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1. Wave-Controlled RIS: A Novel Method for Reconfigurable Elements Biasing

   https://doi.org/10.1109/USNC-URSI52151.2023.10237459  

   Saavedra-Melo, Miguel; Rouhi, Kasra; Capolino, Filippo (July 2023, IEEE)

   A novel method for biasing the varactors of a reconfigurable intelligent surface (RIS) by using resonant standing waves on the biasing transmission line (TL) at a layer below the RF reflective surface to eliminate the need to bring external bias for each element of the RIS is described. We use an analytical model of the RIS to compare the field pattern of the reflected wave by (i) considering the ideal case, (ii) the case where reflection accounts for the varactor's model, and (iii) the case as in (ii) but where the biasing voltage distribution is constructed by using the wave control (i.e., standing waves). 

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2. AI-Driven Optimization of Wave-Controlled Reconfigurable Intelligent Surfaces

   https://doi.org/10.1109/OJCOMS.2025.3598546  

   Ben-Itzhak, Gal; Saavedra-Melo, Miguel; Ayanoglu, Ender; Capolino, Filippo; Swindlehurst, A Lee (January 2025, IEEE Open Journal of the Communications Society)

   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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3. A Versatile Polynomial Model for Reflection by a Reflective Intelligent Surface with Varactors

   https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887248  

   Hanna, David; Melo, Miguel Saavedra; Shan, Feiyu; Capolino, Filippo (July 2022, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI))

   An analytical model for reflection of a reconfigurable intelligent surface (RIS) using varactors to adjust the reflection phase of an incident plane wave is presented. The model results are compared to a full-wave RIS simulation for different varactor bias voltages. The model is intended to be analytical and simple enough, but related to a realistic RIS, to be inserted into MIMO wireless systems algorithms to estimate the propagation channel including the RIS. 

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4. Design and Operation Principles of a Wave-Controlled Reconfigurable Intelligent Surface

   https://doi.org/10.1109/OJCOMS.2024.3492093  

   Ben-Itzhak, Gal; Saavedra-Melo, Miguel; Bradshaw, Benjamin; Ayanoglu, Ender; Capolino, Filippo; Lee_Swindlehurst, A (January 2024, IEEE Open Journal of the Communications Society)

   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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5. RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

   https://doi.org/10.1109/OJCOMS.2024.3353770  

   Ismail, Mohamed I; Shaheen, Abdullah M; Fouda, Mostafa M; Alwakeel, Ahmed S (January 2024, IEEE Open Journal of the Communications Society)

   The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC’17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. 

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