ements and their technical challenges. Then the enabling technolo- gies from NR, mobile edge computing, and heterogeneous communication architecture are illustrated. Chapter 2 discusses the integration of 5G networks with device-to-device (D2D) com- munication. Specifically, the 5G system with underlaid D2D is presented. We show that such system can increase spectral efficiency, providing that resource allocation is properly designed. Chapter 3 deals with NOMA-enabled practical wireless networks. The highlight is the integration of error propagation, a well-known issue in NOMA. It shows that error propa- gation can degrade system performance, depending on the residual value. Chapter 4 presents 5G relay and IoT networks with NOMA. In the first part, we derive the outage probability in the relay system and show the potential of such a configuration. Then, in the second part, the IoT network with power transfer capability is considered. Preface Chapter 5 discusses the robust beamforming problem in cognitive radio system; we specif- ically illustrate the beamforming design when bounded channel estimation error is present. Chapter 6 is a continuation of Chapter 5. It considers a more realistic channel estimation model, in which channel estimation error is modeled as Gaussian variable. Correspond- ingly, beamforming design also changes. Chapter 7 presents mobile edge computing in 5G wireless networks. The system aims at reducing computing latency and offload computation tasks to nearby edge servers. With the goal of maximize computation efficiency, resource allocation optimization is proposed and designed. Chapter 8 further considers security enhancements in mobile edge computing. Our secu- rity design focuses on physical layer, i.e. from wire-tap channel perspective. Chapter 9 deals with an innovative wireless system to facilitate distributed machine learn- ing as opposed to machine learning for wireless communication. We show that efficient information exchange via wireless can accelerate large-scale distributed mach