| PhD Seminar

Name of the Speaker: Mr. Rohit Singh (EE20D428)
Guide: Prof. Radhakrishna Ganti
Venue: ESB-244 (Seminar Hall)
Online meeting link: https://meet.google.com/cxh-ryrq-fug
Date/Time: 26th February 2025 (Wednesday), 2:00 PM
Title: Machine learning based receiver for 5G NR PRACH

Abstract :

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 UEs position. Traditional receivers estimate the RAPID and TA using correlation-based techniques. Correlation-based receivers suffer from false peaks and missed detection in scenarios dominated by high fading and low signal-to-noise ratio.

In this work, we propose a Machine Learning based receiver where we utilize two neural networks, one for the RAPID and one for TA. Different from other works, these two models can run in parallel as opposed to sequentially. This work is followed by design of a hybrid receiver that consists of an AI/ML model for preamble detection followed by conventional peak detection for the TA estimation. Experimental results show superior performance of the receivers compared to conventional receivers both for simulated and real hardware-captured datasets.