论文标题
检测学习：结构学习，注意和对MIMO-OFDM的决策反馈接收处理
Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
论文作者
论文摘要
在多输入 - 多输出正交频率 - 频率 - 划分 - 多路复用（MIMO-OFDM）系统中，有限的空中（OTA）飞行员符号提出了一个主要挑战，用于检测接收器的传输数据符号，尤其是用于基于机器的方法。虽然探索开发飞行员的有效方法至关重要，但也可以利用数据符号来提高检测性能。因此，本文介绍了一种基于在线注意力的方法，即RC-Attstructnet-DF，该方法可以有效地利用Pilot符号，并使用检测到的有效载荷数据使用决策反馈（DF）机制进行动态更新。水库计算（RC）用于时域网络，以促进有效的在线培训。频域网络采用新型的2D多头注意（MHA）模块来捕获时间和频率相关性，以及基于结构的结构网络，以促进DF机制。注意力损失旨在学习频域网络。 DF机制通过通过检测到的数据符号动态跟踪通道变化来进一步增强检测性能。通过在Mimo-OfdM和具有不同调制订单的大型Mimo-OfdM系统中进行的广泛实验，可以证明RC-Attstructnet-DF方法的有效性。
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.
