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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Veröffentlicht in:IET communications Jg. 10; H. 17; S. 2452 - 2460
Hauptverfasser: Liu, Xiaolan, Hu, Fengye, Xiong, Wei, Du, Yu, Cen, Ling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 24.11.2016
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2016.0458