Selection of Waveform Parameters Using Machine Learning for 5G and beyond

Creative Commons License

YAZAR A., Arslan H.

30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019, İstanbul, Türkiye, 8 - 11 Eylül 2019, cilt.2019-September identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2019-September
  • Doi Numarası: 10.1109/pimrc.2019.8904153
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: 5G and beyond, machine learning, multi-numerology, resource allocation, waveform
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© 2019 IEEE.Flexibility is one of the essential requirements for future cellular communications technologies. Providing customized communications solutions for each user and service type cannot be possible without the flexibility in 5G and beyond. Different optimizations need to be done for the flexibility related structures of 5G and beyond systems. In this paper, a novel machine learning (ML) based selection mechanism for the configurable waveform parameters is designed from the flexibility perspective. Moreover, a simulation based dataset generation methodology is proposed for ML systems. Results of computer simulations are presented using the generated dataset.