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2019 | OriginalPaper | Chapter

5. Scalable Signal Detection: Randomized Gaussian Message Passing

Authors : Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan

Published in: Scalable Signal Processing in Cloud Radio Access Networks

Publisher: Springer International Publishing

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Abstract

In this chapter, we convert the signal detection in a C-RAN to an inference problem over a bipartite random geometric graph. By passing messages among neighboring nodes, message passing (a.k.a. belief propagation) provides an efficient way to solve the inference problem over a sparse graph. However, the traditional message-passing algorithm does not guarantee to converge, because the corresponding bipartite random geometric graph is locally dense and contains many short loops. As a major contribution of this chapter, we propose a randomized Gaussian message passing (RGMP) algorithm to improve the convergence. The proposed RGMP algorithm demonstrates significantly better convergence performance than the conventional message passing algorithms. In addition, we generalize the RGMP algorithm to a blockwise RGMP (B-RGMP) algorithm, which allows parallel implementation. The average computation time of B-RGMP remains constant when the network size increases.

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Footnotes
1
If x does not follow a Gaussian distribution, the message-passing algorithm presented in this work gives an approximation of the linear MMSE estimation [3].
 
2
A tree-type graph is an undirected graph in which any two nodes are connected by exactly one path, where a path is a sequence of edges which connect a sequence of vertices without repetition.
 
3
A loop in a graph is a path that starts and ends at the same node.
 
4
Lemma 5.1 was previously shown in Theorem 5.1 of [18], but the proof has been omitted in [18]. Here, we include the detailed proof of Lemma 1 for self-containedness.
 
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Metadata
Title
Scalable Signal Detection: Randomized Gaussian Message Passing
Authors
Ying-Jun Angela Zhang
Congmin Fan
Xiaojun Yuan
Copyright Year
2019
Book
Scalable Signal Processing in Cloud Radio Access Networks

Print ISBN: 978-3-030-15883-5

Electronic ISBN: 978-3-030-15884-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-15884-2_5