Method of and system for optimizing an empirical propagation prediction model in a mobile communications network

Abstract

A propagation prediction model having adjustable parameters is optimized in a mobile communications network, by subdividing a service area into a plurality of map tiles, predicting a tile reliability from the model for each map tile, averaging the predicted tile reliability from all the map tiles to obtain a predicted average service area reliability, measuring a service area reliability for all the map tiles to obtain a measured service area reliability, comparing the predicted average service area reliability with the predicted average service area reliability, and adjusting the parameters of the model when the measured service area reliability differs from the predicted average service area reliability by a predetermined amount.

Description

FIELD OF THE DISCLOSURE[0001]The present disclosure relates generally to a method of, and a system for, optimizing an empirical propagation prediction model in a mobile communications network based on service area reliability and, more particularly, to sequentially tune the model to improve propagation prediction accuracy.BACKGROUND[0002]Mobile communications is experiencing enormous growth, thereby requiring proper planning, expanding, operating and optimizing of mobile communications networks. For example, in a public safety network having one or more base stations in radio communication with land mobile radios (LMRs), both vehicular and handheld, operated by public safety personnel, such as first responders, too few stations may result in spotty or unreliable radio coverage, whereas too many stations are redundant and expensive. A radio signal experiences path loss during propagation between a mobile radio and a network transceiver at a station. Path loss is the attenuation or re...

AI Technical Summary

Benefits of technology

The present disclosure relates to a method and system for optimizing a propagation prediction model in a mobile communications network. The method involves subdividing a service area into map tiles, predicting the reliability of communication in each tile, measuring the reliability of communication in all the map tiles, and adjusting the parameters of the model to improve accuracy. The technical effect of this method is to increase the accuracy of predicting signal strength and reliability in mobile communication networks, which can improve network performance and help ensure proper coverage and reliable communication.

Problems solved by technology

For example, in a public safety network having one or more base stations in radio communication with land mobile radios (LMRs), both vehicular and handheld, operated by public safety personnel, such as first responders, too few stations may result in spotty or unreliable radio coverage, whereas too many stations are redundant and expensive.

Similarly, in a telephone network having one or more cell towers in radio communication with handheld, mobile phones having built-in radio transceivers, too few towers can be as problematic as too many towers, and the radio signal similarly experiences path loss during propagation between a mobile phone and a network transceiver at a tower.

Yet, existing tuning methods that are based solely on signal strength still leaves uncertainty in the accuracy of the propagation prediction as it relates to network performance.

In the Okumura model, Okumura's algorithm (and those of some other models) does not account for propagation in forested environments.

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