Sparse characterization of body-centric radio channels

Xiaodong Yang; Aifeng Ren; Zhiya Zhang; Qammer Hussain Abbasi; Erchin Serpedin; Wei Zhao; Shuyuan Yang; Akram Alomainy

doi:10.1049/PBTE065E_ch4

Xidian University
Texas A and M University at Qatar Education City
Texas A and M University
Queen Mary University of London

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this chapter, sparse characterization of BWCS is discussed. First of all, a novel sparse non-parametric model is proposed to characterize BWCS channels, it has been demonstrated that it is an important supplement to the existing parametric models; and then, compressive sensing technique is applied to the on-body UWB channel estimation, the impulse response of the channel is perfectly reconstructed; finally, particle swarm optimization based support vector regression technique is used to explore obesity’s effect on the on-body narrowband wireless channels. This chapter provides readers a totally new angle of view of looking at the current channel modelling technique in BWCS; thus will be beneficial to the ones who aim to developnew radio channel models for BWCS.

Original language	English
Title of host publication	Advances in Body-Centric Wireless Communication
Subtitle of host publication	Applications and State-of-the-Art
Publisher	Institution of Engineering and Technology
Pages	81-95
Number of pages	15
ISBN (Electronic)	9781849199902
ISBN (Print)	9781849199896
DOIs	https://doi.org/10.1049/PBTE065E_ch4
State	Published - 1 Jan 2016
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Body centric radio channels
Bwcs channels
Channel modelling technique
Compressed sensing
Compressive sensing technique
Impulse response
On-body uwb channel estimation
Onbody narrowband wireless channels
Parametric models
Particle swarm optimisation
Particle swarm optimization
Radio networks
Regression analysis
Sparse characterization
Support vector regression technique
Wireless channels

Access to Document

10.1049/PBTE065E_ch4

Cite this

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title = "Sparse characterization of body-centric radio channels",

abstract = "In this chapter, sparse characterization of BWCS is discussed. First of all, a novel sparse non-parametric model is proposed to characterize BWCS channels, it has been demonstrated that it is an important supplement to the existing parametric models; and then, compressive sensing technique is applied to the on-body UWB channel estimation, the impulse response of the channel is perfectly reconstructed; finally, particle swarm optimization based support vector regression technique is used to explore obesity{\textquoteright}s effect on the on-body narrowband wireless channels. This chapter provides readers a totally new angle of view of looking at the current channel modelling technique in BWCS; thus will be beneficial to the ones who aim to developnew radio channel models for BWCS.",

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author = "Xiaodong Yang and Aifeng Ren and Zhiya Zhang and Abbasi, \{Qammer Hussain\} and Erchin Serpedin and Wei Zhao and Shuyuan Yang and Akram Alomainy",

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AU - Yang, Xiaodong

AU - Ren, Aifeng

AU - Zhang, Zhiya

AU - Abbasi, Qammer Hussain

AU - Serpedin, Erchin

AU - Zhao, Wei

AU - Yang, Shuyuan

AU - Alomainy, Akram

N1 - Publisher Copyright: © The Institution of Engineering and Technology 2016.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - In this chapter, sparse characterization of BWCS is discussed. First of all, a novel sparse non-parametric model is proposed to characterize BWCS channels, it has been demonstrated that it is an important supplement to the existing parametric models; and then, compressive sensing technique is applied to the on-body UWB channel estimation, the impulse response of the channel is perfectly reconstructed; finally, particle swarm optimization based support vector regression technique is used to explore obesity’s effect on the on-body narrowband wireless channels. This chapter provides readers a totally new angle of view of looking at the current channel modelling technique in BWCS; thus will be beneficial to the ones who aim to developnew radio channel models for BWCS.

AB - In this chapter, sparse characterization of BWCS is discussed. First of all, a novel sparse non-parametric model is proposed to characterize BWCS channels, it has been demonstrated that it is an important supplement to the existing parametric models; and then, compressive sensing technique is applied to the on-body UWB channel estimation, the impulse response of the channel is perfectly reconstructed; finally, particle swarm optimization based support vector regression technique is used to explore obesity’s effect on the on-body narrowband wireless channels. This chapter provides readers a totally new angle of view of looking at the current channel modelling technique in BWCS; thus will be beneficial to the ones who aim to developnew radio channel models for BWCS.

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KW - Bwcs channels

KW - Channel modelling technique

KW - Compressed sensing

KW - Compressive sensing technique

KW - Impulse response

KW - On-body uwb channel estimation

KW - Onbody narrowband wireless channels

KW - Parametric models

KW - Particle swarm optimisation

KW - Particle swarm optimization

KW - Radio networks

KW - Regression analysis

KW - Sparse characterization

KW - Support vector regression technique

KW - Wireless channels

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BT - Advances in Body-Centric Wireless Communication

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