Short-wavelength Reverberant Wave Systems for Physical Realization of Reservoir Computing

Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low energy cost. Here, we experimentally demonstrate an approach that uses the high sensitivity of reverberant short wavelength waves for physical realization and enhancement of computational power of a type of ML known as reservoir computing (RC). The potential computation power of RC systems increases with their effective size. We here exploit the intrinsic property of short wavelength reverberant wave sensitivity to perturbations to expand the effective size of the RC system by means of spatial and spectral perturbations. Working in the microwave regime, this scheme is tested experimentally on different ML tasks. Our results indicate the general applicability of reverberant wave-based implementations of RC and of our effective reservoir size expansion technique

The Future of Intelligent Wavefront Shaping for Smart Radio Environments

As the electromagnetic spectrum becomes more congested and the environments in which we need to operate become more complicated, control over the environment itself becomes necessary to ensure the integrity of wireless communication channels. Wavefront shaping with programmable metasurfaces allows wave fields to be manipulated in both time and space, providing a method to interact with the environment. When coupled with deep learning, intelligent wavefront shaping serves as a catalyst, enabling smart radio environments and unlocking applications beyond traditional wireless communication networks. In this paper, we discuss the outlook of intelligent wavefront shaping for wave propagation in complex environments and highlight its transformative potential.