ic communication, on the other hand, has knowledge of the underlying message, and the communication scheme can be dynamically changed to fit different needs of the message. Besides, ubiquitous wireless signals open door for sensing applications, such as localization, monitoring, and healthcare. In recent years, intelligent communication system has been proposed to accommodate these needs. A notable example is wireless federated learning system to cater the distributed machine learning. However, a deep integration from wireless design perspective is strongly desired. Recently, there are several emerging technologies which aim to deliver the goal of 5G and beyond, and address the challenges above. Specifically, in this book, our focus is on the physical layer techniques, such as 5G NR non-orthogonal multiple access (NOMA) and physical layer (PHY) mobile edge computing (MEC), high-level communication architec- ture for pervasive Internet of Things (IoT) devices, as well as wireless federated learning system design. We have conducted preliminary researches to address the challenges mentioned above. Specifically, we discuss how to utilize NOMA on improving aggregated data rate and supporting more devices simultaneously, propose schemes for wearable IoT communications, discuss the usage of MEC on helping with power consumption and latency, and analyze how wireless design can facilitate distributed machine learning. Below we briefly introduce each enabling technique. 1.2 Enabling Technologies 1.2.1 5G New Radio 1.2.1.1 Non-orthogonal Multiple Access (NOMA) Initially proposed by NTT DOCOMO as an enhancement for LTE-advanced (LTE-A) in 2013, NOMA has been recognized as one of the most promising techniques for 5G due to its capability of supporting a higher spectral efficiency (SE) and native integration of massive connectivity. The basic principle of NOMA is that at the transmitter side, multiple signals are added up with different powers, forming a superimposed signal (SS). To ensure weak user's quality of service (QoS),