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A Reconfigurable Intelligent Surface (RIS) consists of many small reflective elements whose reflection properties can be adjusted to change the wireless propagation environment. Envisioned implementations require that each RIS element be connected to a controller, and as the number of RIS elements on a surface may be on the order of hundreds or more, the number of required electrical connectors creates a difficult wiring problem. A potential solution to this problem was previously proposed by the authors in which “biasing transmission lines” carrying standing waves are sampled at each RIS location to produce the desired bias voltage for each RIS element. This paper presents models for the RIS elements that account for mutual coupling and realistic varactor characteristics, as well as circuit models for sampling the transmission line to generate the RIS control signals. The paper investigates two techniques for conversion of the transmission line standing wave voltage to the varactor bias voltage, namely an envelope detector and a sample-and-hold circuit. The paper also develops a modal decomposition approach for generating standing waves that are able to generate beams and nulls in the resulting RIS radiation pattern that maximize either the Signal-to-Noise Ratio (SNR) or the Signal-to-Leakage-plus-Noise Ratio (SLNR). The paper provides five algorithms, two for the case of the envelope detector, one for the sample-and-hold circuit, one for pursuing the global minimum for both circuits, and one for simultaneous beam and null steering. Extensive simulation results show that while the envelope detector is simpler to implement, the sample-and-hold circuit has substantially better performance and runs in substantially less time. In addition, the wave-controlled RIS is able to generate strong beams and deep nulls in desired directions. This is in contrast with the case of arbitrary control of each varactor element and idealized RIS models.
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This content will become publicly available on January 1, 2026
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 estimated 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.
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- Award ID(s):
- 2030029
- PAR ID:
- 10636030
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Open Journal of the Communications Society
- Volume:
- 6
- ISSN:
- 2644-125X
- Page Range / eLocation ID:
- 6650 to 6665
- Subject(s) / Keyword(s):
- Neural network (NN), simulated annealing (SA), genetic algorithm (GA), reconfigurable intelligent surface (RIS), machine learning (ML)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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