The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Massive Random Access

arXiv:2403.18846v1 Announce Type: cross
Abstract: The lack of an efficient preamble detection algorithm remains a challenge for solving preamble collision problems in intelligent massive random access (RA) in practical communication scenarios. To solve this problem, we present a novel early preamble detection scheme based on a maximum likelihood estimation (MLE) model at the first step of the grant-based RA procedure. A novel blind normalized Stein variational gradient descent (SVGD)-based detector is proposed to obtain an approximate solution to the MLE …

abstract algorithm arxiv blind challenge collision communication cs.ai cs.it cs.lg detection eess.sp gradient intelligent massive math.it novel practical random solve type