Joint Channel Estimation and Synchronization Techniques for Time-Interleaved Block-Windowed Burst OFDM

Clicks: 127
ID: 271409
2021
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
From a conceptual perspective, beyond-5G technologies promise to deliver very low latency, even higher data rates, and ultrareliable connections for future generations of communication systems. Modulation schemes based on orthogonal frequency-domain multiplexing (OFDM) can accommodate these requirements for wireless systems. Several hybrid OFDM-based systems, such as the time-interleaved block-windowed burst–OFDM (TIBWB–OFDM), are capable of achieving even better spectral confinement and power efficiency. This paper addresses the implementation of the TIBWB–OFDM system in more realistic and practical wireless link scenarios by addressing the challenges of proper and reliable channel estimation and frame synchronization. We propose to incorporate a preamble formed by optimal correlation training sequences such as the Zadoff–Chu (ZC) sequences. The added ZC preamble sequence is used to jointly estimate the frame beginning through signal-correlation strategies and a threshold decision device, and acquire channel-state information (CSI) by employing estimators on the basis of the preamble sequence and transmitted data. The employed receiver estimators show that it is possible to detect the TIBWB–OFDM frame beginning and highlight the robustness of the TIBWB–OFDM technique to imperfect channel estimations by showing that it can provide comparatively close BER performance to the one where the CSI is perfectly known.
Reference Key
martins2021appliedjoint Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors João Martins;Filipe Conceição;Marco Gomes;Vitor Silva;Rui Dinis;Martins, João;Conceição, Filipe;Gomes, Marco;Silva, Vitor;Dinis, Rui;
Journal applied sciences
Year 2021
DOI
10.3390/app11104403
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.