Random Access is an important step in enabling the initial attachment of a
User Equipment (UE) to a Base Station (gNB). The UE identifies itself by
embedding a Preamble Index (RAPID) in the phase rotation of a known base
sequence, which it transmits on the Physical Random Access Channel (PRACH). The
signal on the PRACH also enables the estimation of propagation delay, often
known as Timing Advance (TA), which is induced by virtue of the UE's position.
Traditional receivers estimate the RAPID and TA using correlation-based
techniques. This paper presents an alternative receiver approach that uses
AI/ML models, wherein two neural networks are proposed, one for the RAPID and
one for the TA. Different from other works, these two models can run in
parallel as opposed to sequentially. Experiments with both simulated data and
over-the-air hardware captures highlight the improved performance of the
proposed AI/ML-based techniques compared to conventional correlation methods.

这篇论文介绍了一种使用人工智能 / 机器学习模型的替代接收器方法，其中提出了两个神经网络分别用于 RAPID 和 TA 的估计，通过模拟数据和硬件捕获实验证明了与传统相关方法相比，所提出的基于人工智能和机器学习技术的方法具有更好的性能。