Machine Learning Techniques for Wireless-Powered Ambient Backscatter Communications: Enabling Intelligent IoT Networks in 6G Era

Machine learning is a rapidly evolving paradigm that has the potential to bring intelligence and automation to low-powered devices. In parallel, there have been considerable improvements in the ambient backscatter communications due to high research interest over the last few years. The combination of these two technologies is inevitable which would eventually pave the way for intelligent Internet-of-things (IoT) in 6G wireless networks. There are several use cases for machine learning-enabled ambient backscatter networks that range from healthcare network, industrial automation, and smart farming. Besides this, it would also be helpful in enabling services like ultra-reliable and low-latency communications (uRLLC), massive machine-type communications (mMTC), and enhanced mobile broadband (eMBB). Also, machine learning techniques can help backscatter communications overcome its limiting factors. The information-driven machine learning does not require the need of a tractable scientific model as the models can be prepared to deal with channel imperfections and equipment flaws in backscatter communications. Particularly, with the use of reinforcement learning approaches, the performance of backscatter devices can be further improved. On-going examinations have likewise demonstrated that machine learning methodologies can be used to protect backscatter devices to enhance their ability to handle security and privacy vulnerabilities. These previously mentioned propositions alongside the ease-of-use of machine learning techniques inspire us to investigate the feasibility of machine learning-based methodologies for backscatter communications. To do such, we start this chapter by talking about the basics and different flavors of machine learning. This includes supervised learning, unsupervised learning, and reinforcement learning. We also shed light on the deep learning models like artificial neural networks (ANN) and deep Q-learning and discuss the hardware requirements of machine learning models. Then, we go on to describe some of the potential uses of machine learning in ambient backscatter communications. In the subsequent sections, we provide a detailed analysis of reinforcement learning for wireless-powered ambient backscatter devices and give some insightful results along with relevant discussion. In the end, we present some concluding remarks and highlight some future research directions.