用于物理层通信的相对相位等变深度神经网络系统
Relative Phase Equivariant Deep Neural Systems for Physical Layer Communications
AI总结：
针对深度神经接收机难以匹配传统接收机能效和速度的问题，本研究将归纳偏置融入物理层，构建对到达相位等变的深度神经接收机，在相似模型规模下显著降低错误率，提升参数效率。
摘要：
在电信时代，对复杂且专业化通信系统的需求不断增长，促使人们聚焦于改进物理层通信。人工智能（AI）已成为实现这一目标的有前景的解决方案途径。深度神经接收机在提升通信系统性能方面已展现出显著潜力。然而，一个主要挑战在于开发能匹配传统接收机能量效率和速度的深度神经接收机。本研究通过群等变深度学习将归纳偏置融入物理层，以提高深度神经接收机的参数效率。我们构建了一个对到达相位具有等变特性的深度神经接收机来实现这一点。研究表明，在模型规模相似的情况下，引入相对相位等变特性可显著降低深度神经接收机的错误率。因此，我们展示了群等变深度学习在物理层通信领域的潜力。
英文摘要：
In the era of telecommunications, the increasing demand for complex and specialized communication systems has led to a focus on improving physical layer communications. Artificial intelligence (AI) has emerged as a promising solution avenue for doing so. Deep neural receivers have already shown significant promise in improving the performance of communications systems. However, a major challenge lies in developing deep neural receivers that match the energy efficiency and speed of traditional receivers. This work investigates the incorporation of inductive biases in the physical layer using group-equivariant deep learning to improve the parameter efficiency of deep neural receivers. We do so by constructing a deep neural receiver that is equivariant with respect to the phase of arrival. We show that the inclusion of relative phase equivariance significantly reduces the error rate of deep neural receivers at similar model sizes. Thus, we show the potential of group-equivariant deep learning in the domain of physical layer communications.
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