Mobile Terminal Location for MIMO Communication Systems

Li, Ji; Conan, J.; Pierre, Samuel

doi:10.1109/tap.2007.901862

Cited by 27 publications

(39 citation statements)

References 7 publications

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“…Under these assumptions, the position estimation errors are small. Each estimation error is modelled as a zero-mean normally distributed random variable, [36][37][38][39]. In the simulations performed, white Gaussian noise with standard deviation set to one degree, [36,38,39], was added to each AoA measurement.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

A Simple 3-D Geometric Channel Model for Macrocell Mobile Communications

Baltzis

Sahalos

2008

Wireless Pers Commun

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One of the fundamental research areas in wireless communications is the development of realistic models that can efficiently and accurately describe the wireless propagation channel. Most of the proposed models disregard the three dimensional character of the signal spread or use techniques with excessive computational complexity. In this paper, we develop a simple 3-D geometric scattering model for the uplink of a macrocell mobile environment that provides the statistics of Angle-of-Arrival (AoA) of the multipath components. The model extends the 2-D geometrical-based single bounce macrocell (GBSBM) model. Explicit closed-form expressions are derived for the statistics of the AoA of the multipaths in the azimuth and elevation planes. Analysis of the results exhibits the advantages of our proposal compared to 2-D and 3-D ones published in the literature. Comparisons with experimental data confirm its validity. Interesting conclusions for the effective evaluation of mobile communication systems have been derived. Moreover, an application of the model to mobile location estimation has been developed and evaluated.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

A Simple 3-D Geometric Channel Model for Macrocell Mobile Communications

Baltzis

Sahalos

2008

Wireless Pers Commun

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“…In total, there will be three paths from the RDs for each performance analysis. For thorough comparison with existing conventional localization schemes, each actual data metrics will be subjected to noise effect using Gaussian random variable noise with a mean of zero, and unknown variance (variances of all devices are identical unless otherwise specified) [18,19,[21][22][23][24].…”

Section: Resultsmentioning

confidence: 99%

“…There are also hybrid localization techniques that use a combination of the above metrics such as fusion of TOA and AOA data to achieve the same ends [18][19][20]. In the bi-directional NLOS approaches [21][22][23][24] which are the emerging novel techniques, both the TOA and AOA measurement data for either the LOS or one bound scattering (multipath that undergoes one bound scattering phenomenon) paths or both paths at both reference and mobile devices are leveraged on for location estimation. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…MIMO exploits antenna arrays [35][36][37][38][39][40][41][42][43][44][45][46] that are coupled with smart antenna technology at both reference and mobile devices which facilitate the AOA estimation at both ends (referred to as bi-directional estimation). Localization using such principle has been explored in [21][22][23][24]. In our earlier papers [21,22], comprehensive bi-directional least square localization schemes have been designed that are based on the TOA and AOA measurement data of the LOS [21,22] and one bound scattering NLOS [21] paths at both the reference and mobile devices.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple bound scattering NLOS multipaths have been successfully rejected through the designed proximity and multipath rejection scheme [21]. Although the bi-directional approaches [21][22][23][24] with just one reference device has been able to localize the mobile device much better than the conventional LOS approach that use three reference devices in multipath environment, the cost of using antenna array at the mobile device in term of computation and physical implementation for localization is expensive.…”

Section: Introductionmentioning

confidence: 99%

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Localization of Omni-Directional Mobile Device in Multipath Environments

Seow¹,

Tan²

2008

PIER

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Abstract-This paper presents a comprehensive Non Line of Sight (NLOS) localization scheme in a multipath environment where the scatterers with smooth surfaces are aligned parallel or perpendicular to each other. It leverages on the estimation of Angle of Arrival (AOA) and Time of Arrival (TOA) of the omni-directional mobile device's signal received at the reference devices. Unlike the conventional Line of Sight (LOS) localization schemes that rely on the various mitigation techniques to mitigate the multipaths that are mistaken as the LOS signal, our proposed two step localization scheme not only utilizes the LOS path but also any one bound scattering NLOS multipath arriving at the reference devices for localization. Channel experiment coupled with simulation results in a typical multipath environment has demonstrated that our proposed localization scheme outperforms the conventional localization schemes that are coupled with their own mitigation techniques. Robustness in performance of our proposed localization scheme towards different scatterers' orientation where they are not aligned parallel or perpendicular to each other are also investigated.

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Antenna-Array-Assisted Frequency Offset Estimation and Data Detection in an Uplink Multiuser MIMO-OFDM Interference Network

Fang

Chen

2009

Wireless Pers Commun

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Due to the popularity of IEEE 802.11a/g/n wireless local area networks, the high-density deployment of access points and their serious mutual interference have become a pressing concern, and made both frequency acquisition and data detection even more difficult. In addition, improving the network coverage and scalability in the mesh mode of IEEE 802.16 wireless metropolitan area network, space and frequency information can provide abundant scheduling information under the configuration. In light of this, this paper presents an antenna-array-assisted algorithm to solve these two problems in a multiuser multipleinput-multiple-output orthogonal frequency division multiplexing interference network. The algorithm begins with the estimation of three channel parameters: frequency offsets, delays and angle selectivity. To make a good use of the array signal characteristics, these three parameters are estimated in a frequency/delay-angle-frequency/delay (FAF) tree structure, in which two frequency/delay estimations and one angle estimation are employed alternatively. One special feature in the FAF tree structure is that temporal filtering or spatial beamforming is invoked between the parameter estimations to decompose signals so as to enhance the estimation accuracy. Thereafter, based on these parameter estimates, a data detection procedure is developed to mitigate both multiple access interference (MAI) and co-channel interference (CCI). Simulations show that the proposed algorithm can provide satisfactory performance even in networks with MAIs and CCIs sharing the same frequency band.

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Mobile Terminal Location for MIMO Communication Systems

Cited by 27 publications

References 7 publications

A Simple 3-D Geometric Channel Model for Macrocell Mobile Communications

A Simple 3-D Geometric Channel Model for Macrocell Mobile Communications

Localization of Omni-Directional Mobile Device in Multipath Environments

Antenna-Array-Assisted Frequency Offset Estimation and Data Detection in an Uplink Multiuser MIMO-OFDM Interference Network

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