Neural Network Handoff Algorithm in a Joint Terrestrial-HAPS Cellular System

Handoff algorithm is used in wireless cellular systems to decide when and to which base station (BS) to handoff in order that the services can be continued uninterrupted. In this paper, we propose a handoff algorithm based on the neural network in a joint system of terrestrial and high altitude platform station (HAPS) cellular systems. As a revolutionary wireless system, HAPS can supply services for uncovered area improving total capacity of service-limited area by a terrestrial BS. Radial-Basis function (RBF) network is used for making a handoff decision to the chosen neighbor BS. The neural inputs consist of the averaged signal strength received from the serving and nearby BSs, user directions estimated by the MUSIC algorithm on an antenna array, and traffic intensities. Positioning a mobile station (MS) is obtained by apply the timing advance (TA) concept. Performance comparisons among the presented method and using the backpropagation (BP) neural network and the conventional Hysteresis method are given in forms of (1) handoff rate, blocking rate, dropping rate at the acceptable grade of service and (2) the difference between the signal power radiated by the serving BS and the minimum required received signal power before call dropping for both Hata model and Rayleigh fading.