Sökning: "Cellulärt nätverk"

Hittade 4 uppsatser innehållade orden Cellulärt nätverk.

  1. 1. Assessing the threat of Stingrays in 4G cellular networks

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Emil Karim; Sina Khoraman; [2023]
    Nyckelord :Cellular network; Wireless communication; Stingray; Privacy; Surveillance; Tracking; 4G; Software-defined radio; Cellulärt nätverk; Trådlös kommunikation; Stingray; Integritet; Övervakning; Spårning; 4G; Mjukvarudefinierad radio;

    Sammanfattning : This bachelor thesis explores the threat of Stingrays, fake cell towers, to the mobile network. The increasing availability of open-source technology and hardware has made it easier to build Stingrays. LÄS MER

  2. 2. Optimization of Physical Uplink Resource Allocation in 5G Cellular Network using Monte Carlo Tree Search

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Gerard Girame Rizzo; [2022]
    Nyckelord :5G NR; PUCCH format; Combinatorial optimization; Physical resource allocation; Monte Carlo Tree Search; 5G NR; PUCCH-format; kombinatorisk optimering; fysisk resursfördelning; Monte Carlo Tree Search;

    Sammanfattning : The Physical Uplink Control Channel (PUCCH), which is mainly used to transmit Uplink Control Information (UCI), is a key component to enable the 5G NR system. Compared to LTE, NR specifies a more flexible PUCCH structure to support various applications and use cases. LÄS MER

  3. 3. Time to Next Flow Classification in Mobile Networks with Federated Learning

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Alex Knight-Williams; [2020]
    Nyckelord :;

    Sammanfattning : Understanding traffic dynamics and user demand in a cellular network is essential for effective resource management, which in turn improves the network’s energy and cost efficiency. This thesis focuses on the task of classifying the time until the arrival of the next flow at a user level in a real network traffic data set. LÄS MER

  4. 4. Network Drone Control using Deep Reinforcement Learning

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Alex Hermansson Grobgeld; [2020]
    Nyckelord :;

    Sammanfattning : In this work, a reinforcement learning approach is adopted to control a drone in a cellular network. The goal is to find paths between arbitrary locations such that low radio quality areas, defined with respect to signal-to-interference-plus-noise-ratio, are avoided with the cost of longer flight paths. LÄS MER