Recovery of missing samples in Orthogonal Frequency Division Multiplexing signals with optimisation using data carriers
Journal article, 2024

A method is proposed for reconstructing an Orthogonal Frequency Division Multiplexing (OFDM) signal that contains data gaps, with the aim to improve demodulation. The main objective is to use the method in a passive radar application with missing data samples and to improve target detection. The OFDM signal is assumed to comply with the Digital Video Broadcasting Terrestrial standard. The proposed recovery method is based on optimisation of a novel objective function, which consists of two parts. The first part is a function of the energy in the out-of-band frequencies, whereas the second, and novel part, uses the location of data carriers in the constellation diagram. The method is evaluated using both simulations and real data. The authors show that the proposed method significantly improves the OFDM signal in just a few iteration steps. The proposed method improved the condition number more than a factor ten thousand millions compared to using the least square method on the out-of-band frequencies only. The authors also decode the symbols with the Viterbi decoding algorithm and show how the required number of iterations with the proposed algorithm depends on the amount of missing samples and on the Signal-to-Noise Ratio in order to achieve a Bit Error Rate of less than one in one hundred thousand millions.

OFDM modulation

channel estimation

signal reconstruction

radar signal processing

passive radar

Author

Anders Haglund

Swedish Defence Research Agency (FOI)

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Per-Olov Frölind

Swedish Defence Research Agency (FOI)

Lars Ulander

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Swedish Defence Research Agency (FOI)

IET Radar, Sonar and Navigation

1751-8784 (ISSN) 1751-8792 (eISSN)

Vol. 18 8 1217-1234

Subject Categories

Communication Systems

Signal Processing

DOI

10.1049/rsn2.12560

More information

Latest update

8/24/2024