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Resource Allocation for Green Cloud Radio Access Networks Powered by Renewable Energy

机译:由可再生能源提供支持的绿云无线电接入网络的资源分配

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In this paper, sustainable resource allocation for green Cloud Radio Access Networks (C-RAN) powered by renewable energy is investigated. Specifically, the Base Station pool (BS pool) in the C-RAN distributes data to a set of remote radio heads (RRHs) with energy harvesting (EH) capability, and allocates sub-carriers to the selected RRHs for downlink transmissions, by jointly considering the user throughput and energy sustainability performance of RRHs. To this end, a utility optimization problem is formulated, characterizing the stochastic process of energy harvesting (EH) and wireless fading channel. Based on Lyapunov optimization techniques, the formulated problem is decomposed into three subproblems, i.e., energy harvesting, data scheduling, and sub-carrier allocation. An efficient online algorithm is developed to obtain the maximal aggregate user utility while ensuring the stability of the data buffers and sustainability of the energy buffers. Performance analysis demonstrates that the proposed algorithm can achieve a suboptimal performance with guaranteed upper bounds of data queue and energy queue lengths. Extensive simulations validate the effectiveness and efficiency of the proposed algorithm.
机译:本文研究了由可再生能源提供支持的绿云无线电接入网络(C-RAN)的可持续资源分配。具体地,C-RAN中的基站池(BS池)将数据分发到具有能量收集(EH)能力的一组远程无线电头(RRH),并通过共同地将子载波分配给所选择的RRHS,以进行下行链路传输考虑到RRHS的用户吞吐量和能量可持续性性能。为此,配制了实用优化问题,表征了能量收集的随机过程(EH)和无线衰落通道。基于Lyapunov优化技术,配制的问题被分解为三个子问题,即能量收集,数据调度和子载波分配。开发了一个有效的在线算法,以获得最大的聚合用户实用程序,同时确保数据缓冲器的稳定性和能量缓冲器的可持续性。性能分析表明,所提出的算法可以实现具有数据队列和能量队列长度的保证的上限的次优性能。广泛的仿真验证了所提出的算法的有效性和效率。

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