Quasi-Optic, Radio Frequency Joint Wireless Power Transfer and Machine Learning-Enabled Sensing

Imagine a world where the air around you is not just filled with invisible radio waves carrying music or messages, but also with a gentle, invisible current of energy capable of waking up a sleeping device. This is the promise of wireless power transfer, a concept that has moved from science fiction to reality, allowing sensors and small electronics to run without batteries. For years, scientists have focused on two separate goals: sending power to devices and using radio waves to "see" the world around them, such as detecting a person's movement or breathing. Usually, these tasks require different equipment. Sending power needs a strong, focused beam, while sensing often relies on complex, active receivers that listen for faint echoes. The challenge has been to combine these two functions into a single, simple system that is safe for people, works over long distances, and doesn't need a dedicated battery or a complicated computer chip just to sense its surroundings.

A team of researchers at the University of Glasgow and Loughborough University has taken a significant step toward solving this problem by creating a system that does both at once. They developed a method where a single device receives power from radio waves and simultaneously uses the tiny fluctuations in that power to sense its environment. The key to their success is a special lens, similar to the glass in a pair of glasses but made of plastic and 3D printed, which focuses the incoming radio energy onto a small array of antennas. This setup allows the device to harvest enough energy to run a sensor even when it is far away, while the very act of receiving that power reveals information about the room it is in.

The researchers built a system that operates at a specific frequency of radio waves, using a 3D-printed lens to gather energy from many different directions at once. Instead of needing a perfect, straight line of sight between the power source and the receiver, the lens captures radio waves that bounce off walls and furniture, combining them to create a stronger, more reliable flow of electricity. This is crucial because in a real room, radio waves rarely travel in a straight line; they scatter and reflect. By using multiple antennas behind the lens, the system can sum up the tiny bits of power coming from all these different reflections. The result is a receiver that is much more efficient than previous designs, capable of generating enough voltage to power a small sensor node from a distance of up to 32 meters, or about 105 feet, in a typical indoor space.

What makes this work truly novel is how the device senses without needing a separate sensing antenna or a powerful transmitter. As a person moves through the room, they block or reflect the radio waves hitting the lens. This causes the amount of power reaching the receiver to wiggle up and down in a specific pattern. The researchers found that these tiny fluctuations in the voltage output of the power receiver contain enough information to detect a person's presence and even track their location. In one experiment, the system was sensitive enough to detect the rhythmic rise and fall of a person's chest as they breathed, simply by analyzing the voltage changes in the power line. This means the device is not just charging; it is also acting as a silent, passive observer of the space around it.

To prove this concept works in the real world, the team tested their system in a long corridor and an open atrium. They powered a small Bluetooth sensor, which is the kind of low-power chip used in fitness trackers and smart home devices, from a distance of 22 meters continuously, and up to 32 meters in short bursts. Even when a person walked between the power source and the sensor, blocking the direct path, the system remained operational. This resilience is due to the lens capturing energy from reflected paths that the person did not block. The researchers also demonstrated that by using a machine learning model trained on data collected from the sensor, the system could accurately identify which zone a person was standing in, achieving a success rate of over 90 percent. This was done without any additional radio hardware dedicated to sensing; the power receiver itself provided all the necessary data.

The study highlights a shift in how we might think about wireless networks. Instead of building separate systems for charging devices and monitoring environments, this approach suggests that the power receiver itself can be the sensor. The researchers were careful to note that their system operates within strict safety limits for human exposure to radio waves, ensuring that the energy levels remain safe even when people are nearby. They showed that by focusing the energy with a lens and combining the power from multiple angles, they could overcome the usual limitations of distance and safety regulations. While the system currently relies on a machine learning model that processes data after it is collected, the work proves that the raw data needed for contactless sensing and localization is already present in the power output of a simple, passive receiver.

This research opens a path toward a future where the sensors in our homes and workplaces are not just powered by batteries that need replacing, but are energized by the very radio waves that fill our environment. By turning the act of charging into an act of sensing, the researchers have demonstrated a way to make wireless networks more intelligent and efficient. The ability to detect breathing, track movement, and power devices simultaneously using a single, passive lens-based receiver suggests a new era for the Internet of Things, where devices are not only connected but also aware of their surroundings, all while running on energy harvested from the air.