An Efficient Machine Learning Approach for Indoor Localization

Indoor localization has gained much attention over several decades due to enormous applications. However, the accuracy of indoor localization is hard to improve because the signal propagation has small scale effects which leads to inaccurate measurements. In this paper, we propose an efficient learning approach that combines grid search based kernel support vector machine and principle component analysis. The proposed approach applies principle component analysis to reduce high dimensional measurements. Then we design a grid search algorithm to optimize the parameters of kernel support vector machine in order to improve the localization accuracy. Experimental results indicate that the proposed approach reduces the localization error and improves the computational efficiency comparing with K-nearest neighbor, Back Propagation Neural Network and Support Vector Machine based methods.

Performance of Relay Networks in Fading Environments with Dominant Specular Components

Radio propagation in dense and super dense wireless networks as well as indoor-to-outdoor picocell networks can have multiple line-of-sight or multiple specular components. The performance of a dual-hop decode-and-forward relaying system over multiple specular components fading channels(MSCC)with multiple Rayleigh distributed co-channel interferers in an interference-limited environment is investigated. The MSCC fading model is designed to allow direct and meaningful comparisons to be made between line-of-sight channels and non-line-of-sight channels, with exact parameter correspondences. Comparisons of outage and bit error performance between Nakagami-m/Rayleigh and MSCC/Rayleigh fading environments show that the MSCC model is needed to describe line-of-sight channels that cannot be accurately modeled by the Nakagami-m, or other fading models.

Improved Eigenstructure-Based 2D DOA Estimation Approaches Based on Nystrom Approximation

In this paper,we propose improved approaches for two-dimensional(2 D) direction-of-arrival(DOA) estimation for a uniform rectangular array(URA).Unlike the conventional eigenstructure-based estimation approaches such as Multiple Signals Classification(MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique(ESPRIT),the proposed approaches estimate signal and noise subspaces with Nystr?m approximation,which only need to calculate two sub-matrices of the whole sample covariance matrix and avoid the need to directly calculate the eigenvalue decomposition(EVD) of the sample covariance matrix.Hence,the proposed approaches can improve the computational efficiency greatly for large-scale URAs.Numerical results verify the reliability and efficiency of the proposed approaches.

A new TDOA algorithm based on Taylor series expansion in cellular networks

Time difference of arrival(TDOA)is the positioning technique with the most potential in cellular mobile telecommunication systems.The Taylor series expansion method has been widely used in solving nonlinear equations for its high accuracy and good robustness.However,the performance of the Taylor’s method depends highly on the initial estimation.Therefore,one new algorithm,hybrid optimizing algo-rithm(HOA)was proposed,which combines the Taylor series expansion method with the steepest decent method.The steepest decent method features fast convergence at the initial iteration and small computation complexity.HOA takes great advantage of both methods.Simulation results show that HOA achieves better performance on positioning accuracy and efficiency.