ABSense: sensing electromagnetic waves on metasurfaces via ambient compilation of full absorption

Abstract

Metasurfaces (MS) constitute effective media for manipulating and transforming impinging EM waves. Related studies have explored a series of impactful MS capabilities and applications in areas such as wireless communications, medical imaging and energy harvesting. A key-gap in the existing body of work is that the attributes of the EM waves to-be-controlled (e.g., direction, polarization, phase) are known in advance. The present work proposes a solution to the EM wave sensing problem using the intelligent and networked MS counterparts-the HyperSurfaces (HSFs), without requiring dedicated field sensors. An nano-network embedded within the HSF iterates over the possible MS configurations, finding the one that fully absorbs the impinging EM wave, hence maximizing the energy distribution within the HSF. Using a distributed consensus approach, the nano-network then matches the found configuration to the most probable EM wave traits, via a static lookup table that can be created during the HSF manufacturing. Realistic simulations demonstrate the potential of the novel scheme. Moreover, we show that the proposed workflow is also an ambient EM compiler, i.e., an autonomic HSF that translates high-level EM objectives to corresponding, low-level EM actuation commands.