the development of the fifth generation communication technology, improving energy efficiency (EE is defined as the ratio of the system throughput over the total power consumption) of wireless communication becomes a hot topic, which has attracted wide attention from industry and academia. Heterogeneous networks (HetNets) have been considered as a new promising technique for expanding network coverage and improving EE. Robust resource allocation is a huge challenge when uncertainty parameters are involved in this issue. The problem is more significant in HetNets since perfect channel state information is not available at femtocell base station's transmitters. In this paper, we study the downlink resource allocation in HetNets under outage probability constraint, and formulate the EE maximization problem as a nonlinear fractional programming problem. In order to solve the fractional programming problem, firstly, we transform the original problem into an equivalent optimization problem in a parametric subtractive form. Then based on the exponential distribution model under Rayleigh fading environment, the probability constraint is transformed into a deterministic constraint. Finally, we propose a two-loop iteration algorithm to find the optimal solution by using Dinkelbachs method and Lagrangian dual decomposition method. Simulation results demonstrate the convergence and the effectiveness of the proposed algorithm.
机译:随着第五代通信技术的发展,提高能源效率(EE被定义为系统吞吐量与总功耗的系统吞吐率)的无线通信成为一个热门话题,它引起了来自工业和学术界的广泛关注。异构网络(Hetnets)被认为是扩展网络覆盖和改善EE的新有希望的技术。当在此问题中涉及不确定性参数时,强大的资源分配是一个巨大的挑战。由于在Femtocell基站的发射器中不可用完美的频道状态信息,因此问题在Hetnets中更为显着。在本文中,我们研究了中断概率约束下的Hetnets中的下行资源分配,并将EE最大化问题标注为非线性分数规划问题。为了解决分数编程问题,首先,我们以参数减法形式将原始问题转换为等同的优化问题。然后基于瑞利衰落环境下的指数分布模型,将概率约束变为确定性约束。最后,我们提出了一种双循环迭代算法,通过使用Dinkelbachs方法和拉格朗日双分解方法来找到最佳解决方案。仿真结果证明了所提出的算法的收敛性和有效性。

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