Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection
<|reference_start|>Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection: Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only the limited over-the-air (OTA) pilot symbols are utilized for training. However, the previous RC-based approach does not design the RC architecture based on the properties of the OTFS system to fully unlock the potential of RC. This paper introduces a novel two-dimensional RC (2D-RC) approach for online symbol detection on a subframe basis in the OTFS system. The 2D-RC is designed to have a two-dimensional (2D) filtering structure to equalize the 2D circular channel effect in the delay-Doppler (DD) domain of the OTFS system. With the introduced architecture, the 2D-RC can operate in the DD domain with only a single neural network, unlike our previous work which requires multiple RCs to track channel variations in the time domain. Experimental results demonstrate the advantages of the 2D-RC approach over the previous RC-based approach and the compared model-based methods across different modulation orders.<|reference_end|>
@article{xu2024neural,
    title={Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection},
    author={Jiarui Xu, Karim Said, Lizhong Zheng, and Lingjia Liu},
    journal={arXiv preprint arXiv:2406.16868},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16868},
    primaryClass={eess.SP cs.AI}
}