Faculty of Electrical Engineering and Information Technologies

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    Mobile Edge Computing services with QoS support model for Next Generation Mobile Networks
    (FEEIT, Skopje, 2022)
    Shuminoski, Tomislav
    ;
    Velichkovska, Bojana
    ;
    This paper presents a novel overview in intelligent multi-access QoS mobile edge computing (MEC) for beyond 5G networks and services. There are many challenges faced by the expansion of Cloud networks and Mobile Networks, which can be solved by providing connectivity at the edge of the network, i.e. with Mobile Edge computing networks. The MEC improves overall network performance and reduces end-to-end service delay. Also, the improved advanced QoS model including Machine Learning (ML) algorithm within for next generation of mobile networks and services are proposed. The purpose of the ML algorithm is to understand the traffic activity and determine how the traffic schedule should be made. Given a set of machines and a set of jobs, the model should compute the processing schedule that minimizes specified metrics. The proposed model combines the most powerful features of both Cloud and Edge computing, independent from any existing and future Radio Access Technology, leading to possible better performance utility networks, lower service delay with high QoS provisioning for many used multimedia service. Finally, this paper gives an overview of the existing Mobile Edge Computing technologies and several existing use cases. Undoubtedly, MEC with QoS support is an innovative network paradigm going in 6G, which can essentially answer many of the existing Mobile Networks’ challenges.
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    Mobile Edge Computing services with QoS support for beyond 5G Networks – Use Cases
    (2021-09)
    Nunev, David
    ;
    Shuminoski, Tomislav
    ;
    Velichkovska, Bojana
    ;
    This paper presents a novel research in intelligent multi-access QoS mobile edge computing (MEC) for beyond 5G services. Also, the improved advanced QoS model and architecture for beyond 5G systems and services are proposed. The proposed model combines the most powerful features of both Cloud and Edge computing, independent from any existing and future Radio Access Technology, leading to high performance utility networks with high QoS provisioning for any used multimedia modern service over present and future mobile and wireless networks and systems. Moreover, the proposed architecture will allow applications and network services to be executed at the edge part of the network, giving lower end-to-end delay for the end-user services and applications. Finally, this paper gives an overview of the existing Mobile Edge Computing technologies and several use cases. Undoubtedly, MEC is an innovative network paradigm going beyond 5G to cater for the unprecedented growth of computation demands and the ever increasing computation quality of user experience requirements.
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    Item type:Publication,
    Lyapunov Drift-Plus-Penalty Based Resource Allocation in IRS-Assisted Wireless Networks with RF Energy Harvesting
    (Brno University of Technology, 2022-09)
    ;
    Hadzi-Velkov, Zoran
    ;
    Shuminoski, Tomislav
    We propose a resource allocation policy for intelligent reflective surface (IRS)-assisted wireless powered communication network (WPCN) where the energy harvesting (EH) users (EHUs) have finite energy storage and data buffers, for storing the harvested energy and the input (sensory) data, respectively. The IRS reflecting coefficients for uplink and downlink are chosen to focus the beam towards a specific EHU, but have additional constant phase offsets (different for uplink and downlink) in order to account for the direct link between the base station and the IRS targeted EHU, and the influence to the EH process of other EHUs in downlink. The EHUs acquire data from their sensors, receive energy in downlink and send information in uplink. We maximize the overall average amount of sensor information in the WPCN by optimizing the IRS reflecting coefficients for the downlink transmissions, the amount of acquired sensor information and the duration of the information transmission period for each EHU in each epoch using the Lyapunov drift-plus-penalty optimization technique. The simulation results demonstrate the effectiveness of the proposed solution.