Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection

• URL: http://arxiv.org/abs/2406.16868v1
• Date: Fri, 8 Mar 2024 21:33:41 GMT
• Title: Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection
• Authors: Jiarui Xu, Karim Said, Lizhong Zheng, Lingjia Liu,
• Abstract summary: Reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system. This paper introduces a novel two-dimensional RC (2D-RC) approach for online symbol detection on a subframe basis in the OTFS system.
• Score: 29.019014658900463
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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