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arXiv 1802.03576cs.ITmath.IT
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软件定义移动网络中能量收集用户的下行链路用户关联和上行链路调度

Downlink User Association and Uplink Scheduling for Energy Harvesting Users in Software-Defined Mobile Networks

Hassan Yeganeh, Javane Rostampoor

摘要:

在本文中,我们考虑一个异构网络,其中包括一个宏基站和一些微微基站利用大规模MIMO和MIMO技术,分别。采用中央软件定义移动网络(SDMN)控制器,以提供用户关联和能量调度。用户被认为是电池有限的,并且能够同时进行无线信息和功率传输(SWIPT),以便收获能量并解决能量短缺问题。这些用户从下行链路中的接收信号中获取能量,并通过其上行链路通信消耗能量。本文通过引入一个合适的效用函数,联合优化网络的总速率和所收获的能量来处理下行链路用户关联。在这方面,经由下行链路优化阶段计算每个用户的最优用户关联和功率分割因子。然后,将上行链路调度过程定义为在每个时间点选择最佳用户进行数据传输,并通过求解李雅普诺夫漂移加惩罚函数优化其发射功率。仿真结果提供,以确认所提出的算法相比,以前的用户关联和上行链路调度方法在用户之间提供公平性和电池管理方面的最优性。

英文摘要:

In this paper we consider a heterogeneous network which consists of a macro base station and some pico base stations utilizing massive MIMO and MIMO techniques, respectively. A central software-defined mobile network (SDMN) controller is adopted in order to provide user association and energy scheduling. The users are considered battery limited and are capable of simultaneous wireless information and power transfer (SWIPT) in order to harvest energy and address the energy shortage issue. These users harvest energy from the received signals in the downlink and consume it via their uplink communications. This paper deals with the downlink user association by jointly optimizing the overall sum-rate of the network and the harvested energy by introducing an appropriate utility function. In this regard, the optimum user association and power splitting factor for each user are calculated via the downlink optimization stage. Then, the process of uplink scheduling is defined as choosing the best users in each time epoch to transfer data as well as optimizing their transmit power by solving Lyapunov drift-plus-penalty function. Simulation results are provided in order to confirm the optimality of the proposed algorithm in comparison with the previous user association and uplink scheduling approaches in terms of providing fairness and battery management among users.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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