NeTS: Small: Sustainable Wireless Communications with Optimal Utilization of Renewable Energy and Storage
NeTS:小型:可持续无线通信,优化利用可再生能源和存储
基本信息
- 批准号:1619184
- 负责人:
- 金额:$ 25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-10-01 至 2020-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The massive increases in wireless devices and data are expected to lead to significant increases in energy consumption and carbon emissions of future wireless networks. The problem will be exacerbated by a growing number of off-grid base stations powered by diesel. This project addresses the sustainability of future wireless networks by significantly reducing the use of electricity and diesel in running wireless base stations, and thereby carbon emissions. While various techniques have been proposed to reduce power consumption of wireless networks, this project will address the challenging problem of efficient and cost-effective use of intermittent renewable power sources like solar and wind power to minimize grid/diesel power consumption while ensuring no adverse impact on user experience. Additionally, the project will demonstrate the feasibility of solar-powered small cells, significantly enhancing wireless connectivity in rural or remote areas, and enable unplanned and rapid deployments in urban areas. The resulting software and testbed will facilitate future research in renewable energy for wireless networks, and a new course on sustainable communications. The PI will work closely with University of California-San Diego Center for Wireless Communications industry members to validate the techniques developed, and facilitate adoption in their products which will lead to adoption of solar and wind energy sources to power future generations of wireless networks.The project will introduce the concept of using data storage in user devices to transfer surplus renewable power to surplus data stored, to be utilized during periods of deficit renewable power to reduce electricity/diesel, with no need for energy storage. It will be enabled by a novel dynamic base station resource allocation technique which can indirectly impact data rate. This, in turn, will affect the data stored at user devices and also the base station power consumed, depending on surplus and deficit periods, without affecting user experience. A second technique will be developed for use of an additional low capacity energy storage at the base station to further enhance utilization of the temporally varying renewable power. The technique will simultaneously decide to use project will also develop new dynamic user association and transmit power adaptation techniques making use of spatial variations in renewable power between neighboring base stations to minimize total grid electricity/diesel consumed for heterogeneous networks, including showing feasibility of solar-only powered small cells. A simulation framework and testbed will be developed to demonstrate the effectiveness of the approaches.
无线设备和数据的巨大增加预计将导致未来无线网络的能源消耗和碳排放大幅增加。越来越多的由柴油动力的离网基站加剧了问题。该项目通过大大减少运行无线基站的电力和柴油的使用,从而解决未来无线网络的可持续性,从而解决碳排放。尽管已经提出了各种技术来减少无线网络的功耗,但该项目将解决有效且具有成本效益的间歇性可再生能源(如太阳能和风能)的挑战性问题,以最大程度地减少电网/柴油机的功耗,以确保没有不良影响在用户体验上。此外,该项目将证明由太阳能驱动的小细胞的可行性,可显着提高农村或偏远地区的无线连接,并在城市地区实现计划外和快速部署。最终的软件和测试台将促进无线网络可再生能源的未来研究,以及有关可持续通信的新课程。 PI将与加利福尼亚大学迭戈大学无线通信行业成员中心紧密合作,以验证开发的技术,并促进其产品中的采用,这将导致采用太阳能和风能来源,从而为后代的无线网络提供动力。项目将介绍在用户设备中使用数据存储的概念,以将剩余的可再生能源传输存储的剩余数据,并在赤字可再生能源期间使用以减少电力/柴油,而无需存储储能。它将通过一种新型的动态基站资源分配技术来启用,该技术可能会间接影响数据速率。反过来,这将影响存储在用户设备以及所消耗的基站功率的数据,具体取决于剩余和赤字时期,而不会影响用户体验。将开发第二种技术,用于在基站使用额外的低容量储能,以进一步增强时间变化的可再生能源的利用。该技术将同时决定使用项目,还将开发新的动态用户协会,并传输功率适应技术,利用邻近基站之间可再生功率的空间变化,以最大程度地减少用于异质网络消耗的总电网电力/柴油,包括显示出Solar- Solar-- solar--可行性。只有动力的小细胞。将开发一个模拟框架和测试台以证明方法的有效性。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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Sujit Dey其他文献
Eeects of Resource Sharing on Circuit Delay: an Assignment Algorithm for Clock Period Optimization
资源共享对电路延迟的影响:时钟周期优化的分配算法
- DOI:
- 发表时间:
1994 - 期刊:
- 影响因子:0
- 作者:
S. Bhattacharya;Sujit Dey;F. Brglez - 通讯作者:
F. Brglez
Classification of Patient Recovery From COVID-19 Symptoms Using Consumer Wearables and Machine Learning
使用消费类可穿戴设备和机器学习对患者从 COVID-19 症状中恢复的分类
- DOI:
10.1109/jbhi.2023.3239366 - 发表时间:
2023 - 期刊:
- 影响因子:7.7
- 作者:
Jared Leitner;Alexander Behnke;Po;M. Ritter;Marlene Millen;Sujit Dey - 通讯作者:
Sujit Dey
Techniques for implementation of at-speed testable, high performance, and low cost linear designs
实现高速可测试、高性能和低成本线性设计的技术
- DOI:
10.1109/vlsisp.1995.527494 - 发表时间:
1995 - 期刊:
- 影响因子:0
- 作者:
M. Potkonjak;Sujit Dey;Kevin T. Kornegay - 通讯作者:
Kevin T. Kornegay
The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial
使用精确的生活方式指导进行基于人工智能的自主数字健康干预对成人高血压患者血压的影响:单臂非随机试验
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Jared Leitner;Po;P. Agnihotri;Sujit Dey - 通讯作者:
Sujit Dey
Sujit Dey的其他文献
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{{ truncateString('Sujit Dey', 18)}}的其他基金
PFI-TT: Virtual Physical Therapy Using Mobile Devices and Artificial Intelligence
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- 批准号:
2044759 - 财政年份:2021
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
I-Corps Sites - Type II: Von Liebig Center as Type II Renewal I-corps Site
I-军团站点 - II 型:冯·李比希中心作为 II 型更新 I-军团站点
- 批准号:
1644648 - 财政年份:2017
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
The von Liebig Center as an iCorp Site: established POC catalyzing even earlier university discoveries
冯·李比希中心作为 iCorp 站点:建立的 POC 催化了更早的大学发现
- 批准号:
1322093 - 财政年份:2013
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
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NeTS:小型:协作研究:通过用户和应用感知的动态小区重新配置迈向绿色蜂窝网络
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1116859 - 财政年份:2011
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
CSR: Small: Collaborative Research: Adaptive Applications and Architectures for Variation-Tolerant Systems
CSR:小型:协作研究:容变系统的自适应应用程序和架构
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0917354 - 财政年份:2009
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
Power Analysis and Optimization Methodologies for Wireless Embedded Nanochips
无线嵌入式纳米芯片的功耗分析和优化方法
- 批准号:
9912414 - 财政年份:2000
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
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