Compressed Sensing for Feedback Generation in OFDM Based LiFi Systems

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Javad Gholipour - , Vodafone Stiftungsprofessur für Mobile Nachrichtensysteme (Autor:in)
  • Kai Lennert Bober - , Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut (Autor:in)
  • Malte Hinrichs - , Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut (Autor:in)
  • Volker Jungnickel - , Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut (Autor:in)

Abstract

In this paper, we evaluate the effectiveness of compressed-sensing-based channel estimation in LiFi orthogonal frequency-division multiplex (OFDM) systems with multiple-input multiple-output (MIMO). The sparseness of LiFi channels suggests that it is beneficial to utilize compressed sensing which has been investigated also for radio communications. In this paper, we formulate the mathematical problem of compressed-sensing for feedback generation in OFDM-based LiFi systems. We use well-known algorithms for both, sparse pilot design and sparse recovery and apply them to LiFi OFDM channels. We evaluate the effectiveness of these algorithms by measuring the achievable mean squared error (MSE) and vary the total numbers of subcarriers as well as the number of pilot subcarriers.

Details

OriginalspracheEnglisch
Titel2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
Seitenumfang6
ISBN (elektronisch)978-1-6654-9122-8
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Externe IDs

Scopus 85159789189

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

  • LiFi, MIMO, OFDM, channel estimation, compressed sensing