Sparse array synthesis for WBAN with minimised side lobe via convex optimisation

This study concerns sensor selection problem in wireless body area network (WBAN), which can reduce the burden of the human body and improve the energy efficiency. The authors highlight the sparse sensor array synthesis algorithm for different shapes of sensor arrays via convex optimisation to solve...

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Vydané v:	IET communications Ročník 10; číslo 17; s. 2452 - 2460
Hlavní autori:	Liu, Xiaolan, Hu, Fengye, Xiong, Wei, Du, Yu, Cen, Ling
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 24.11.2016
Predmet:	Algorithms body area networks Computer simulation convex optimisation convex programming energy conservation energy efficiency improvement l1‐norm Lobes minimised side lobe Optimization regular cuboid sensor arrays regular spherical sensor arrays Sensor arrays sensor distribution simulation sensor placement sensor selection problem Sensors sparse sensor array synthesis algorithm Synthesis WBAN weight vector wireless body area network Wireless networks convex optimisation energy efficiency improvement regular cuboid sensor arrays weight vector l1-norm convex programming body area networks minimised side lobe sensor selection problem wireless body area network sensor arrays WBAN sensor distribution simulation sensor placement sparse sensor array synthesis algorithm energy conservation regular spherical sensor arrays
ISSN:	1751-8628, 1751-8636
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Shrnutí:	This study concerns sensor selection problem in wireless body area network (WBAN), which can reduce the burden of the human body and improve the energy efficiency. The authors highlight the sparse sensor array synthesis algorithm for different shapes of sensor arrays via convex optimisation to solve this problem. For simplicity, one regular spherical sensor array and five regular cuboid sensor arrays are considered to simulate the sensor distribution around the human body. As a comparison, the conventional method with one objective method is used to synthesise the sensor array first. Then the proposed algorithm which includes two objective variables: the l1-norm of weight vector and the peak side-lobe level is used. Simulations demonstrate that the proposed sparse sensor array synthesis algorithm achieves array sparsity with lower side lobe and more concentrated main lobe when operated in spherical and cuboid sensor array. Hence, the sensor selection problem is achieved by the sparse sensor array synthesis.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1751-8628 1751-8636
DOI:	10.1049/iet-com.2016.0458