论文标题
尖峰神经网络决策反馈均衡
Spiking Neural Network Decision Feedback Equalization
论文作者
论文摘要
在过去的几年中，人工神经网络（ANN）已成为解决传统方法难以解决的通信工程任务的事实上的标准。同时，人工智能社区将其研究推向了以生物学启发的，类似脑的尖峰神经网络（SNN），该网络有望实现极其节能的计算。在本文中，我们研究了在通道均衡中使用SNN对超低复杂性接收器的使用。我们提出了一个基于SNN的均衡器，其反馈结构类似于决策反馈均衡器（DFE）。为了将现实世界数据转换为尖峰信号，我们引入了一种新颖的三元编码，并将其与传统的日志尺度编码进行比较。我们表明，对于三个不同的模范通道，我们的方法显然优于常规线性均衡器。我们强调，主要是将通道输出转换为尖峰的转换引入了少量的性能惩罚。带有决策反馈结构的拟议SNN可以使竞争性节能收发器的途径。
In the past years, artificial neural networks (ANNs) have become the de-facto standard to solve tasks in communications engineering that are difficult to solve with traditional methods. In parallel, the artificial intelligence community drives its research to biology-inspired, brain-like spiking neural networks (SNNs), which promise extremely energy-efficient computing. In this paper, we investigate the use of SNNs in the context of channel equalization for ultra-low complexity receivers. We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE). For conversion of real-world data into spike signals we introduce a novel ternary encoding and compare it with traditional log-scale encoding. We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels. We highlight that mainly the conversion of the channel output to spikes introduces a small performance penalty. The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.
