NeTS: Small: Designing Agile and Scalable Self-Healing Functionalities for Ultra Dense Future Cellular Networks
NeTS: Small: Designing Agile and Scalable Self-Healing Functionalities for Ultra Dense Future Cellular Networks
批准号:
1619346
负责人:
Ali Imran
金额:
$50.0万
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
项目状态:
已结题
起止时间:
2016-10-01 至 2020-09-30
中文摘要
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英文摘要
Cellular networks are subject to cell outages of several types. Complete outages are often caused by equipment malfunctions; partial outages or degraded performances are often caused by parameter mis-configurations. Outage rates are proportional to cell density and base station complexity. Both of these factors have been consistently on rise from 1G to 4G. Current semi-automated approaches to cell outage management have proven inadequate and highly inefficient even for today's network, and are surely unfeasible for future cellular networks marked by ultra-dense cell deployment and mounting complexity. If no intervening measures are taken, cell outage management may become a primary challenge for future cellular networks, such as 5G. To remedy this, this project will develop an Advance Cell Outage Management (ACOM) framework for fully automating cell outage detection and compensation in future ultra-dense, heterogeneous cellular networks. ACOM will be built by developing and integrating three novel solutions: 1) Autonomous Macro Cell Outage Detection and root cause analysis (MOD); 2) Autonomous Small Cell Outage Detection and root cause analysis (SOD); and 3) Autonomous Heterogeneous Cell Outage Compensation (HOC). In ACOM, the outage detected and diagnosed by MOD and SOD will be exploited by the optimization process in HOC to transform future ultra-dense, heterogeneous cellular deployments into fully self-healing systems. Key distinct features of ACOM will include agility, stability and flexibility to accommodate varying user densities, cell sizes, and radio channel conditions, and ultra-dense deployments of small cells. The proposed plan lies at the nexus of profiling, anomaly detection, prediction, sparse matrix completions, multidimensional scaling, and dynamics handling. It employs machine learning, optimization, and game theory. Project outcomes will be validated using data from real network data while leveraging a full scale outdoor 5G testbed. If successful, this project is certain to make strong impact on all aspects of evolving digital society that count on reliability of cellular networks. Another key impact of this project is that it offers strong workforce training in a highly sought-after multi-disciplinary skill set needed to conduct proposed research, while ensuring participation of women and other underrepresented groups, and K-12 outreach. The project will also leverage collaboration with national and international stake holders in the cellular ecosystem to maximize its impact on standardization.
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Generative Adversarial Learning for Machine Learning empowered Self Organizing 5G Networks
机器学习的生成对抗学习赋能自组织 5G 网络
DOI:
10.1109/iccnc.2019.8685527
发表时间:
2019
期刊:
Networking and Communications (ICNC
影响因子:
--
作者:
[Hughes, Ben, Bothe, Shruti, Farooq, Hasan, Imran, Ali]
通讯作者:
Imran, Ali
DOI:
10.1109/tvt.2018.2846655
发表时间:
2018-06
期刊:
IEEE Transactions on Vehicular Technology
影响因子:
6.8
作者:
[Ahmad Asghar;H. Farooq;A. Imran]
通讯作者:
Ahmad Asghar;H. Farooq;A. Imran
DOI:
10.1109/glocom.2018.8647689
发表时间:
2018-12
期刊:
2018 IEEE Global Communications Conference (GLOBECOM)
影响因子:
--
作者:
[Usama Masood;Ahmad Asghar;A. Imran;A. Mian]
通讯作者:
Usama Masood;Ahmad Asghar;A. Imran;A. Mian
DOI:
10.1186/s13638-018-1208-0
发表时间:
2018-08
期刊:
Eurasip Journal on Wireless Communications and Networking
影响因子:
2.6
作者:
[Oluwakayode Onireti;A. Imran;M. Imran]
通讯作者:
Oluwakayode Onireti;A. Imran;M. Imran
Concurrent CCO and LB Optimization in Emerging HetNets: A Novel Solution and Comparative Analysis
新兴 HetNet 中的并发 CCO 和 LB 优化:一种新颖的解决方案和比较分析
DOI:
10.1109/pimrc.2018.8580900
发表时间:
2018
期刊:
Indoor and Mobile Radio Communications (PIMRC
影响因子:
--
作者:
[Asghar, Ahmad, Farooq, Hasan, Imran, Ali]
通讯作者:
Imran, Ali
共 18 条
Collaborative Research: SpecEES: Designing A Spectrally Efficient and Energy Efficient Data Aided Demand Driven Elastic Architecture for future Networks (SpiderNET)
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批准号:1923669
-
项目类别:Standard Grant
-
资助金额:$50.01万
-
财政年份:2019
-
负责人:Ali Imran
-
依托单位:
NeTS: Small: Designing an Advanced Mobility Management and Utilization Framework for Enabling mmWave Multi-Band Ultra-Dense Cellular Networks of Future
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批准号:1718956
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项目类别:Standard Grant
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资助金额:$50.0万
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II-New: TurboRAN: Testbed for Ultra-Dense- Multi-Band Control and Data Plane Split Radio Access Networks of the Future
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批准号:1730650
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资助金额:$100.0万
-
财政年份:2017
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负责人:Ali Imran
-
依托单位:
IRES: US-UK: Enabling Ultra-Dense Future Cellular Networks (5G)
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批准号:1559483
-
项目类别:Standard Grant
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资助金额:$24.94万
-
财政年份:2016
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负责人:Ali Imran
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依托单位:
国内基金
海外基金
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