can be achieved. This is because more antennas give a higher system DoF; therefore, less power is sufficient for information detection. 6.5 Summary This chapter extends Chapter 5 and considered a more realistic Gaussian CSI estimation model. A similar min power and max EH problem is formulated followed by solutions and numerical results. As for future research directions, the system model can be generalized to account for more use cases, for example, considering the physical layer security and the interference arising from multiple cells. Mobile Edge Computing in 5G Wireless Networks 7.1 Background In Chapters 2-6, we mainly addressed the spectral and large number of device connection challenges for the future 5G systems. However, the ever-increasing demand for various applications such as gaming, autonomous driving, and augmented reality/virtual reality (AR/VR) have been recognized as one of the driving forces for the prosperity of the smart devices [142]. Due to the limitations on size, battery, and cost, these small-size smart devices can experience performance bottleneck when computation-intensive tasks need to be exe- cuted. One option is to deploy centralized services such as cloud centers to help the data processing. However, cloud servers can be located far away, which can inevitably cause longer end-to-end transmission delay [142]. In contrast to the centralized infrastructure, recent network paradigms such as MEC tend to allocate resources to devices in close proximity for joint processing. For example, the work in [181] used unmanned aerial vehicles (UAVs) to help D2D wireless networks [109]. This paradigm shift can effectively reduce the long backhaul latency and energy consumption, as well as support a more flexible infrastructure in a cost-effective way. Furthermore, MEC together with virtual machine (VM) migration can effectively increase the scalability [154] while reducing service delay [153]. Due to these advantages, MEC has attracted extensive research attention in various vertical segments. On