LTE network slicing and resource trading schemes for machine-to-machine communications - Gendy, Sylvia Nader (AUC Knowledge Fountain, 2020)
The Internet of Things (IoT) is envisioned as the future of human-free communications. IoT relies on Machine-to-Machine (M2M) communications rather than conventional Human-to-Human (H2H) communications. It is expected that billions of Machine Type Communication Devices (MTCDs) will be connected to the Internet in the near future. Consequently, the mobile data traffic is poised to increase dramatically. Long Term Evolution (LTE) and its subsequent technology LTE-Advanced (LTE-A) are the candidate carriers of M2M communications for the IoT purposes. Despite the significant increase of traffic due to IoT, the Mobile Network Operators (MNOs) revenues are not increasing at the same pace. Hence, many MNOs have resorted to sharing their radio resources and parts of their infrastructures, in what is known as Network Virtualization (NV). In the thesis, we focus on "slicing" in which an operator known as Mobile Virtual Network Operator (MVNO), does not own a spectrum license or mobile infrastructure, and relies on a larger MNO to serve its users. The large licensed MNO divides its spectrum pool into slices. Each MVNO reserves one or more slice(s). There are 2 forms of slice scheduling: Resource-based in which the slices are assigned a portion of radio resources or Data rate-based in which the slices are assigned a certain bandwidth. In the first part of this thesis we present different approaches for adapting resource-based NV, Data rate-based NV to Machine Type Communication (MTC). This will be done in such a way that resources are allocated to each slice depending on the delay budget of the MTCDs deployed in the slice and their payloads. The adapted NV schemes are then simulated and compared to the Static Reservation (SR) of radio resources. They have all shown an improved performance over SR from deadline missing perspective. In the second part of the thesis, we introduce a novel resource trading scheme that allows sharing operators to trade their radio resources based on the varying needs of their clients with time. The Genetic Algorithm (GA) is used to optimize the resource trading among the virtual operators. The proposed trading scheme is simulated and compared to the adapted schemes from the first part of the thesis. The novel trading scheme has shown to achieve significantly better performance compared to the adapted schemes.