NB-IoT M2M Communications in 5G Cellular Networks

Resumo

Cellular networks are continuously evolving to provide enhanced capabilities and widen the supported use cases beyond the initial focus on mobile broadband. Recently, this evolution has paved the way to support the Internet of Things (IoT). The inclusion of IoT in the cellular networks, denoted as Cellular IoT (CIoT), is bringing a larger and more extensive ecosystem of use cases than ever to cellular networks. The next cellular generation, denoted as 5G, already considers in its design this foreseen connectivity heterogeneity. However, previous generations have to be optimized to cope with these new requirements. To meet this challenge, the Third Generation Partnership Project (3GPP) has standardized three solutions for CIoT: i) Extended Coverage GSM IoT (EC-GSM-IoT), ii) Long Term Evolution Cat-M1 (LTE-M), iii) Narrowband Internet of Things (NB-IoT). These solutions are Low-Power Wide-Area (LPWA) technologies. That means their design goals are extended coverage, low power and low cost devices, and massive connections. Particularly for LTE-M and NB-IoT, they are standards that can be deployed today to serve LPWA use cases and will become part of the 5G family. NB-IoT defines a new radio access technology based on LTE and is specifically tailored for ultra-low-end IoT applications. In addition to the emerging IoT use cases, IoT is built upon Machine-Type Communication (MTC), also denoted as Machine to Machine (M2M) communications. The characteristics of MTC traffic greatly differ from human-generated traffic. For example, both differ in the uplink and downlink traffic loads, temporal distribution of the traffic, traffic profiles (MTC usually follows a periodic or bursty traffic), or mobility. Within this context, the main objective of this thesis is to study the inclusion of massive MTC (mMTC) into cellular networks. More precisely, the use NB-IoT to support mMTC within the cellular networks. First, the signaling impact due to MTC in the current cellular network (i.e. 4G), is studied. To that end, a new architecture for the main control plane entity of the core is assumed. This new architecture is based on Network Functions Virtualization (NFV) and the studied entity is the Mobility Management Entity (MME). The analytical model is based on queuing theory. In the study, four possible designs are proposed and three traffic classes are considered: mobile broadband, mMTC, and low latency MTC. The evaluation is carried out considering the resources needed for dimensioning, the cost of the system, and the response time of each traffic class assumed. The results show the level of resource sharing and the target design traffic significantly impact the performance of each traffic class and the number of resources needed. Second, an analytical study of the NB-IoT coverage extension performance. To that end, the evaluation includes all available NB-IoT techniques applied to achieve the target of 164 dB Maximum Coupling Loss (MCL). The proposed analytical expressions are based on the Shannon theorem. The analysis includes the limitations due to realistic channel estimation. The results show the performance of the Signal to Noise Ratio (SNR) gain when doubling repetitions is significantly affected when assuming realistic channel estimation compared to ideal channel estimation. Consequently, NB-IoT devices in weak coverage condition will be challenging to reach even considering the novel NB-IoT techniques to extend coverage. Third, analytical and experimental NB-IoT performance evaluations are developed. The analytical evaluation is based on Markov chains and the experimental evaluation uses a controlled testbed. This testbed consists of commercial NB-IoT devices connected to a base station emulator. The NB-IoT performance evaluation is done in terms of the device's battery lifetime and latency. Using the testbed, the NB-IoT devices are studied empirically and later the proposed analytical model is experimentally validated. To that end, different traffic and coverage scenarios are considered. The validation results show the analytical model performs well compared to the empirical measurements under the same configuration in both cases. The results reach a maximum relative error of the battery lifetime estimation between the model and the measurements of 21% for an assumed Inter-Arrival Time (IAT) of 6 min. This relative error can be further reduced if larger IATs are considered or the model simplifications assumed are specified in the model. Additionally, the results demonstrate NB-IoT devices can achieve the targets of 10 years of battery lifetime or 10 seconds of uplink transmission latency for a large range of scenarios when the traffic profile has a large IAT, or the configuration of the radio resources do not require an extensive number of repetitions.