Data compression for low-latency, high-fidelity, and ultra-reliable AR/VR in bandwidth constrained environments.

The US Army is developing a sophisticated Synthetic Training Environment (STE), a federation of globally distributed training assets, which combines traditional live training exercises with virtual training environments, constructive training environments, and various Army gaming and simulator systems. In Phase II of this program, a functional prototype of a software-defined machine learning (ML)-powered network architecture, the High-Performance STE Overlay Network (HP-SON), will be built to optimize performance across this diverse network and deliver a usable STE training experience. In Phase I of this program, a limited prototype was developed to address the STE networking challenge, which resulted in a promising, yet nascent, Technology Readiness Level (TRL) 3 capability. The technical objective of this PHASE II effort is to fully implement the HP-SON system in the Phase I testbed and test the system and mature the capability to a TRL 5. The result will be a set of hardware and software that is ready to integrate into Army STE testbed facilities for testing in operationally relevant capabilities to increase its TRL to 6. The network will have a maximum speed of 8 GB, with scalable augmented reality and/or virtual reality (AR/VR) capabilities, with low to no latency in a bandwidth-constrained environment. The HP-SON architecture utilizes software-defined networking (SDN) and ML concepts to characterize current, and predict future network conditions, and then adapt traffic flow behaviors based on those characterizations to achieve performance goals. The result is the efficient usage of network resources and an improved overall user experience. While initially intended for application to the STE, HP-SON aims to be network agnostic.  Consequently, this approach is believed to have wide commercial application to any IP network as a network performance acceleration appliance.