Enhancements for 5G NR PRACH Reception: An AI/ML Approach
This work addresses random access efficiency for 5G networks, but it is incremental as it applies existing AI/ML techniques to a specific domain problem.
The paper tackles the problem of random access reception in 5G NR by proposing AI/ML-based neural networks for estimating Preamble Index and Timing Advance, showing improved performance over traditional correlation methods in experiments with simulated and hardware-captured data.
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.