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Spatial Deep Learning for Wireless S...

University of Toronto (Canada).

Spatial Deep Learning for Wireless Scheduling.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Spatial Deep Learning for Wireless Scheduling./
作者:	Cui, Wei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	75 p.
附註:	Source: Masters Abstracts International, Volume: 81-02.
Contained By:	Masters Abstracts International81-02.
標題:	Communication. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13421665
ISBN:	9781085762151

Spatial Deep Learning for Wireless Scheduling.
Cui, Wei.

Spatial Deep Learning for Wireless Scheduling. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 75 p.

Source: Masters Abstracts International, Volume: 81-02.

Thesis (M.A.S.)--University of Toronto (Canada), 2019.

This item must not be sold to any third party vendors.

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model, both of which are computationally intensive. This thesis explores approaches using deep learning for scheduling, based solely on the geographic locations inputs. We innovated a solution by using a novel neural network architecture that computes the geographic spatial convolutions for interference estimation, over multiple feedback stages to learn the optimum solution through unsupervised training. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger problem sizes. Furthermore, this thesis also focuses on optimizing Proportional Fairness across the network, through proposing a novel scheduling approach that utilizes the sum-rate optimal scheduling heuristics over judiciously chosen subsets of links, showing highly competitive performances.

ISBN: 9781085762151Subjects--Topical Terms:

556422
Communication.
Subjects--Index Terms:

Artificial intelligence

Spatial Deep Learning for Wireless Scheduling.
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300 $a 75 p.
500 $a Source: Masters Abstracts International, Volume: 81-02.
500 $a Advisor: Yu, Wei.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2019.
506 $a This item must not be sold to any third party vendors.
520 $a The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model, both of which are computationally intensive. This thesis explores approaches using deep learning for scheduling, based solely on the geographic locations inputs. We innovated a solution by using a novel neural network architecture that computes the geographic spatial convolutions for interference estimation, over multiple feedback stages to learn the optimum solution through unsupervised training. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger problem sizes. Furthermore, this thesis also focuses on optimizing Proportional Fairness across the network, through proposing a novel scheduling approach that utilizes the sum-rate optimal scheduling heuristics over judiciously chosen subsets of links, showing highly competitive performances.
590 $a School code: 0779.
650 4 $a Communication. $3 556422
650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer science. $3 573171
653 $a Artificial intelligence
653 $a Deep learning
653 $a Machine learning
653 $a Non-convex optimization
653 $a Resources allocation
653 $a Wireless communication
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690 $a 0984
710 2 $a University of Toronto (Canada). $b Electrical and Computer Engineering. $3 1148628
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793 $a English
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