Base Station Power Optimization for Green Networks Using Reinforcement Learning

Semih AKTAŞ; Hande ALEMDAR

doi:https://doi.org/10.35377/saucis.04.02.932709

Makale detayları

Base Station Power Optimization for Green Networks Using Reinforcement Learning

Yazarlar:

Semih AKTAŞ | Hande ALEMDAR

Ana Başlıklar:

Özel bilgisayar yöntemleri | Uygulamalı fizik

Dergi:

Sakarya University Journal of Computer and Information Sciences 2021 Vol.4 Issue 2, pp.244-265

Belge Tipi:

Makale

Yayın Tarihi:

8/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000551909

DOI:

https://doi.org/10.35377/saucis.04.02.932709

Özet:

 

The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore “green” approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving energy savings without degrading the quality of service is a huge challenge. In order to address this issue, we propose a machine learning based intelligent solution that also incorporates a network simulator. We develop a reinforcement-based learning model by using deep deterministic policy gradient algorithm. Our model update frequently the policy of network switches in a way that, packet be forwarded to base stations with an optimized power level. The policies taken by the network controller are evaluated with a network simulator to ensure the energy consumption reduction and quality of service balance. The reinforcement learning model allows us to constantly learn and adapt to the changing situations in the dynamic network environment, hence having a more robust and realistic intelligent network management policy set. Our results demonstrate that energy efficiency can be enhanced by 32% and 67% in dense and sparse scenarios, respectively.

Makale detayları

Base Station Power Optimization for Green Networks Using Reinforcement Learning

Yazarlar: Semih AKTAŞ | Hande ALEMDAR

Ana Başlıklar: Özel bilgisayar yöntemleri | Uygulamalı fizik

Dergi: Sakarya University Journal of Computer and Information Sciences 2021 Vol.4 Issue 2, pp.244-265

Belge Tipi: Makale

Yayın Tarihi: 8/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000551909

DOI: https://doi.org/10.35377/saucis.04.02.932709

Sayı

Yazarlar:

Semih AKTAŞ | Hande ALEMDAR

Ana Başlıklar:

Özel bilgisayar yöntemleri | Uygulamalı fizik

Dergi:

Sakarya University Journal of Computer and Information Sciences 2021 Vol.4 Issue 2, pp.244-265

Belge Tipi:

Makale

Yayın Tarihi:

8/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000551909

DOI:

https://doi.org/10.35377/saucis.04.02.932709