Machine-Type Communication (MTC) is an integral use case for future generation of cellular networks, where fully automated data generation, exchange, processing and actuation among intelligent machines is foreseen. The objective of this thesis is to develop and analyze resource allocation strategies to efficiently meet the stringent requirements of diverse MTC scenarios. We exploit the concepts of power control, rate allocation, relaying cooperation, spatial diversity, aggregation and Non-Orthogonal Multiple Access (NOMA), in the context of such new and challenging scenarios. An overarching objective is to carry out both theoretical performance analysis and algorithm development of the proposed strategies, while focusing on three relevant MTC scenarios.
First, we analyze interference-free setups where Machine-Type Devices (MTDs) are powered via Wireless Energy Transfer (WET). Several mechanisms are proposed and analyzed for enabling Ultra-Reliable Low-Latency Communications (URLLC). Second, we propose power and rate control strategies for large-scale interference-limited networks under reliability and delay constraints. We explore Orthogonal Multiple Access (OMA) and NOMA schemes, for which our proposed schemes depend only on the target reliability and latency, and on average statistics of the signal and interference. Third, we analyze data aggregation and resource scheduling mechanisms for massive MTC (mMTC). We propose NOMA and develop an analytical framework based on Stochastic Geometry to investigate its performance. For the scenario on which MTDs are powered via WET, we propose Channel State Information (CSI)-free multiple-antenna strategies to power the massive set of MTDs.
All in all, our proposed techniques were demonstrated to have great potential to improve performance metrics such as reliability, latency, energy efficiency and scalability with respect to the number of simultaneously served devices. We expect that our novel ideas influence the development and implementation of new and more sophisticated strategies for coping with the increasingly growing quality of service demands of future networks.