In conventional communication systems, any interference between two communicating points is regarded as unwanted noise since it distorts the received signals. On the other hand, allowing simultaneous transmission and intentionally accepting the interference of signals and even benefiting from it have been considered for a range of wireless applications. As prominent examples, non-orthogonal multiple access (NOMA), joint source-channel coding, and the computation codes are designed to exploit this scenario. They also inspired many other fundamental works from network coding to consensus algorithms. Especially, federated learning is an emerging technology that can be applied to distributed machine learning networks by allowing simultaneous transmission. Although various simultaneous transmission applications exist independently in the literature, their main contributions are all based on the same principle; the superposition property. In this survey, we aim to emphasize the connections between these studies and provide a guide for the readers on the wireless communication techniques that benefit from the superposition of signals. We classify the existing literature depending on their purpose and application area and present their contributions. The survey shows that simultaneous transmission can bring scalability, security, low-latency, low-complexity and energy efficiency for certain distributed wireless scenarios which are inevitable with the emerging Internet of things (IoT) applications.
翻译:在常规通信系统中,两个通信点之间的任何干扰都被视为不需要的噪音,因为它扭曲了收到的信号。另一方面,允许同时传输和故意接受信号的干扰,甚至从中得益,被考虑用于一系列无线应用。作为突出的例子,非横向多重访问(NOMA)、联合源-通道编码和计算代码是为了利用这一假设。它们还启发了网络编码到协商一致算法的许多其他基本工作。特别是,联结学习是一种新兴技术,可以通过允许同时传输的方式应用于分布式机器学习网络。虽然文献中独立存在各种同时传输的应用,但它们的主要贡献都基于同一原则;超置属性。在这次调查中,我们旨在强调这些研究之间的联系,并为读者提供关于无线通信技术的指南,这些技术得益于信号的超置放。我们根据现有文献的目的和应用领域进行分类,并介绍其贡献。调查表明,同时传播可以带来可扩展性、安全性、低延迟性、低复杂性和节能效率,对于某些分布式无线情景来说是不可避免的。