Grant: Discovery Grants Program - Individual

Non-Orthogonal Radio Access with Security Provisioning for Future Wireless Networks Recent years have witnessed a significant increase in the demand for pervasive deployment of wireless communications, including an increasingly widespread Internet-of-Things (IoT). While the race for increased data rates to provide multimedia streaming has continued through the enhanced mobile broadband (eMBB) pillar of wireless networks, supporting IoT has brought unexplored challenges. IoT includes both massive machine-type communications (mMTC), characterized by very high density of low-cost energy-efficient devices typically transmitting non-delay-sensitive data, and mission-critical low-latency ultra-reliable communications (URLLC). Correspondingly, potentially trillions of connected devices with heterogeneous quality-of-service requirements will be connected to future wireless networks, which will need to undergo substantial transformations at all levels of the network architecture. This proposal focuses on addressing challenges at the radio access level. Large-scale non-orthogonal radio access networks (NORANs) with cell densification and multiple co-occurring technologies, including full duplex and massive multiple-input multiple-output, will be examined in terms of interference characterization and management to satisfy the requirements of eMBB and emerging coexisting eMBB-URLLC scenarios. In addition, for mMTC, an aim is to design new non-orthogonal transmission schemes to allow massive connectivity of devices in uplink, along with receivers capable of detecting the activity and data of these devices. Ensuring security is crucial for the mMTC uplink transmission and large-scale NORANs. Devising transmission schemes which provide better security at the physical layer is another goal of this proposal. The proposed research program will develop new theoretical and practical frameworks for tackling crucial design challenges in future radio access networks, with security provisioning. By exploiting concepts from signal processing, information theory, optimization theory, and machine learning to solve challenges of future wireless networks, the highly qualified personnel trained under this program will acquire valuable skills to contribute to both the Canadian high-tech sector and research community. Furthermore, the research findings are anticipated to lead to new practical solutions for the benefit of the global telecommunications industry.