Enabling Massive Device Connectivity in 5G Cellular Networks

Abstract

With the introduction of Internet of Things (IoT), millions of devices are expected to be connected to wireless networks to provide diverse types of services including remote con- trol, surveillance, detection, sensing, etc. As a result, supporting massive connectivity with various functionalities becomes an essential task in building the future cellular networks. Mo- tivated to fulfill such a demand, this dissertation consisting of two parts investigate the roles of different advanced wireless communication techniques on enabling massive connectivity for 5G cellular networks. In the first part (Chapters 2 and 3), random access, spectrum sensing and cognitive radio (CR) are combined to solve the network overloading and packet scheduling issues in nar- rowband (NB), low data rate and high delay tolerance cellular networks. Optimal sensing parameters are derived to maximize the network throughput for different sensing mecha- nisms. Trade-offs between the random access narrowband cognitive radio (NB-CR) network throughput and the sensing accuracy under different sensing environments are also investi- gated. In the second part (Chapters 4 and 5), Massive MIMO technique is employed to support massive connectivity in broadband, high data rate and real-time networks. A single-carrier system architecture applying spatial temporal zero forcing linear equalizers (ST-ZF-LE) is proposed to mitigate frequency-selective fading in Massive MIMO systems. A quantifica- tional analysis is presented to reveal the relationships between system parameters and per- formance in terms of degrees of freedom (DoFs) and signal to interference plus noise ratio (SINR). Closed-form expressions of the ergodic data rate and the outage probability are then derived for both single-cell systems and multi-cell systems with perfect and imperfect channel state information (CSI).