REINFORCEMENT LEARNING FOR SCHEDULING AND MIMO BEAM SELECTION USING CAVIAR SIMULATIONS

被引：0

作者：

Tavares Borges, Joao Paulo ^{[1
]}

de Oliveira, Ailton Pinto ^{[1
]}

Bastos e Bastos, Felipe Henrique ^{[1
]}

Ne do Nascimento Suzuki, Daniel Takashi ^{[1
]}

de Oliveira Junior, Emerson Santos ^{[1
]}

Bezerra, Lucas Matni ^{[2
]}

Nahum, Cleverson Veloso ^{[1
]}

Batista, Pedro dos Santos ^{[3
]}

da Rocha Klautau Junior, Aldebaro Barreto ^{[1
]}

机构：

[1] Fed Univ Para, BR-66075110 Belem, Para, Brazil

[2] Univ Estacio Sa, BR-66055260 Belem, Para, Brazil

[3] Ericsson Res, S-16480 Stockholm, Sweden

来源：

2021 ITU KALEIDOSCOPE CONFERENCE: CONNECTING PHYSICAL AND VIRTUAL WORLDS (ITU K) | 2021年

关键词：

5G; 6G; beam selection; MIMO; mmWave; RL; NETWORKS;

D O I：

10.23919/ITUK53220.2021.9662100

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes a framework for research on Reinforcement Learning (RL) applied to scheduling and MIMO beam selection. This framework consists of asking the RL agent to schedule a user and then choose the index of a beamforming codebook to serve it. A key aspect of this problem is that the simulation of the communication system and the artificial intelligence engine is based on a virtual world created with AirSim and the Unreal Engine. These components enable the so-called CAVIAR methodology, which leads to highly realistic 3D scenarios. This paper describes the communication and RL modeling adopted in the framework and also presents statistics concerning the implemented RL environment, such as data traffic, as well as results for three baseline systems.

引用

页码：141 / 148

页数：8

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