Next generation of wireless systems and internet of things (IoT), together with smart energy networks and artificial intelligence (AI) will create a new paradigm to enable novel application scenarios and services to be deployed in future smart cities and intelligent transportation by effectively sharing information among different entities in a fast, flexible, power efficient and reliable manner. Given the huge amount of data which is generated every second, fast environment condition variability and demand for more efficient and high response control, the future networks present inherent prediction and control challenges that defy traditional model-based approaches. To address these daunting challenges, we would utilize advanced machine learning and deep reinforcement learning algorithms which enable fast data-driven adaptation to different operating conditions.

This dissertation proposes how advanced machine learning tools could be utilized in the existing wireless and smart energy networks problems. We will consider different and diverse range of tasks including anomaly and fault detection for predictive maintenance in smart grids, distributed resource allocation and network control using federated deep reinforcement learning in wireless networks and best wireless channels selection for video streaming. Specifically the main contributions of this thesis could be summarized as these four parts:

In the first part we would study the smart wireless channel selection using convolutional neural network-based predictive model for high quality video streaming. The second part would be on using AI for smart energy systems and power networks, where we propose a framework based on a combination of signal processing and deep learning techniques for anomaly and fault detection in smart grids. In the third part we would talk on federated multi-agent deep reinforcement learning for distributed power control in multi cell mobile wireless networks. The last part we would consider a network slicing environment where different mobile operators compete for physical resource block while they want to satisfy their clients demands in terms of latency requirement and we propose a deep reinforcement learning solution and a reward based personalization method to tackle this problem.