زيد البدير

In recent years, determining the position of a target has gained great importance. Because the number of mobile devices, Internet of Things and overall demand for location-based services (LBS) are increasing. One of the most popular methods for location is the fingerprint approach based on the Received Signal Strength Indicator (RSSI). This approach is cost-effective due to its simplicity and compatibility with existing hardware and network infrastructures and has received much attention. RSSI values are dependent on environmental conditions and are affected by interference and noise, which reduces the accuracy of location measurement. Achieving as much accuracy as possible in centimeter units is one of the main challenges and goals in the positioning industry. To achieve this, machine learning algorithms and especially deep learning are used to analyze complex data such as RSSI values and reduce environmental effects. Collecting and processing effective parameters related to location estimation, combined methods and capabilities that the fifth generation network provides to users are suggested to achieve this goal. In this research, after examining the location methods based on RSSI in cellular networks and related works in this field, a location algorithm based on Friss relations is presented to estimate the location of a cell. Then, its implementation and eva‎luation methods were discussed in order to eva‎luate the accuracy of this method.

مكان يابي با استفاده از قدرت سيگنال (RSSI) , شبكه هاي سلولي , شبكه هاي بي سيم , بكارگيري هوش مصنوعي در مكان يابي , روش اثرانگشت

Localizaition using signal strength (RSSI) , Cellular Networks , wireless networks , Using artificial intelligence in positioning , Fingerprint Method

Zeid Albadir

Dr. Abolfazl Diyanat