Detect to Learn: Structure Learning with Attention and Decision Feedback
for MIMO-OFDM Receive Processing
- URL: http://arxiv.org/abs/2208.09287v4
- Date: Tue, 20 Jun 2023 18:23:11 GMT
- Title: Detect to Learn: Structure Learning with Attention and Decision Feedback
for MIMO-OFDM Receive Processing
- Authors: Jiarui Xu, Lianjun Li, Lizhong Zheng, and Lingjia Liu
- Abstract summary: This paper introduces an online attention-based approach, namely RC-AttStructNet-DF, that can efficiently utilize pilot symbols and be dynamically updated with the detected payload data.
The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols.
- Score: 25.66317464603635
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The limited over-the-air (OTA) pilot symbols in
multiple-input-multiple-output orthogonal-frequency-division-multiplexing
(MIMO-OFDM) systems presents a major challenge for detecting transmitted data
symbols at the receiver, especially for machine learning-based approaches.
While it is crucial to explore effective ways to exploit pilots, one can also
take advantage of the data symbols to improve detection performance. Thus, this
paper introduces an online attention-based approach, namely RC-AttStructNet-DF,
that can efficiently utilize pilot symbols and be dynamically updated with the
detected payload data using the decision feedback (DF) mechanism. Reservoir
computing (RC) is employed in the time domain network to facilitate efficient
online training. The frequency domain network adopts the novel 2D multi-head
attention (MHA) module to capture the time and frequency correlations, and the
structural-based StructNet to facilitate the DF mechanism. The attention loss
is designed to learn the frequency domain network. The DF mechanism further
enhances detection performance by dynamically tracking the channel changes
through detected data symbols. The effectiveness of the RC-AttStructNet-DF
approach is demonstrated through extensive experiments in MIMO-OFDM and massive
MIMO-OFDM systems with different modulation orders and under various scenarios.
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