Supervisor: Professor Arumugam Nallanathan
Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. In this research, employment of machine learning in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-to-device communications, and so on will be investigated. Besides, Narrow Band-Internet of Things (NB-IoT) is an emerging cellular-based radio access technology, which offers a range of flexible configurations for different coverage enhancement (CE) groups to provide reliable uplink connections for massive IoT devices with diverse data traffic. To optimize the number of served IoT devices, the uplink resource configurations need to be adjusted in real-time according to the dynamic traffic, this brings the challenge of how to select the configurations at the Evolved Node B (eNB) in the multiple CE groups scenario with high-dimension and interdependency. To tackle this challenge, the reinforcement learning (RL) is proposed as a promising solution, where the RL agent (i.e., implemented at the eNB) automatically updates the uplink resource configuration by interacting with the environment (i.e., the communication procedures in NB-IoT). In this research, how the artificial intelligence techniques such as deep learning, artificial neural networks (ANN) can be used dynamically to solve the numerous challenges in the Internet of Things (IoT) will be investigated.