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    Wyświetlanie 1-3 z 3
    Tytuł:
    Two-Dimensional Drone Base Station Placement in Cellular Networks Using MINLP Model
    Autorzy:
    Taghavi, Mina
    Abouei, Jamshid
    Powiązania:
    https://bibliotekanauki.pl/articles/227091.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    5G cellular networks
    drone base stations
    mobile base stations
    node placement
    Opis:
    Utilization of drones is going to become predominated in cellular networks as aerial base stations in order to temporary cover areas where stationary base stations cannot serve the users. Detecting optimal location and efficient number of drone-Base Stations (DBSs) are the targets we tackle in this paper. Toward this goal, we first model the problem using mixed integer non-linear programming. The output of the proposed method is the number and the optimal location of DBSs in a two-dimension area, and the object is to maximize the number of covered users. In the second step, since the proposed method is not solvable using conventional methods, we use a proposed method to solve the optimization problem. Simulation results illustrate that the proposed method has achieved its goals.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 4; 701-706
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Design and Analysis of An Improved AODV Protocol Based on Clustering Approach for Internet of Vehicles (AODV-CD)
    Autorzy:
    Ebadinezhad, Sahar
    Powiązania:
    https://bibliotekanauki.pl/articles/1844586.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ad hoc on-demand distance vector routing
    clustering
    internet of vehicles
    vehicular ad hoc networks
    quality of service
    5G wireless networks
    Opis:
    The Internet of Vehicles (IoVs) has become a vital research area in order to enhance passenger and road safety, increasing traffic efficiency and enhanced reliable connectivity. In this regard, for monitoring and controlling the communication between IoVs, routing protocols are deployed. Frequent changes that occur in the topology often leads to major challenges in IoVs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is “clustering”. This study focuses on IoVs’ stability and to create an efficient routing protocol in dynamic environment. In this context, we proposed a novel algorithm called Cluster-based enhanced AODV for IoVs (AODV-CD) to achieve stable and efficient clustering for simplifying routing and ensuring quality of service (QoS). Our proposed protocol enhances the overall network throughput and delivery ratio, with less routing load and less delay compared to AODV. Thus, extensive simulations are carried out in SUMO and NS2 for evaluating the efficiency of the AODV-CD that is superior to the classic AODV and other recent modified AODV algorithms.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 1; 13-22
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    MIMO Beam Selection in 5G Using Neural Networks
    Autorzy:
    Ruseckas, Julius
    Molis, Gediminas
    Bogucka, Hanna
    Powiązania:
    https://bibliotekanauki.pl/articles/2055220.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    5G
    context information
    MIMO beam orientation
    machine learning
    neural networks
    Opis:
    In this paper, we consider cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple input, multiple output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user-equipment to reduce latency in beam/cell identification. Due to high path-loss in mmWave systems, beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, narrow beam must be formed. Thus, a number of possible beam orientations and consequently time needed for the discovery increases significantly when random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptable performance for practical applications. This finding can be useful for user devices with limited processing power.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 4; 693--698
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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