Space seems empty and therefore the perfect environment for radio communications. Don’t let that fool you: There’s still plenty that can disrupt radio communications. Earth’s fluctuating ionosphere can impair a link between a satellite and a ground station. The materials of the antenna can be distorted as it heats and cools. And the near-vacuum of space is filled with low-level ambient radio emanations, known as cosmic noise, which come from distant quasars, the sun, and the center of our Milky Way galaxy. This noise also includes the cosmic microwave background radiation, a ghost of the big bang. Although faint, these cosmic sources can overwhelm a wireless signal over interplanetary distances.
Depending on a spacecraft’s mission, or even the particular phase of the mission, different link qualities may be desirable, such as maximizing data throughput, minimizing power usage, or ensuring that certain critical data gets through. To maintain connectivity, the communications system constantly needs to tailor its operations to the surrounding environment.
Imagine a group of astronauts on Mars. To connect to a ground station on Earth, they’ll rely on a relay satellite orbiting Mars. As the space environment changes and the planets move relative to one another, the radio settings on the ground station, the satellite orbiting Mars, and the Martian lander will need continual adjustments. The astronauts could wait 8 to 40 minutes—the duration of a round trip—for instructions from mission control on how to adjust the settings. A better alternative is to have the radios use neural networks to adjust their settings in real time. Neural networks maintain and optimize a radio’s ability to keep in contact, even under extreme conditions such as Martian orbit. Rather than waiting for a human on Earth to tell the radio how to adapt its systems—during which the commands may have already become outdated—a radio with a neural network can do it on the fly.
Such a device is called a cognitive radio. Its neural network autonomously senses the changes in its environment, adjusts its settings accordingly—and then, most important of all, learns from the experience. That means a cognitive radio can try out new configurations in new situations, which makes it more robust in unknown environments than a traditional radio would be. Cognitive radios are thus ideal for space communications, especially far beyond Earth orbit, where the environments are relatively unknown, human intervention is impossible, and maintaining connectivity is vital.
Worcester Polytechnic Institute and Penn State University, in cooperation with NASA, recently tested the first cognitive radios designed to operate in space and keep missions in contact with Earth. In our tests, even the most basic cognitive radios maintained a clear signal between the International Space Station (ISS) and the ground. We believe that with further research, more advanced, more capable cognitive radios can play an integral part in successful deep-space missions in the future, where there will be no margin for error.
Future crews to the moon and Mars will have more than enough to do collecting field samples, performing scientific experiments, conducting land surveys, and keeping their equipment in working order. Cognitive radios will free those crews from the onus of maintaining the communications link. Even more important is that cognitive radios will help ensure that an unexpected occurrence in deep space doesn’t sever the link, cutting the crew’s last tether to Earth, millions of kilometers away.
Cognitive radio as an idea was first proposed by Joseph Mitola III at the KTH Royal Institute of Technology, in Stockholm, in 1998. Since then, many cognitive radio projects have been undertaken, but most were limited in scope or tested just a part of a system. The most robust cognitive radios tested to date have been built by the U.S. Department of Defense.
When designing a traditional wireless communications system, engineers generally use mathematical models to represent the radio and the environment in which it will operate. The models try to describe how signals might reflect off buildings or propagate in humid air. But not even the best models can capture the complexity of a real environment.
A cognitive radio—and the neural network that makes it work—learns from the environment itself, rather than from a mathematical model. A neural network takes in data about the environment, such as what signal modulations are working best or what frequencies are propagating farthest, and processes that data to determine what the radio’s settings should be for an optimal link. The key feature of a neural network is that it can, over time, optimize the relationships between the inputs and the result. This process is known as training.