IOT-5G USER TRACKING IN A 5G NETWORK USING 60 GHZ MM-WAVES BASED ON AN ABF-ED ALGORITHMS FOR A CLUTTERED INDOOR ENVIRONMENT

Abstract. This paper presents a user tracking algorithm in an IoT-5G Network (or IoT-5GN). Hereby, we aim at studying and evaluating the sensing performances of the IoT-5G Access Point (or IoT-5G AP) primary signal by the IoT-5G user in a cluttered indoor environment using an energy detector (or ED) algorithm and an Alpha-&-Beta Filter (ABF or α-β-F) estimator. The 5G primary signal (or 5G-PS) frequency that we would like to detect is: 60 GHz. As a result, the 5G-PS sensing via the proposed ABF-ED algorithm, enabled us to track the IoT-5G user inside of the IoT-5G AP coverage area. The performances of the proposed ABF-ED algorithm in this paper work is evaluated by the probability of total detection error (or PTDE) measure. Through different scenarios simulations, the performances and robustness of the proffered algorithm are demonstrated.