Abstract
Reconfigurable reflective surfaces can alter the propagation environment to improve wireless communication and power transfer. Paramount to this operation—which has attracted much attention recently—is the assumption that the reflective surface has prior knowledge of the propagation environment, for example, the direction/location of the transmitter and the intended receiver(s). To address this need, we propose a reconfigurable reflective metasurface with integrated sensing capabilities. By modifying the tunable meta-atoms constituting the metasurface, we couple small portions of the incident wave to an array of sensing waveguides. As an illustrative example, we demonstrate the ability to use the sampled incident wave to detect its angle of arrival. In addition, we propose and numerically demonstrate the possibility to reduce the required sensors, i.e., the number of radio frequency (RF) chains needed to acquire the sensed signals, by leveraging the inherent metasurface’s tunable multiplexing capability. A reconfigurable reflective metasurface with integrated sensing capabilities can benefit wireless communications, wireless power transfer, RF sensing, and smart sensors.
Highlights
In this work, we propose an alternative solution for the Reconfigurable intelligent surfaces (RISs) network incorporation problem by designing a reconfigurable reflective metasurface configuration with integrated sensing capability
We propose an alternative solution for the RIS network incorporation problem by designing a reconfigurable reflective metasurface configuration with integrated sensing capability
As a demonstrative example of the proposed RIS’s design and functionality, we show that we can estimate the angle of arrival (AoA) of an incident signal
Summary
H which has the smallest distance from w corresponds to the estimated AoA. Mathematically, if we assume hj is the jth column of H′H , the column that exhibits the minimum distance, Jest , is given by: Jest. To test the process described above, we simulate this setup with four test beams incident at angles that are not in our dictionary These angles are selected to be distributed over the whole range of interest (−57◦ to 57◦). We will use these test AoAs for all the studies reported in this paper. For each test incident angle, we study the effect of the system’s noise on the estimation accuracy by adding additive white Gaussian noise to the sampled signals (using the built-in awgn function in MATLAB). Characterizing these relationships is beyond the scope of this paper and is left for future works
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