The Blind Normalized Stein Variational Gradient Descent-Based Detection
  for Intelligent Massive Random Access
Xin Zhu Ahmet Enis Cetin
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 model. First, by exploring the
relationship between the Hadamard transform and wavelet transform, a new
modified Hadamard transform (MHT) is developed to separate high-frequencies
from important components using the second-order derivative filter. Next, to
eliminate noise and mitigate the vanishing gradients problem in the SVGD-based
detectors, the block MHT layer is designed based on the MHT, scaling layer,
soft-thresholding layer, inverse MHT and sparsity penalty. Then, the blind
normalized SVGD algorithm is derived to perform preamble detection without
prior knowledge of noise power and the number of active devices. The
experimental results show the proposed block MHT layer outperforms other
transform-based methods in terms of computation costs and denoising
performance. Furthermore, with the assistance of the block MHT layer, the
proposed blind normalized SVGD algorithm achieves a higher preamble detection
accuracy and throughput than other state-of-the-art detection methods.