Autors: Semov, P. T., Koleva, P. H., Poulkov, V. K.
Title: Adaptive resource scheduling based on neural network and mobile traffic prediction
Keywords: Heterogeneous networks; Neural networks; Scheduling; Traffic prediction; Wireless communications

Abstract: Nowadays with the deployment of a large and dense heterogeneous networks more sophisticated algorithms for resource scheduling are needed. Implementing hard coded scheduling algorithms without taking into account the very specific dynamic of the traffic generated by the mobile users can lead to a network performance quite far from the optimal. By using novel machine learning (ML) algorithms we can store not only the raw traffic data and its variations but also build the so-called heat maps, reflecting the changes of the traffic over time, space and per user. Using neural network (NN) architectures, trained by the raw data statistics, we can store the network traffic model at minimum data storage without the need of keeping and looking up at the raw data. Using such NN architecture the network state in next time intervals could be predicted and this prediction used for decision making about how the network resources to be scheduled among the active mobile users. To implement adaptive..

References

    Issue

    in Proceedings of International Conference on Telecommunications and Signal Processing (TSP), 1-3 July 2019, pp. 585-588, 2019, Hungary, DOI 10.1109/TSP.2019.8769088

    Copyright IEEE

    Цитирания (Citation/s):
    1. Den Eynde, J.V., Verdyck, J., Blondia, C., Moonen, M., "Minimal Delay Violation-Based Cross-Layer Scheduler and Resource Allocation for DSL Networks", IEEE Access, vol. 9, pp. 75905-75922, 2021, DOI: 10.1109/ACCESS.2021.3081793. - 2021 - в издания, индексирани в Scopus или Web of Science
    2. Yu, W., Tongtong, W., "Minimizing Task Completion Time in the Cloud based on Random Neural Network", Proceedings - 2021 International Conference on Computer, Blockchain and Financial Development, CBFD 2021, pp. 60-65, 2021, DOI: 10.1109/CBFD52659.2021.00019. - 2021 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science