Integrated OTFS Waveform Design Based on Unified Matrix for Joint Communication and Radar System

• Published in: IEEE internet of things journal 2024-09, Vol.11 (18), p.29235-29251
• Main Authors: Li, Mao, Liu, Wei, Lei, Jing, Zhu, Jinkun, An, Kang, Chatzinotas, Symeon
• Format: Article
• Language: English
• Subjects: Algorithms; Banded structure; Coarse and fine estimation; Communications systems; Complexity; Complexity theory; discrete fractional Fourier transform (DFrFT); Doppler effect; Error analysis; Estimation; Fourier transforms; fractional LU-ZF/MMSE equalization; OFDM; Order parameters; Orthogonal Frequency Division Multiplexing; Parameter estimation; Performance degradation; Radar; Radar equipment; sensing parameter estimation; Sensors; UM-OTFS waveform; Waveforms
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3433406

Orthogonal time frequency space (OTFS) has attracted a lot of attention as a feasible waveform applied in joint communication and radar (JCR) systems in contrast to orthogonal frequency division multiplexing (OFDM) waveform. To explore the advantages of OTFS waveform, first, a unified matrix (UM) expression is summarized by utilizing discrete fractional Fourier transform (DFrFT), and then a novel OTFS waveform based on UM expression is investigated in this article. The fractional order parameters of the proposed UM-OTFS waveform is set to the same values during preprocessing and Heisenberg transformation stages, and the UM-OTFS waveform can be converted into other waveform forms by undergoing different fractional order parameters. In addition, a three-stage sensing parameter estimation algorithm is developed for target velocity and range estimation through grid partitioning, coarse and fine estimation. Meanwhile, a low-complexity fractional zero force (ZF) or minimum mean square error (MMSE) equalizer based on lower-upper (LU) decomposition (LU-ZF/MMSE) is presented, which results in a log-linear order of complexity without any performance degradation of bite error ratio (BER) by analyzing sparsity and quasi-banded structure of the equivalent matrix. The simulation results indicate the superiority of the proposed UM-OTFS waveform in terms of sensing parameter estimation and BER performance compared with several advanced waveforms.