Abstract: This presentation provides an overview and selected recent research at USF on the emerging 5G/IoT and provides some perspectives on the contemplated 6G wireless network ---with emphasis on applications and research directed towards the Internet of InVivo Things (IoIT).
The fifth generation (5G) of mobile communication systems will impact our life more than any other wireless technology by enabling a seamlessly connected society and become the Internet of Tomorrow that brings together people, data, and “things” via a myriad of new applications. This presentation will review the expected disruptive market opportunities, demanding applications, and focus on several research challenges and potential technologies needed to meet the ambitious 5G/IoT requirements for broadband networking, low-latency applications [e.g., autonomous vehicles] technologies, and Internet of Things (IoT) scenarios such as Machine-to-Machine (M2M) networking. We will discuss the role of Machine Learning and other techniques to optimize: cell-less network architectures, performance of mmWave networks, self-organizing networks, novel MAC protocols for M2M communications, latency minimization, NOMA [non-orthogonal multiple access] based MAC protocols for increased throughput in machine-to-machine communications, and methods to enable near-instant recovery from link or nodal failures.
While there are is already much speculation on the applications, or use cases for 6G, in vivo wireless communications and networking of biomedical devices has the potential of being a key component of the sixth generation (6G) wireless networks, perhaps as part of the Internet of InVivo Things (IoIT) in advancing health care delivery. This technology, while in the embryonic stage, offers the promise of improving the effectiveness of emerging cyberphysical biomedical systems. This presentation provides an overview of selected research on characterizing the in vivo wireless RF channel and contrasting this channel with the familiar cellular and WLAN channels. The importance of obtaining accurate channel models is essential to the design of efficient communication systems and network protocols to support advanced bio-medical applications. We describe research on signal processing matched to the in vivo channel including MIMO in vivo and Cooperative Network Coding systems, as well as two of our experimental biomedical systems that focus on changing the paradigm for minimally invasive surgery and a novel electrocardiogram, the vectorcardiogram, that provides 24x7 diagnostic cardiac capability in a compact wearable device and uses Machine Learning to predict cardiac events
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