Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint
  User Activity Detection and Channel Estimation   Faced with the massive connection, sporadic transmission, and small-sized
data packets in future cellular communication, a grant-free non-orthogonal
random access (NORA) system is considered in this paper, which could reduce the
access delay and support more devices. In order to address the joint user
activity detection (UAD) and channel estimation (CE) problem in the grant-free
NORA system, we propose a deep neural network-aided message passing-based block
sparse Bayesian learning (DNN-MP-BSBL) algorithm. In this algorithm, the
message passing process is transferred from a factor graph to a deep neural
network (DNN). Weights are imposed on the messages in the DNN and trained to
minimize the estimation error. It is shown that the weights could alleviate the
convergence problem of the MP-BSBL algorithm. Simulation results show that the
proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a
smaller number of iterations.

8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics