The provision of high performing and efficient data communications is crucial for the success of most space exploration missions. Many challenges affect this goal as data transmissions have to occur over unreliable channels that span very large distances and that may not be available continuously over time. As a result, communication opportunities must be exploited optimally to achieve the reliable, high volume and low latency data transfers that will be demanded by future space missions.
This goal is hindered by the use of a communication management approach that is mainly centralized. Such practice creates limitations to what can be optimized not only because of the need for expert human assistance but also because certain system updates could not be communicated to the required network devices within a reasonable time to be effective given the physical dimensions and nature of the network.
We propose to develop a software-defined networking method that exploits cognitive networking methods to optimize the transmission of data flows in a space network. We propose to utilize the Intel Loihi spiking neural network processor and develop learning algorithms for it to achieve very low SWaP processing. The key benefit of this approach will be novel scheduling capabilities that are also implemented on an ultra low SWaP system, making it very suitable for power constrained systems, such as cubesats. This work is being carried out jointly with the University of Houston.
Potential NASA applications include cognitive networking systems for satellites, in particular for constellations of satellites.
Potential non-NASA applications include terrestrial software defined radio communications systems, particularly for systems that are deployed remotely and need high performance communications but low power consumption.