移动网络环境智能终端尾能耗优化研究

发布时间：2018-08-27 13:14

【摘要】：目前移动网络已经成为智能终端获取信息资源的主要方式之一,而移动网络数据传输过程中产生的尾能耗,是导致智能终端能源效率低下的关键因素,因此合理有效地降低智能终端的尾能耗,对于提升智能终端的能源效率意义重大。本文紧紧围绕如何降低移动网络环境下智能终端的尾能耗展开深入研究,在此基础上提出了两种尾能耗优化方法,并取得了较好的能耗优化效果。本文已完成的主要工作内容如下:1.详细分析了移动网络中的RRC状态机模型及尾能耗的产生机理,并指出了其中的RRC协议和非连续接受机制(DRX)的设计缺陷,为后续的尾能耗优化研究奠定了理论基础。2.针对RRC协议固定的尾巴时间造成的尾能耗问题,依据3G数据流表现出的时间相关性,提出一种基于时间序列的移动网络尾能耗优化方法(BOA)。该机制通过为观察到的数据块时间间隔序列(TAB)建立自回归移动平均模型(ARMA Model)来预测未来时刻的TAB值,并根据预测值动态调节尾巴时间,实现3G网络环境下的智能终端尾能耗优化。3.针对4G网络中非连续接收机制单一的周期配置难以适应多业务并发的问题,提出一种基于马尔科夫链的自适应DRX优化机制(AM-DRX)。该机制首先为不同的数据业务状态建立马尔科夫链模型来预测未来时刻的业务状态,然后结合预测值和信道监听状态来综合调整DRX周期,实现4G网络环境下的智能终端尾能耗优化。4.设计仿真实验对上述两种尾能耗优化方法的有效性进行验证,实验结果表明,BOA算法与固定尾巴时间的Fixed-Tail算法相比能源效率提升42.25%,AM-DRX算法与固定周期的DRX机制相比能有效降低终端功耗,同时实现了数据业务的自适应DRX参数配置。
[Abstract]:At present, mobile network has become one of the main ways for intelligent terminals to obtain information resources, and the tail energy consumption generated in the process of mobile network data transmission is the key factor leading to the low energy efficiency of intelligent terminals. Therefore, it is of great significance to reduce the tail energy consumption of intelligent terminals reasonably and effectively for improving the energy efficiency of intelligent terminals. This paper focuses on how to reduce the tail energy consumption of intelligent terminals in mobile network environment. On this basis, two tail energy optimization methods are proposed, and good energy optimization results are achieved. The main contents of this paper are as follows: 1. The state machine model of RC in mobile network and the tail energy consumption are analyzed in detail. The design flaws of RRC protocol and discontinuous acceptance mechanism (DRX) are pointed out, which lays a theoretical foundation for the subsequent research on tail energy optimization. 2. Aiming at the tail energy consumption problem caused by the fixed tail time of RRC protocol, a time series based mobile network tail energy is proposed according to the time correlation of 3G data stream. This mechanism establishes an autoregressive moving average (ARMA) model for the observed block time interval sequence (TAB) to predict the TAB values at future times, and dynamically adjusts the tail time according to the predicted values to optimize the tail energy consumption of intelligent terminals in 3G networks. 3. Formulation of discontinuous receivers in 4G networks. A Markov Chain-based Adaptive DRX Optimization Mechanism (AM-DRX) is proposed to solve the problem of multi-service concurrency. Firstly, a Markov Chain model is established for different data service states to predict future service states, and then the DRX cycle is adjusted by combining predictive value with channel monitoring state. The simulation results show that the energy efficiency of the BOA algorithm is 42.25% higher than that of Fixed-Tail algorithm with fixed tail time, and the AM-DRX algorithm can effectively reduce the final energy consumption compared with the fixed-period DRX mechanism. The power consumption of the terminal is realized, and the adaptive DRX parameter configuration of data service is realized.
【学位授予单位】：西北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5

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