1. Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection
- Author
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Xu, Jiarui, Said, Karim, Zheng, Lizhong, and Liu, Lingjia
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Artificial Intelligence - Abstract
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., Comment: 6 pages, conference paper. arXiv admin note: substantial text overlap with arXiv:2311.08543
- Published
- 2024