Implementation of 3GPP LTE CatM1 Technology in NS3: System Simulation and Performance As the type of transmitted data over communication networks started to shift from the humangenerated to massive MachineType Communications (mMTCs), entirely new applications within the Internet of Things (IoT) market emerged. Recently introduced Cellular IoT (CIoT) communication technologies operating in the licensed frequency spectrum (i.e., Narrowband IoT (NBIoT) and LTEM) complemented the already implemented and fieldtested technologies (e.g., LoRa(WAN) and Sigfox) transmitting data in licenseexempt frequency bands. Owing to the licensed spectrum as well as to the standardization efforts put by the Third Generation Partnership Project (3GPP) and International Telecommunication Union (ITU), the CIoT technologies are supposed to handle the massive number of data transmissions by introducing new categories of communication devices. In this regard, this paper elaborates on the critical parameters of the LTE CatM introduced by 3GPP. Along with the highlights of the technology in question, the implementation of LTE CatM1 in Network Simulator 3 (NS3) is presented. Out of the obtained simulation data, the focus is primarily given to the network capacity for mMTC scenarios while utilizing the LTE CatM technology. What is even more, the sidebyside evaluation with legacy LTELTEA systems is provided.

Characterizing Delay and Control Traffic of the Cellular MME with IoT Support Massive Internetofthings (MIoT) represents one of the main use cases of 5G, as well as one of the most challenging ones. Accordingly, MIoT traffic is given special treatment in the core network, with the Mobility Management Entity (MME) serving both control and data traffic. In this context, it is critical to properly dimension the MME and to adapt its capability to traffic fluctuations. To this end, we present a compact, closed form linking the number of IoT sources with the rate at which bearers are requested from the MME, and such a rate delay incurred by MME operations. Our model represents a valuable tool to make effective, realtime scaling decisions in virtualized cellular core networks.