Deep Learning-Based PRACH Detection Algorithm Design and Simulation

PRACH (Physical random access channel) plays an important role in fifth generation (5G) system, and PRACH detection is a key issue in the field of wireless signal processing. The work in this paper proposes a FNN (Fully-connected Neural Network) based algorithm to reduce the signal processing complexity without loss of detection rate. Different models are given in this paper to support different lengths of PRACH preambles. Simulation results are provided to evaluate the performance of the proposed algorithm.