2012Unpublished venueRequires access

A shadowing-aware Density_Map for location estimation using COB in non-uniformly populated cellular systems

Nejla Ghaboosi, Abbas Jamalipour

Open publisher page 1 citations

Abstract

The presence of non-line-of-sight (NLOS) propagation poses significant problems in positioning a mobile terminal within cellular communication systems. Count-of-Beacon (COB) is a new radiolocation technique where the existence of NLOS propagation does not degrade the precision of the estimated position. However, non-uniformity of subscribers' distribution negatively affects the performance of the COB technique. The Density_Map of each cell can be used to lighten the impact of nonuniform distribution of subscribers. However, the accuracy of the Density_Map is highly reliant on the propagation conditions of the wireless environment. This paper analyzes the sensitivity of the Density_Map to the deviation of the attenuation that signals experience in shadow fading environments. This analysis is then used to redefine the Density_Map. Simulation results are used to verify the validity of the new defined map when the environment is suffering from high shadowing.

About this research paper

What this paper is about

The presence of non-line-of-sight (NLOS) propagation poses significant problems in positioning a mobile terminal within cellular communication systems. Count-of-Beacon (COB) is a new radiolocation technique where the existence of NLOS propagation does not degrade the precision of the estimated position. However, non-uniformity of subscribers' distribution negatively affects the performance of the COB technique. The Density_Map of each cell can be used to lighten the impact of nonuniform distribution of subscribers. However, the accuracy of the Density_Map is highly reliant on the propagation conditions of the wireless environment. This paper analyzes the sensitivity of the Density_Map to the deviation of the attenuation that signals experience in shadow fading environments. This analysis is then used to redefine the Density_Map. Simulation results are used to verify the validity of the new defined map when the environment is suffering from high shadowing.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The presence of non-line-of-sight (NLOS) propagation poses significant problems in positioning a mobile terminal within cellular communication systems. Count-of-Beacon (COB) is a new radiolocation technique where the existence of NLOS propagation does not degrade the precision of the estimated position. However, non-uniformity of subscribers' distribution negatively affects the performance of the COB technique. The Density_Map of each cell can be used to lighten the impact of nonuniform distribution of subscribers. However, the accuracy of the Density_Map is highly reliant on the propagation conditions of the wireless environment. This paper analyzes the sensitivity of the Density_Map to the deviation of the attenuation that signals experience in shadow fading environments. This analysis is then used to redefine the Density_Map. Simulation results are used to verify the validity of the new defined map when the environment is suffering from high shadowing.

Key concepts: Non-line-of-sight propagation, Shadow mapping, Computer science, Attenuation, Fading, Wireless, Position (finance), Real-time computing

Related papers

Back to paper searchBrowse research topicsOriginal source
A shadowing-aware Density_Map for location estimation using COB in non-uniformly populated cellular systems — Research Paper | ScholarLens