Non-Line-Of-Sight conditions pose a major challenge to cellular radio positioning. Such conditions, when the direct Line-Of-Sight path is blocked, result in additional propagation delay for the signal, additional attenuation, and an angular bias. Therefore,many researchers have proposed various algorithms to mitigate the measured error caused by this phenomenon. This paper presentsthe procedure for improving accuracy of determining the mobile station location in cellular radio networks in Non-Line-of-Sightpropagation environment, based on the Time Of Arrival oriented estimator using the Particle Swarm Optimization algorithm. Incomputer science, Particle Swarm Optimization is an evolutionary computational method that optimizes a problem by iteratively tryingto improve a candidate solution with regard to a given measure of quality. The proposed algorithm uses the repeating Time-Of-Arrivaltest measurements using the four base stations and for simulation selects the measurement combination that give the smallest regionenclosed by the overlap of four circles. In this way, the smallest intersect area of the four Time-Of-Arrival circles is obtained, andtherefore the smallest positioning error. After that, we consider the complete problem as a combinatorial optimization problem withthe corresponding object function that represents the nonlinear relationship between the intersection of the four circles and the mobilestation location. The Particle Swarm Optimization finds the optimal solution of the object function and efficiently determines themobile station location. The simulation results show that the proposed method outperforms conventional algorithms such as theWeighted Least Squares and the Levenberq-Marquardt method.
Fixed Channel Assignment and Neural Network Algorithm for Channel Assignment Problem in Cellular Radio Networks
