The upcoming Internet of Things and fifth generation communications are expected to support short package transmissions with low complexity and low energy consumption, which motivates applications of noncoherent communications. First, we review the design methods for noncoherent communications based on two statistical schemes, that is, maximum likelihood (ML) decoding and energy-based decoding, which heavily rely on models of channel state information distributions. Then a data-driven machine learning method is proposed to design the noncoherent transceiver for short package transmissions. Neural networks are trained separately or jointly by utilizing finite channel realizations to construct the training samples. With the proposed method, two nondeterministic polynomial-time hard problems, joint transmitters design and ML decoding, are efficiently and approximately solved. Simulations reveal that the proposed machine learning method outperforms the conventional statistical method for cases with imperfect knowledge of the channel state information distributions or multiple transmitters.

中文翻译：

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非相干通信的设计：从统计方法到机器学习

预计即将到来的物联网和第五代通信将支持具有低复杂性和低能耗的短包装传输，这将激发非相干通信的应用。首先，我们回顾基于两种统计方案的非相干通信的设计方法，即最大似然（ML）解码和基于能量的解码，这两种方法严重依赖于信道状态信息分布模型。然后提出了一种数据驱动的机器学习方法来设计用于短封装传输的非相干收发器。通过利用有限通道实现来构造训练样本，对神经网络进行单独或联合训练。利用提出的方法，两个不确定的多项式时间难题，联合发射机设计和ML解码，得到有效解决。仿真表明，对于不完全了解信道状态信息分布或多个发射机的情况，所提出的机器学习方法优于传统的统计方法。