Joint communication and radar sensing in 5G mobile network by compressive sensing

Radio sensing can be integrated with communication in what the authors call future perceptive mobile networks. Due to the complicated signal structure, it is challenging to estimate sensing parameters such as delay, angle of arrival, and Doppler when joint communication and radar/radio sensing is ap...

Full description

Saved in:
Bibliographic Details
Published in:IET communications 2020-12, Vol.14 (22), p.3977-3988
Main Authors: Rahman, Md Lushanur, Zhang, J. Andrew, Huang, Xiaojing, Guo, Y. Jay, Lu, Zhiping
Format: Article
Language:English
Subjects:
1D–3D CS techniques
2D cluster Kronecker CS algorithm
5G channel conditions
5G mobile communication
5G mobile network
a prior probability distribution
cluster structure
clustered channels
complicated signal structure
compressed sensing
compressive sensing
fifth‐generation new radio standard
future perceptive mobile networks
improved sensing parameter estimation
joint communication
multipath channels
parameter estimation
probability
radar sensing
Research Article
sensing parameters
sparse signal recovery problem
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Radio sensing can be integrated with communication in what the authors call future perceptive mobile networks. Due to the complicated signal structure, it is challenging to estimate sensing parameters such as delay, angle of arrival, and Doppler when joint communication and radar/radio sensing is applied in perceptive mobile networks. Radio sensing with signals compatible with a fifth-generation (5G) new radio standard using one-dimension (1D) to 3D compressive sensing (CS) techniques under 5G channel conditions is studied. In the case of 1D–3D CS techniques, they formulate the parameter estimation as a sparse signal recovery problem. These algorithms demonstrate respective advantages, but also show shortcomings in dealing with clustered channels. To effectively exploit the cluster structure in multipath channels, they also propose a 2D cluster Kronecker CS algorithm for significantly improved sensing parameter estimation via introducing a prior probability distribution. Simulation results are provided and they focus the respective advantages and disadvantages of these techniques that validate the effectiveness of the proposed algorithms.
ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2020.0384