Wavefront Shaping in a Complex Reverberant Environment with a Binary Tunable Metasurface

Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing, or imaging. The use of intelligent, reconfigurable metasurfaces provides a potential means for achieving a radio environment that is capable of directing propagating waves to optimize wireless channels on-demand, ensuring reliable operation and protecting sensitive electronic components. The capability to isolate or reject unwanted signals in order to mitigate vulnerabilities is critical for any practical application. In the first part of this dissertation, I describe the use of a binary programmable metasurface to (i) control the spatial degrees of freedom for waves propagating inside an electromagnetic cavity and demonstrate the ability to create nulls in the transmission coefficient between selected ports; and (ii) create the conditions for coherent perfect absorption. Both objectives are performed at arbitrary frequencies. In the first case a novel and effective stochastic optimization algorithm is presented that selectively generates coldspots over a single frequency band or simultaneously over multiple frequency bands. I show that this algorithm is successful with multiple input port configurations and varying optimization bandwidths. In the second case I establish how this technique can be used to establish a multi-port coherent perfect absorption state for the cavity. In the second part of this dissertation, I introduce a technique that combines a deep learning network with a binary programmable metasurface to shape waves in complex electromagnetic environments, in particular ones where there is no direct line-of-sight. I applied this technique for wavefront reconstruction and accurately determined metasurface configurations based on measured system scattering responses in a chaotic microwave cavity. The state of the metasurface that realizes desired electromagnetic wave field distribution properties was successfully determined even in cases previously unseen by the deep learning algorithm. My technique is enabled by the reverberant nature of the cavity, and is effective with a metasurface that covers only ~1.5% of the total cavity surface area