Delay/Disruption Tolerant Reinforcement Learning and Aurora Based Communication System

Stottler Henke proposes DREAMS, the Delay/disruption tolerant REinforcement learning and Aurora based coMmunication System, to address NASA’s need for distributed autonomous adaptive network communications technology, including planning and scheduling. DREAMS-equipped devices will sense and receive data and utilize the latest advances in Reinforcement Learning to discover optimal link parameters (e.g., frequency, modulation scheme) to improve various metrics (focusing on maximizing throughput and minimizing BER). The best links are combined to form potential communications paths from a start node to an end node. When feasible, these paths are aggregated by a nearby scheduling node which consolidates the network state and then allocates communications through the network. The proposed work builds on a series of our relevant research efforts (including several NASA-funded efforts) involving resource planning and scheduling, machine learning, low-SWaP algorithms, and distributed algorithms. We have already built a graphical satellite communications simulator which uses NASA’s WorldWind to display satellites and shifting communications links. In addition, we have already integrated our core planning and scheduling algorithm into NASA’s core Flight System. For these reasons, we propose aggressive Phase I objectives culminating in a Phase I Prototype of TRL 4.

Distributed autonomous network communication optimization will become increasingly important as our activity and ambitions in space increase. Optimizing network communications by hand is becoming increasingly challenging; there is a clear need for machine-to-machine fully autonomous network optimization. Stottler Henke’s proposed technology can uniquely fulfill this gap, leveraging past successes with intelligent scheduling, machine learning, and distributed algorithms.

A clear transition opportunity is communications for military operations to coordinate mobile and stationary assets in real time in potentially denied environments. Improving situational awareness by increasing data flow in adversarial environments will significantly improve mission outcomes. Commercial networks with intermittent/variable links (including commercial space networks) could benefit.

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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