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首页> 外文期刊>Journal of neurosurgical sciences >Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks
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Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

机译:可再生能源通电异质蜂窝网络节能资源分配方法

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摘要

In this paper, maximizing energy efficiency (EE) through radio resource allocation for renewable energy powered heterogeneous cellular networks (HetNet) with energy sharing, is investigated. Our goal is to maximize the network EE, conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources. We define the objective function as a sum weighted EE of all links in the HetNet. We formulate the resource allocation problem in terms of subcarrier assignment, power allocation and energy sharing, as a mixed combinatorial and non-convex optimization problem. We propose an energy efficient resource allocation scheme, including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker (KKT) conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning (RL). Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations. The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.
机译:本文研究了通过用于可再生能源的无线电资源分配来最大化能量效率(EE),用于可再生能量供电的具有能量共享的能量共享。我们的目标是最大化网络EE,征服可再生能源的不稳定性,并保证在分配资源期间用户的公平性。我们将目标函数定义为HetNet中的所有链接的总和加权EE。我们在子载波分配,功率分配和能量共享方面制定资源分配问题,作为混合组合和非凸优化问题。我们提出了一种节能资源分配方案,包括用于迭代子载波分配和功率分配的集中资源分配算法,其中通过分析解决问题和水的karush-kuhn-tucker(kkt)条件来解决电力分配问题。此后填充问题和基于加强学习的低复杂性分布式资源分配算法(RL)。我们的数值结果表明,集中式和分布式算法与几次迭代融合。数值结果还表明,我们所提出的集中和分布式资源分配算法优于网络EE方面的现有参考算法。

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