Experimental Analysis of Biasing Voltage Generation in Wave-Controlled RIS

Reconfigurable intelligent surfaces (RISs), an emerging technology proposed for inclusion in next generation wireless communication systems, are programmable surfaces that can adaptively reflect incident electromagnetic radiation in different desired directions. To reduce the complexity and physical profile of conventional RIS designs, a novel concept known as Wave-Controlled RIS has been proposed, in which standing waves along a transmission line are used to generate the required dc bias for reflective control. This paper shows the design of such a Wave-Controlled RIS and its biasing transmission line. The effectiveness of this approach in generating the correct dc bias from a single standing wave frequency is analyzed through both theoretical modeling and experimental validation, which uncovered a dependence on impedance matching not accounted for by the theory. Additionally, the potential for reflective control using only a single standing wave frequency on the biasing transmission line is explored, demonstrating the ability of single-beam steering toward angles near broadside.

AI-Driven Optimization of Wave-Controlled Reconfigurable Intelligent Surfaces

A promising type of Reconfigurable Intelligent Surface (RIS) employs tunable control of its varactors using biasing transmission lines below the RIS reflecting elements. Biasing standing waves (BSWs) are excited by a time-periodic signal and sampled at each RIS element to create a desired biasing voltage and control the reflection coefficients of the elements. A simple rectifier can be used to sample the voltages and capture the peaks of the BSWs over time. Like other types of RIS, attempting to model and accurately configure a wave-controlled RIS is extremely challenging due to factors such as device non-linearities, frequency dependence, element coupling, etc., and thus significant differences will arise between the actual and assumed performance. An alternative approach to solving this problem is data-driven: Using training data obtained by sampling the reflected radiation pattern of the RIS for a set of BSWs, a neural network (NN) is designed to create an input-output map between the BSW amplitudes and the resulting sampled radiation pattern. This is the approach discussed in this paper. In the proposed approach, the NN is optimized using a genetic algorithm (GA) to minimize the error between the predicted and measured radiation patterns. The BSW amplitudes are then designed via Simulated Annealing (SA) to optimize a signal-to-leakage-plus-noise ratio measure by iteratively forward-propagating the BSW amplitudes through the NN and using its output as feedback to determine convergence. The resulting optimal solutions are stored in a lookup table to be used both as settings to instantly configure the RIS and as a basis for determining more complex radiation patterns.