 agree with the simulation results very well. Future work can optimize the power allocation factor as and BS to achieve the best outage performance under different schemes. 4.2 NOMA in a mmWave-Based loT Wireless System with SWIPT Outage probability for UE 2-analytical results Outage probability for UE 2-simulation results 10 -2 Power split coefficient B Figure 4.9 Outage performance for UE 2 as the function of B. In the second part, we consider applying NOMA and D2D relaying in a mm Wave-based wireless system that consists of high-power base stations and low-power IoT devices. The lower-power IoT devices do not have external power supplies and have limited battery life. In order to prolong battery life and also to motivate low-power IoT devices to help relay sig- nals from others, low-power IoT devices can harvest energy from electromagnetic signals. To make the energy harvest model more realistic, non-linear energy harvesting model is used. The theoretical analysis on outage probability is given for the proposed scheme and system model. Simulation results validate the accuracy of the analysis. Robust Beamforming in NOMA Cognitive Radio Networks: Bounded CSI 5.1 Background As a promising technique of improving the spectral efficiency (SE), cognitive radio (CR) techniques have also been investigated for decades, where the secondary users (SUs) may access the spectrum bands of the primary users (PUs), as long as the interference caused by SUs is tolerable [234]. According to [64], in order to implement CR in practice, three operational models have been proposed, namely, opportunistic spectrum access, spectrum sharing, and sensing-based enhanced spectrum sharing. In 5G era, it is envisioned that the combination of NOMA with CR is capable of further improving the SE. As a benefit of its low implementation complexity, spectrum sharing has been widely applied. In [111-119], the authors analyzed the performance of a spectrum sharing CR combined with NOMA. It was shown that the SE can be significantly improved by using 