Neural Network based Non Orthogonal Random Access for 6G NTN-IoT

Mohammadi Georganaki, Ali (2022) Neural Network based Non Orthogonal Random Access for 6G NTN-IoT. [Laurea magistrale], Università di Bologna, Corso di Studio in Telecommunications engineering [LM-DM270]
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Pervasive and distributed Internet of Things (IoT) devices demand ubiquitous coverage beyond No-man’s land. To satisfy plethora of IoT devices with resilient connectivity, Non-Terrestrial Networks (NTN) will be pivotal to assist and complement terrestrial systems. In a massiveMTC scenario over NTN, characterized by sporadic uplink data reports, all the terminals within a satellite beam shall be served during the short visibility window of the flying platform, thus generating congestion due to simultaneous access attempts of IoT devices on the same radio resource. The more terminals collide, the more average-time it takes to complete an access which is due to the decreased number of successful attempts caused by Back-off commands of legacy methods. A possible countermeasure is represented by Non-Orthogonal Multiple Access scheme, which requires the knowledge of the number of superimposed NPRACH preambles. This work addresses this problem by proposing a Neural Network (NN) algorithm to cope with the uncoordinated random access performed by a prodigious number of Narrowband-IoT devices. Our proposed method classifies the number of colliding users, and for each estimates the Time of Arrival (ToA). The performance assessment, under Line of Sight (LoS) and Non-LoS conditions in sub-urban environments with two different satellite configurations, shows significant benefits of the proposed NN algorithm with respect to traditional methods for the ToA estimation.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Mohammadi Georganaki, Ali
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
6G,NTN,mMTC,NB-IoT,NPRACH,Neural Networks
Data di discussione della Tesi
5 Dicembre 2022

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