Femto-Assisted Location Estimation in Heterogeneous Networks
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Solution Overview
Problem
Current location estimation techniques in wireless communication networks, such as GPS, are ineffective in non-line-of-sight environments like indoor settings due to poor connectivity and signal interference, leading to inaccurate user equipment (UE) positioning.
Innovation Solution
The implementation of a femto-assisted location estimation (FALE) scheme using particle filtering, which combines macro and femto base station information to determine UE location through time difference of arrival (TDOA) measurements, employing a particle filter to handle non-linear estimation and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS techniques are used for location estimation, then positioning accuracy is improved in outdoor LOS environments, but location estimation becomes unreliable in indoor NLOS environments
Solution Approach 1:
The patent introduces femto base stations as intermediary nodes between the UE and the network core. These femto BSs perform TDOA measurements and provide location information to the eNodeB, which then estimates the UE position. This intermediary approach enables reliable indoor positioning where direct GPS signals are unavailable.
Solution Approach 2:
The patent segments the location estimation function across multiple components: femto base stations perform local TDOA measurements, the eNodeB receives measurements from multiple femto BSs, and the server performs the final position calculation using particle filtering. This segmentation allows each component to specialize and improves overall system reliability.
2Device complexity
If conventional Cell ID techniques are used, then device complexity is reduced, but location estimation accuracy deteriorates in heterogeneous networks
Solution Approach 1:
The patent makes the eNodeB and server perform multiple functions: they handle conventional cellular communication tasks while simultaneously performing TDOA measurements, receiving location reports from femto BSs, executing particle filtering algorithms, and calculating UE positions. This multi-functionality improves accuracy without requiring separate dedicated hardware.
Solution Approach 2:
The patent changes the fundamental parameter used for position estimation from simple cell identifier (Cell ID) to time difference of arrival (TDOA) measurements. This parameter change enables accurate positioning in heterogeneous networks by utilizing the temporal information from multiple femto base stations.
3Measurement precision
If particle filtering is applied to handle non-linear estimation, then location estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies particle filtering selectively rather than universally. The algorithm is activated only when location estimation is required, and the number of particles used can be adjusted based on accuracy requirements. This partial application reduces overall computational burden while maintaining high accuracy when needed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The FALE scheme enhances UE location estimation accuracy in indoor environments by leveraging both macro and femto base station data, reducing computational complexity and providing better positioning compared to conventional methods like Cell ID techniques.
Implementation Method 1
combines macro and femto base station information to determine UE location through time difference of arrival (TDOA) measurements
Data Source
AI summary
The disclosed subject matter relates to a wireless communications environment. Femto-assisted location estimation schemes can determine UE location information based on information from either macro base stations or femto base stations in a LTE-A heterogeneous network. Positions information for a femto base station can be employed even though the exact positions of fBS may not be available. The femto base station position information can be depicted as a probabilistic distribution. Bayesian estimation based on TDOA and utilization of a particle filter can facilitate determining UE location information. Furthermore, a simplified scheme can reduce computational complexity.