A Scalable Hidden-Markov Model Algorithm for Location-Based Services in WiMAX Networks

Hidden-Markov Models (HMM) have shown promise as viable solutions to providing location based services (LBS) within cellular networks. Previously established work includes a scheme to merge the stochastic contribution of the HMM and maximum likelihood decisions based on signal strength measurements and timing adjust parameters. A novel scalable positioning algorithm that utilizes the aforementioned techniques along with reorientation of the state vector in order to favor the local measurements within the area of interest is proposed in this paper. The resulting scheme is presented and its performance validated through simulations built from a scenario based on a real world WiMAX network. The results demonstrate improved performance over previous work, and the effect of scaling the algorithm is discussed.