Next Generation Space Communication Powered by AI : Super Exclusive

Space may be the final frontier, but it continues to pose myriad technical challenges as commercial and government-driven space investment continues. One of those challenges is developing more effective space-based communication systems for the increasing number of satellites and spacecrafts that need to interact with one another in the void. A team of researchers has developed an algorithm to enable cognitive radio functions on satellite communications systems to adapt themselves autonomously.

Current space communication systems deploy radio-resource selection algorithms, but they are rudimentary and work with a pre-programmed look-up table. Furthermore, they have little flexibility regarding the various parameters for the performance goals the system needs to achieve. Researchers from Worcester Polytechnic Institute, Pennsylvania State University and NASA’s John H. Glenn Research Center, have designed a new algorithm that allows autonomous parameter selection for radio resource allocated using a novel artificial intelligence architecture.

Credit : Third Party Reference

Autonomous space communication is critical because space is a harsh environment. A number of things can go wrong in space, which why space communications systems should be able to operate without human intervention. The team’s algorithms could serve as the core of a new cognitive engine (CE) used as a baseline for developing communication systems for the next generation of spacecraft and satellites.

The team developed a CE design that autonomously selects multiple radio transmitter settings while attempting to achieve multiple conflicting goals in a dynamically changing communications channel.  It accomplishes this by leveraging reinforcement learning (RL) and “virtual exploration” structures studied in the author’s previous research. The CE integrates these with a novel artificial neural network ensemble design and new algorithms to implement the exploitation aspect of multi-objective reinforcement learning (MORL).

Credit : Third Party Reference

Through RL, the artificial neural network can be trained to adapt to the dynamic conditions of space through multiple trials and experiments, as the algorithm is set up to learn in a manner similar to the human brain by weighing inputs to achieve a goal. In the researchers’ CE, the system can learn how to adapt to achieve multiple goals for satellite communication.

The proof-of-concept design was created through computational simulations as well as ground- and spaced-based experiments. It successfully addresses the limitations of current technology by enabling:

  • Table-free state-action mapping with fixed memory size;
  • Operation over dynamically changing channels;
  • Decoupling of states from actions &
  • Usage of continuous action and state spaces.

As space exploration continues to develop with trips to the moon and Mars, cognitive radio and communication systems will be essential for space flight. There will be a need for space “internetworking” to manage the interaction of user spacecrafts, relay spacecrafts and ground stations. This new CE could be a needed progression for space communication to be more efficient and reliable for any situation encountered during space travel.

Reference- https://divyanshspacetech.wordpress.com

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