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Robustness in Nonorthogonal Multiple Access 5G Networks

Robustness in Nonorthogonal Multiple Access 5G Networks

While the countdown to next month’s launch of Secure Maritime 5G research capabilities continues, Naval Postgraduate School (NPS) faculty and students have been extending the foundations underlying mmWave and multi-access edge computing and investigating defense applications of 5G already. Earlier this year, for example, Benjamin Pimentel, Dr. Alex Bordetsky, and Dr. Ralucca Gera’s paper, Robustness in Nonorthogonal Multiple Access 5G Networks, was presented at the 55th Hawaii International Conference on System Sciences. From the abstract,

The diversity of fifth generation (5G) network use cases, multiple access technologies, and network deployments requires measures of network robustness that complement throughput-centric error rates. In this paper, we investigate robustness in nonorthogonal multiple access (NOMA) 5G networks through temporal network theory. We develop a graph model and analytical framework to characterize time-varying network connectedness as a function of NOMA overloading. We extend our analysis to derive lower bounds and probability expressions for the number of medium access control frames required to achieve pairwise connectivity between all network devices. (Pimentel et al., 2022).

Two student projects that explore how 5G capabilities might be integrated into maritime and defense operations are the 5G Enabled Maritime Wifi Buoy and Enhanced Corpsman to Hospital Optical System projects.

Secure Maritime 5G, which is a collaborative effort between AT&T and SLAMR, expands our all-domain research and development (R&D) environments—which enables NPS to better support national R&D priorities and the ability of the U.S. Army, Marine Corps, Navy, Air Force, Space Force, and Coast Guard to adopt leading edge technologies.

Pimentel, B., Bordetsky, A., & Gera, R. (2022). Robustness in Nonorthogonal Multiple Access 5G Networks. Proceedings of the 55th Hawaii International Conference on System Sciences, USA, 7444-7453.

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