Machine learning assistedmulti-cell base station sleepmode management
Başlık çevirisi mevcut değil.
- Tez No: 756533
- Danışmanlar: DR. MEYSAM MASOUDİ, DR. ÖZLEM TUGFE DEMİR
- Tez Türü: Yüksek Lisans
- Konular: Elektrik ve Elektronik Mühendisliği, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Electrical and Electronics Engineering, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2022
- Dil: İngilizce
- Üniversite: Kth-Kunglıga Teknıska Hogskolan (royal Instıtute Of Technology)
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Özet yok.
Özet (Çeviri)
Fifth generation (5G) communication technology provides higher data rates, better connectivity, and many new facilities. However, there are some challenges that need to be addressed. Denser 5G base station deployment, more power-hungry communication equipment, and an exponentially growing number of mobile users are expected to result in high energy consumption, which is a critical issue for the future in terms of sustainability and potential threats to the environment. These issues raise the importance of energyefficient network design in an aim to increase energy saving. Energy saving could be potentially achieved on the most energy-consuming component of the mobile networks: base stations. By switching off a number of components while not serving any users, base stations could switch to a sleeping mode thus, save energy. This thesis focuses on maximizing the sleeping opportunity of a base station while it is not serving any users by exploiting novelties brought by 5G networks namely 5G NR numerology and designing a sleep management algorithm for base stations to reduce redundant energy consumption by using Advanced Sleep Modes. To maximize the duration of sleep, 5G numerology with finer time granularity was used and an increase in the sleeping opportunity was observed compared to the baseline numerology in 4G systems. Based on that, a Qlearning algorithm has been designed in a reference base station in a multicell environment in order to manage how long and how deep to sleep by using Advanced Sleep Modes. The results have shown that compared to a non-intelligent sleep mode management scheme, which takes advantage of only the shallowest sleep mode, up to 80% of the potentially wasted energy when the BS is inactive could be saved. With a balanced trade-off policy between power consumption and additional latency induced to the users, 51% of the potentially wasted energy is saved while the users experience an additional latency of 2.1 ms on average. The algorithm adapts to the changing conditions in the network by making decisions according to the varying sleeping opportunities.
Benzer Tezler
- Secure and coordinated beamforming in 5G and beyond systems using deep neural networks
5G ve ötesi sistemlerde derin sinir ağları kullanarak güvenli ve koordineli hüzmeleme
UTKU ÖZMAT
Doktora
İngilizce
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik ÜniversitesiBilişim Uygulamaları Ana Bilim Dalı
DR. ÖĞR. ÜYESİ MEHMET AKİF YAZICI
DR. ÖĞR. ÜYESİ MEHMET FATİH DEMİRKOL
- Adaptive physical layer security with machine learning: From detection to defense
Makine öğrenmesi ile adaptif fiziksel katman güvenliği: Tespitten savunmaya
UFUK ALTUN
Doktora
İngilizce
2026
Elektrik ve Elektronik MühendisliğiKoç ÜniversitesiElektrik ve Elektronik Mühendisliği Ana Bilim Dalı
PROF. DR. ERTUĞRUL BAŞAR
- Derin öğrenme destekli dielektrik rezonatör anten tasarımı
Deep learning assisted dielectric resonator antenna design
FİDAN GAMZE KIZILÇAY
Doktora
Türkçe
2026
Elektrik ve Elektronik MühendisliğiSakarya ÜniversitesiElektrik-Elektronik Mühendisliği Ana Bilim Dalı
DOÇ. DR. MUHAMMET HİLMİ NİŞANCI
- Makine öğrenmesi yöntemleriyle bel bölgesi rahatsızlıklarının tanısı
Diagnosis of lumbar disease by using machine learning techniques
YAVUZ ÜNAL
Doktora
Türkçe
2015
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolSelçuk ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
YRD. DOÇ. DR. HASAN ERDİNÇ KOÇER
DOÇ. DR. KEMAL POLAT
- FLAGS framework and decentralized federated learning under device volatility
FLAGS platformu ve cihaz dalgalanması durumunda merkeziyetsiz federe öğrenme
AHNAF HANNAN LODHI
Doktora
İngilizce
2023
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolKoç ÜniversitesiBilgisayar Bilimleri ve Mühendisliği Ana Bilim Dalı
PROF. DR. ÖZNUR ÖZKASAP
YRD. DOÇ. DR. BARIŞ AKGÜN