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UEs power Reduction Evolution with Adaptive Mechanism over LTE Wireless Networks

Journal of Electronic Science and Technology 2018年第1期16卷 79-83页

作者： Ruchi Sachan Chang Wook Ahn Department of Computer Engineering Sungkyunkwan UniversityGyeonggi-do 16419

At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).

关键词： Green communication 4 generation long term evolution(4G LTE) networks rate adaptation(RA) power consumption model

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维普期刊数据库

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基于绿色分布式光载无线系统的无线传感器网络的使能技术(英文)

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China Communications 2011年第8期8卷 56-63页

作者：沈希徐坤伍剑林金桐 State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications

Novel enabling technologies from physical layer to Medium Access Control (MAC) layer are proposed to provide energy efficient Radio-over-Fiber (RoF) Distributed Antenna System (DAS) based Wireless Sensor Networks (WSN). The power consumption performance of the network is evaluated in terms of the total network power consumption based on the proposed power consumption models from the physical layer. The results illustrate that for a given power consumption value, the tradeoff among the number of Remote Access Units (RAUs), the number of Multiple-Input Multiple-Output (MIMO) antennas and the number of hops must be taken into account. In order to efficiently reduce the total network power consumption, we also proposed a solution from the MAC layer using a sleep-aware MAC protocol, which can dynamically centralized control MIMO antennas to operate in a sleep mode. The proposed enabling technologies provide basis for selecting suitable RoF DAS based WSN and effectively reduce the power consumption.

关键词： RoF DAS WSN power consumption model medium control access protocol

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维普期刊数据库

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GAM modeling on power consumption for Campus Buildings

GAM Modeling on Power Consumption for Campus Buildings

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第37届中国控制会议

作者： Sheng-Luen Chung Yi-Xun Chen Ming-Wei Ye Electrical Engineering Department Taiwan University of Science and Technology

Precise prediction of power consumption is critical to power generation planning, which is important to overall power demand and supply management. power consumption for university campuses is different from that of general office buildings and shopping malls due to, among other factors, the particular academic calendar that includes summer and winter vacations. GAM（Generalized Additive model） model [1] is a powerful tool to fit a set of response data to a collection of explanatory variables for its exceptional capability of modeling interacting explanatory variables though nonlinear smooth functions. GAM has been successfully employed in power prediction for modeling targets like research covering office buildings and nation-wide consumption. However, the most difficult problem in applying GAM modeling is how to find the right fitting GAM formula in the first place. In this regard, this paper proposes a general form of GAM demand model based on human daily activity. On top of this general form, we propose an AOO（Adding One by One）algorithm to find a fitting GAM model for a particular target based on historic data collected from that particular target. In particular, in the context of power consumption modeling, we train the general GAM model by a year-long historic power consumption data of year 2014 from a university campus to derive prediction models for both the expected value and its variance. The two resultant fitting GAM formulas thus obtained for both expected value and the associated variance have been applied to predict the power consumption for the following three years of 2015-2017. The prediction results have shown preferable performance, MAPE for expected value and CAE for variance,over that predicted by the GAM models reported in the literature[2].

关键词： power consumption model Generalized Additive model(GAM)

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