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Proceedings Paper

Application of neural networks to the dynamic spatial distribution of nodes within an urban wireless network
Author(s): William S. Hortos
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Paper Abstract

The optimal location of wireless transceivers or communicating sensor devices in an urban area and within large human-made structures is considered. The purpose of the positioning of the devices is formation of a distributed network, either in a mesh or hub-spoke topology, that achieves robust connectivity of the nodes. Real-world examples include wireless local area networks (LANs) within buildings and radio beacons in an outdoor mobile radio environment. Operating environments contain both fixed and moving interferers that correspond to both stationary and time-varying spatial distributions of path distortion of stationary and transient fading and multipath delays that impede connectivity. The positioning of the autonomous wireless devices in an area with an unknown spatial pattern of interferers would normally be a slow incremental process. The proposed objective is determination of the spatial distribution of the devices to achieve the maximum radio connectivity in a minimal number of iterative steps. Impeding the optimal distribution of wireless nodes is the corresponding distribution of environmental interferers in the area or volume of network operation. The problem of network formation is posed as an adaptive learning problem, in particular, a self-organizing map of locally competitive wireless units that recursively update their positions and individual operating configurations at each iterative step of the neural algorithm. The scheme allows the wireless units to adaptively learn the pattern distribution of interferers in their operating environment based on the level of radio interference measured at each node by an equivalent received signal strength from wireless units within the node's hearing distance. Two cases are considered. The first is an indoor human-made environment where the interference pattern is largely deterministic and stationary and the units are positioned to form a wireless LAN. The second situation applies to an outdoor urban environment, where a fixed number of units on mobile platforms operating in a random spatial distribution of interferers.

Paper Details

Date Published: 6 April 1995
PDF: 13 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205187
Show Author Affiliations
William S. Hortos, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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