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.

对于正交多访问（OMA）系统，服务的用户设备（UES）的数量仅限于可用的正交资源的数量。另一方面，非正交多访问（NOMA）方案允许多个UES使用相同的正交资源。这种额外的自由度为资源分配带来了新的挑战。缓冲状态信息（BSI），例如等待传输的数据包的大小和年龄，可用于改善OMA系统中的调度。在本文中，我们研究了BSI对上行链路多载波NOMA场景中集中调度程序的性能的影响，UE具有各种数据速率和延迟要求。为了处理将UES分配给资源的大型组合空间，我们提出了一个基于Actor-Critic-Critic强化学习纳入BSI的新型调度程序。使用诺基亚的“无线套件”进行培训和评估。我们提出了各种新颖的技术来稳定和加快训练。建议的调度程序优于基准调度程序。