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MIT's Breakthrough: Managing Turbulence in Free-Space Communication

MIT's groundbreaking method tackles atmospheric turbulence, boosting free-space communication's reliability and capacity. It also introduces a novel approach to building physical neural networks using light scattering.

In the picture I can speak an airplane is flying in the air which is in white color. In the...
In the picture I can speak an airplane is flying in the air which is in white color. In the background, we can see the cloudy sky.

MIT's Breakthrough: Managing Turbulence in Free-Space Communication

Researchers at MIT have developed a groundbreaking method to manage turbulence in free-space communication using a unique multimode fiber reservoir. This novel approach promises to enhance the reliability and capacity of optical communication systems.

The team, led by Christopher R. Rawlings and Mitchell A. Cox, tackled atmospheric turbulence, a significant challenge in free-space optical communication. They introduced a physically configured multimode fiber reservoir that acts as a task-dependent hardware design, mitigating the effects of turbulence.

The system demonstrated impressive results, outperforming an ideal modal decomposition by an average of 20.32 ±3.00% in moderate to high-turbulence regimes. It successfully classified handwritten digits and recognized distorted orbital angular momentum modes, showcasing its versatility and high accuracy.

The research also presents a novel approach to building physical neural networks using light scattering within a multi-mode fiber. This method is both efficient and trainable, offering the potential for lower energy consumption compared to electronic systems. Moreover, the system is potentially scalable, opening up possibilities for wider applications.

The new method developed by MIT researchers promises to revolutionize free-space communication by effectively managing atmospheric turbulence. By employing a physically configured multimode fiber reservoir and reframing the receiver as a physical likelihood processor, the system enhances performance, reduces the need for complex digital signal processing, and offers potential for lower energy consumption and scalability.

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