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Bayesian Nonparametric Reinforcement...

Shih, Po-Kan.

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Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence./
作者:	Shih, Po-Kan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	82 p.
附註:	Source: Masters Abstracts International, Volume: 82-11.
Contained By:	Masters Abstracts International82-11.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28493248
ISBN:	9798738619939

Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence.
Shih, Po-Kan.

Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 82 p.

Source: Masters Abstracts International, Volume: 82-11.

Thesis (M.S.)--Arizona State University, 2021.

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

With the formation of next generation wireless communication, a growing number of new applications like internet of things, autonomous car, and drone is crowding the unlicensed spectrum. Licensed network such as LTE also comes to the unlicensed spectrum for better providing high-capacity contents with low cost. However, LTE was not designed for sharing spectrum with others. A cooperation center for these networks is costly because they possess heterogeneous properties and everyone can enter and leave the spectrum unrestrictedly, so the design will be challenging. Since it is infeasible to incorporate potentially infinite scenarios with one unified design, an alternative solution is to let each network learn its own coexistence policy. Previous solutions only work on fixed scenarios. In this work we present a reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE-LAA agents in 5 GHz unlicensed spectrum. The coexistence problem was modeled as a Dec-POMDP and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure was introduced in the reward function to encourage fair sharing between agents. We turned the reinforcement learning into an optimization problem by transforming the value function as likelihood and variational inference for posterior approximation. Simulation results demonstrate that this algorithm can reach high value with compact policy representations, and stay computationally efficient when applying to agent set.

ISBN: 9798738619939Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Bayesian statistics

Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence.
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500 $a Source: Masters Abstracts International, Volume: 82-11.
500 $a Advisor: Moraffah, Bahman;Papandreou-Suppappola, Antonia.
502 $a Thesis (M.S.)--Arizona State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a With the formation of next generation wireless communication, a growing number of new applications like internet of things, autonomous car, and drone is crowding the unlicensed spectrum. Licensed network such as LTE also comes to the unlicensed spectrum for better providing high-capacity contents with low cost. However, LTE was not designed for sharing spectrum with others. A cooperation center for these networks is costly because they possess heterogeneous properties and everyone can enter and leave the spectrum unrestrictedly, so the design will be challenging. Since it is infeasible to incorporate potentially infinite scenarios with one unified design, an alternative solution is to let each network learn its own coexistence policy. Previous solutions only work on fixed scenarios. In this work we present a reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE-LAA agents in 5 GHz unlicensed spectrum. The coexistence problem was modeled as a Dec-POMDP and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure was introduced in the reward function to encourage fair sharing between agents. We turned the reinforcement learning into an optimization problem by transforming the value function as likelihood and variational inference for posterior approximation. Simulation results demonstrate that this algorithm can reach high value with compact policy representations, and stay computationally efficient when applying to agent set.
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650 4 $a Computer engineering. $3 621879
650 4 $a Technical communication. $3 3172863
653 $a Bayesian statistics
653 $a Coexistence
653 $a Nonparametric model
653 $a Reinforcement learning
653 $a Spectrum sharing
653 $a Variational inference
653 $a Wireless communication
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793 $a English
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