On the Energy and Data Storage Management in Energy Harvesting Wireless Communications
On the Energy and Data Storage Management in Energy Harvesting Wireless Communications
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DOI：
10.1109/tcomm.2019.2934451
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发表时间：
2019-08
影响因子：
8.3
通讯作者：
Sami Akın;M. C. Gursoy
中科院分区：
文献类型：
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作者：
Sami Akın;M. C. Gursoy
无线通信中的能量收集（EH）已成为近年来传输技术研究的焦点。在此，储能建模是必须仔细对待的关键设计基准之一。了解能量存储动态和吞吐量水平是必不可少的，特别是对于通信系统，其中的性能完全取决于收集的能量。虽然应避免能源中断，但也应防止能源溢出，以便利用所有收获的能源。因此，一个简单的，但全面的，分析模型，可以代表的EH无线通信系统的一般类别的特性需要建立。在本文中，调用工具，从大偏差理论沿着马尔可夫过程，建立了一个牢固的联系之间的能量状态的电池和数据传输过程通过无线信道的EH发射机。特别是，一个简单的指数近似的能量溢出的概率制定，其特征在于在电池中的能量衰减率作为能量使用的措施。然后，将能源中断和供应映射到马尔可夫过程上，建立了离散状态模型，给出了给定能源到达和需求过程的能源中断概率表达式。最后，在能量溢出和中断约束下，在无线信道上的平均数据服务（传输）率得到和系统的有效容量，它表征的最大数据到达率下的服务质量（QoS）的约束施加在数据缓冲区溢出概率的发送器缓冲区，推导出。
Energy harvesting (EH) in wireless communications has become the focus of recent transmission technology studies. Herein, energy storage modeling is one of the crucial design benchmarks that must be treated carefully. Understanding the energy storage dynamics and the throughput levels is essential especially for communication systems in which the performance depends solely on harvested energy. While energy outages should be avoided, energy overflows should also be prevented in order to utilize all harvested energy. Hence, a simple, yet comprehensive, analytical model that can represent the characteristics of a general class of EH wireless communication systems needs to be established. In this paper, invoking tools from large deviation theory along with Markov processes, a firm connection between the energy state of the battery and the data transmission process over a wireless channel is established for an EH transmitter. In particular, a simple exponential approximation for the energy overflow probability is formulated, with which the energy decay rate in the battery as a measure of energy usage is characterized. Then, projecting the energy outages and supplies on a Markov process, a discrete state model is established and an expression for the energy outage probability for given energy arrival and demand processes is provided. Finally, under energy overflow and outage constraints, the average data service (transmission) rate over the wireless channel is obtained and the effective capacity of the system, which characterizes the maximum data arrival rate at the transmitter buffer under quality-of-service (QoS) constraints imposed on the data buffer overflow probability, is derived.