Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems [adtive read as adaptive]

This paper presents a new model for the propagation prediction for mobile communication network inside building. The model is based on the determination of the dominant paths between the transmitter and the receiver, diffraction at the corner and wave-guiding effect. The field strength is predicted with adaptive neuro-fuzzy inference systems (ANFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved.