Enhancements for 5G NR PRACH Reception: An AI/ML Approach
Rohit Singh Anil Kumar Yerrapragada Jeeva Keshav S Radha Krishna Ganti
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.