# CircuitRIS: A Simulation Framework for Analyzing Circuit-Level Non-Idealities in RIS-Aided Wireless Communications

Stefano Lioce, Gianfranco Avitabile, Antonello Florio, Walid Dabbous

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Stefano Lioce, Gianfranco Avitabile, Antonello Florio, Walid Dabbous. CircuitRIS: A Simulation Framework for Analyzing Circuit-Level Non-Idealities in RIS-Aided Wireless Communications. ICECS2025 - 32nd IEEE International Conference on Electronics Circuits and Systems, IEEE, Nov 2025, Marrakech, Morocco. [hal-05316422](https://hal.science/hal-05316422v1)

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Submitted on 15 Oct 2025

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# CircuitRIS: A Simulation Framework for Analyzing Circuit-Level Non-Idealities in RIS-Aided Wireless Communications

Stefano Lioce*,†, Gianfranco Avitabile†, Antonello Florio†, and Walid Dabbous*

*Centre Inria d'Université Côte d'Azur, Sophia Antipolis, 06902 France

†Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, BA, 70125 Italy

e-mail: {first.lastname@inria.fr, first.lastname@poliba.it}

***Abstract***—The advent of new generations of wireless communication pushed the development of innovative technologies to meet emerging requirements. Among these latter, Reconfigurable Intelligent Surfaces (RISs) attracted particular interest. A RIS is a passive, reconfigurable device that enhances the performance of wireless communication between end-users. This work presents a simulation framework for analyzing RISs, correlating circuit-level parameters with system-level metrics. This study examines the impact of circuit non-idealities on the overall performance of RIS-assisted communication systems in indoor environments. Furthermore, the analysis explores the influence of different phase quantization strategies, the number of unit cells, and the reflection angle on the system's performance. In the specific case of RISs composed of unit cells equipped with varactor diodes to enable reconfigurability, we assessed the impact of varactor non-ideality on system performance.

***Index Terms***—Reconfigurable Intelligent Surfaces (RIS), Wireless Communication, Systems Modeling, Circuit Simulation.

## I. INTRODUCTION

In recent years, the growing demand for advanced applications and services with enhanced performance increased the need for high-speed, low-latency wireless communication. This has driven the evolution of new generations of mobile networks. While fifth-generation (5G) systems address part of these requirements, they are not expected to meet them entirely, making the exploration of sixth-generation (6G) technologies necessary. To overcome spectrum congestion at lower frequencies and enhance performance, 6G is expected to operate at higher frequencies, offering wider bandwidths, higher data rates, and reduced latency. However, this comes at the expense of more limited coverage, as signal attenuation and penetration losses increase with frequency, according to Friis's law. To address these challenges, Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising solution. A RIS is composed of a large array of unit cells (UCs) whose reflection properties can be dynamically controlled via low-power electronic components, enabling the manipulation of incident signals in terms of phase and/or amplitude. RIS technology has attracted significant attention from both academia and industry, resulting in extensive research efforts on UC design, channel modeling, and system-level performance analysis [1]–[5]. A review of the literature highlights a lack of integration between these three research areas. For instance, the channel models

rely on an idealized representation of the unit cells, which in turn leads to a simplified and ideal phase quantization process for the values assigned to the UCs.

In our previous work [6], we presented a preliminary analysis of a simulation framework, CircuitRIS, aiming to fill this gap. In particular, we evaluated the impact of different phase quantization strategies and the actual phase shift imposed by the unit cell on the overall system-level performance. Moreover, the radiation pattern of the surface was analyzed as a function of phase quantization accuracy. The objective of this work is to extend the simulation framework by providing a more detailed characterization of the surface. In particular, along with the already mentioned phase quantization strategy, we took into account also variational analysis on the reconfiguration circuit components, the number of the UCs, and the RIS reflection angles.

The paper is organized as follows. Section II recalls some details on the unit cell and the propagation scenario. Section III introduces the proposed simulation framework. Section IV presents and discusses the obtained results. Hence, the conclusions close this work.

## II. RECALLS ON THE UC AND PROPAGATION SCENARIO

CircuitRIS is built around the UC proposed by Rains et al. in [7], the OpenRIS project. The choice is due to the fact that the OpenRIS project is open-source and this helps validate the results thanks to the reproducibility paradigm [8]. We here recall some brief details on the UC to better understand the following sections. The UC is a 1-bit dual-linear polarized varactor-based structure with a center frequency of 3.5 GHz. The structure consists of a central patch connected to two ring resonators which are reconfigurable through a pair of varactor diodes. The two varactors are able to impose phase shifts with 1-bit resolution. The electromagnetic analysis leads to finding the two inverse bias voltage levels which lead respectively to a 0/ + π phase shift, namely the OFF and ON state for the varactor. The UC behavior was then integrated into the RIS-assisted communications channel model proposed by Basar et al. in [9]. To isolate and emphasize the contribution of the RIS itself, a series of simplifications were introduced, excluding interfering phenomena that could affect communication. Specifically, it was assumed that the transmitter (Tx) and
<figure><img src="image_2.png" /><figcaption>Fig. 1: Schematic of the equivalent circuit of the SMV1408 varactor diode.</figcaption></figure>

receiver (Rx) were in a Non-Line-of-Sight (NLoS) condition, while both maintained a Line-of-Sight (LoS) connection with the RIS. Under these conditions, communication between end-users can occur either via multipath signals resulting from reflections from the environment, or through the RIS. In the latter case, the RIS is dynamically reconfigured to maximize the SNR, as described in [6].

### III. THE CIRCUITRIS SIMULATIONS FRAMEWORK

In this section, we describe the CircuitRIS simulation framework. This framework aims to establish a relationship between circuit-level and system-level FoMs. In order to achieve this objective, we considered several parameters, including the number of bits for phase representation, the transmission power $P_T$, the number of UCs, and the signal reflection angle on the surface. We studied the impact of these parameters on system-level FoMs. Finally, the impact of the capacitance values tolerances on system performance is assessed. We determined the reflection coefficient for circuit-level analysis and SNR, ABR, and BER as system-level FoMs.

The first step of the framework involves circuit analysis of the reconfigurable component integrated within the UC. We characterized its behavior in the frequency domain using the scattering matrix, which was subsequently included in the UC layout for electromagnetic simulation. The second step is to evaluate the reflection coefficient of the UC in terms of modulus and phase. The last step is the channel model for RIS-aided communication, in which we imported the reflection coefficient of the UC to model the RIS response. In [6], we performed a preliminary validation of the proposed framework analyzing the impact of $P_T$, and the number of bits used for phase quantization on the system-level FoMs.

In this section, we recall some key concepts and provide a more detailed analysis of the framework. We started with the characterization of the reconfigurability circuit integrated into the unit cell. The UC includes two SMV1408 varactor diodes [10], which were simulated in Keysight Advanced Design System (ADS) by slightly modifying the SPICE model provided in the component datasheet, as shown in Figure 1. Specifically,

we introduced a 50 Ω load to model the connection point to the antenna, a voltage generator to apply a reverse bias to the diode, and appropriate radio frequency (RF) choke and direct current (DC) block components to respectively allow or block the DC signal along the path. This approach allowed us to account for the non-ideal behavior of the components, providing a more realistic characterization of the surface. Subsequently, we integrated the scattering parameter into the unit cell modeled in CST Studio Suite to perform the electromagnetic analysis. We simulated an infinite array of elements, illuminated by a vertically polarized plane wave. Then we extracted the reflection coefficient via a far-field probe placed in front of the array. We implemented an indoor RIS-assisted channel model in MATLAB, including the effect of additive white Gaussian noise (AWGN). We assumed a noise power $P_N$ = -80 dBm. We computed statistical metrics over 100 independent simulations to capture the randomness introduced by the noise. System-level performance will be evaluated for different quantization strategies to assess the impact of phase resolution on overall system behavior. This impact depends on the reconfiguration technology implemented in the UC. Following the notation in [6], continuous-phase, 2-bit, and 1-bit quantization strategies are referred to as *cRIS*, *2-qRIS*, and *1-qRIS*, respectively. These cases assume ideal conditions, where the incident wave is perfectly reflected and the phase shift introduced by each UC precisely matches the analytically computed value. In practice, however, UCs introduce losses and deviations from ideal phase shifts. This more realistic scenario is denoted as *s1-qRIS*, where we considered the non-idealities of the circuit components. By characterizing the unit cell's reflection coefficient for different reverse-bias voltages applied to the diode, we observed variations in both phase and amplitude compared to the ideal scenario. Specifically, the phase difference between the OFF state (0 V) and the ON state (20 V) reached 114°, while the amplitude varied from -0.3 dB to -2.06 dB, representing the attenuation introduced by the cell. We used these values as inputs in the evaluation of the performance of the *s1-qRIS* scenario.

As a preliminary measure for system analysis, we fixed both the number of surface elements to 64 and the node positions, and varied the transmission power. The analysis considered three reference power levels, 3 dBm, 10 dBm, and 20 dBm, representing typical minimum, average, and maximum values for indoor communication. Next, by fixing the transmission power at the average value of 10 dBm, we investigated the effect of varying the number of UCs composing the surface. The analysis covered a range from 4 to 1024 elements, arranged in a square grid configuration. Consequently, we considered only configurations whose total number of elements is a perfect square. Finally, we focused on a 64-element surface with a transmission power of 10 dBm, setting fixed positions for both the RIS and the Tx. The Rx maintained a constant distance from the RIS while moving within the environment, thereby enabling the simulation of different reflection angles. The range of angles that were considered in this study ranged from 0° to 80°, with a 1°
step. We excluded the angles exceeding 80° from this study due to the surface's inability to efficiently reflect signals in the end-fire direction. Finally, we evaluated the impact of varactor non-idealities on the system performance. As regards the analysis of the impact of the non-ideality, and according to the component datasheet [10], each reverse bias voltage corresponds to a nominal capacitance value with a tolerance of ±0.2 pF. To evaluate the effect of this mismatch, where two diodes under the same bias voltage exhibit different capacitance values, we carried out a Monte Carlo simulation. In this setup, we considered 21 possible capacitance values for each bias voltage. For each UC, we determined the one-bit quantized phase value required to maximize the SNR, and we applied the corresponding reverse bias voltage. One of the 21 possible capacitance values is then randomly assigned. Based on these results, both best-case and worst-case scenarios are identified in terms of system-level performance.

## IV. RESULTS

This section presents a detailed analysis of the simulation results, based on the comparison between the RIS-assisted and without RIS (wRIS) scenario, in which the signal is assumed to be reflected by a wall in the room. The comparison is performed by evaluating the SNR, ABR, and BER.

Table I shows the system-level performance results for the three transmission power values and different quantization strategies. The results indicate that system performance improves with both increasing transmit power and higher accuracy in the representation of the phase values. Notably, as $P_T$ rises, the BER converges toward approximately 1.2%, due to the increasing impact of AWGN, which becomes the dominant effect at high power levels.

Then, we assessed the influence of the number of UCs composing the RIS on system-level FoMs. As shown in Figure 2 for $P_T$ of 10 dBm, SNR, ABR, and BER exhibit significant improvements up to approximately 200 UCs, beyond which the performance continues to improve, but at a slower rate. This phenomenon aligns with the principles of array theory, as the reduction in radiating elements leads to a less directive main lobe, resulting in reduced performance.

Afterward, the analysis focused on the impact of reflection angles on the FoMs of the system. Results in Figure 3 show that, as expected, performance degrades as the reflection angle approaches the end-fire direction. Specifically, SNR decreases from 17 dB to 8 dB, corresponding to a BER increase from 1.2% to 3%.

Finally, we investigated the effect of varactor diode non-ideality on the system-level FoMs. As illustrated in Figure 4, SNR, ABR, and BER were evaluated for a 64-UC RIS with actual 1-bit quantization and $P_T$ ranging from 0 to 20 dBm. The term *nominal* refers to results based on the electromagnetic simulation of the varactor schematic in Figure 1, while *mean*, *min*, and *max* represent the average, worst, and best cases across 1,000 Monte Carlo simulations, respectively. Results in Figures 4a–4b reveal a relatively constant SNR and ABR variation of 6 dB and 1.3 bit/s/Hz, respectively, across transmit

power levels. For BER, Figure 4c, a 2.4% discrepancy at 0 dBm gradually decreases, becoming negligible at 20 dBm.

## V. CONCLUSIONS

In this paper, we presented CircuitRIS, a simulation framework for the analysis of the impact of circuit components on system-level FoMs for an RIS-assisted indoor communication system. The framework took into account physical and circuit-level parameters like the impact of the number of unit cells comprising the surface, various phase quantization strategies, and reflection angles on system performance. The results demonstrated that performance improves with increasing transmission power, increasing the number of elements, and increasing the resolution of phase values. Conversely, performance deteriorates as the reflection angle approaches the end-fire direction. Moreover, a reconfigurable UC based on a varactor diode was analyzed, and the impact of diode non-ideality was studied. We studied the effects of diode non-ideality on communication performance, which requires a subsequent surface calibration step to enhance system performance. Future studies will involve validating the proposed framework on other surface prototypes with different reconfiguration mechanisms.

## ACKNOWLEDGMENT

This work was partially supported by the European Union - Next Generation EU under the Italian National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.3, CUP D93C22000910001, partnership on "Telecommunications of the Future" (PE00000001 - program "RESTART"). It was also supported by the CONVERGE project which has received funding under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101094831.

## REFERENCES

[1] A. Scheder, J. Fleischmann, P. Gröschel, and M. Vossiek, "Dual polarized 1-bit reconfigurable reflective unit cell design for intelligent reflective surfaces operating in the 5.8 ghz ism band," in 2024 54th European Microwave Conference (EuMC), 2024, pp. 236–239.

[2] A. Araghi, M. Khalily, M. Safaei, A. Bagheri, V. Singh, F. Wang, and R. Tafazolli, "Reconfigurable intelligent surface (ris) in the sub-6 ghz band: Design, implementation, and real-world demonstration," *IEEE Access*, vol. 10, pp. 2646–2655, 2022.

[3] E. Basar, I. Yildirim, and F. Kilinc, "Indoor and outdoor physical channel modeling and efficient positioning for reconfigurable intelligent surfaces in mmwave bands," *IEEE Transactions on Communications*, vol. 69, no. 12, pp. 8600–8611, 2021.

[4] L. Yang, Y. Yang, M. O. Hasna, and M.-S. Alouini, "Coverage, probability of snr gain, and dor analysis of ris-aided communication systems," *IEEE Wireless Communications Letters*, vol. 9, no. 8, pp. 1268–1272, 2020.

[5] T. Bao, H. Wang, H.-C. Yang, W.-J. Wang, and M. O. Hasna, "Performance analysis of ris-aided communication systems over the sum of cascaded rician fading with imperfect csi," in 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022, pp. 399–404.

[6] S. Lioce, G. Avitabile, A. Florio, D. Saucez, and W. Dabbous, "The impact of the circuit non-idealities on the system-level communication metrics in reconfigurable intelligent surfaces," in 2025 IEEE 16th Latin America Symposium on Circuits and Systems (LASCAS), vol. 1, 2025, pp. 1–5.
<table><caption>TABLE I: ABR, SNR and BER for the scenarios wRIS, and with a 64-UCs RIS with cRIS, 1-qRIS, s1-qRIS, and 2-qRIS.</caption><thead><tr><th rowspan="2">scenario</th><th colspan="3">ABR [bit/s/Hz]</th><th colspan="3">SNR [dB]</th><th colspan="3">BER [%]</th></tr><tr><th>3 dBm</th><th>10 dBm</th><th>20 dBm</th><th>3 dBm</th><th>10 dBm</th><th>20 dBm</th><th>3 dBm</th><th>10 dBm</th><th>20 dBm</th></tr></thead><tbody><tr><td>wRIS</td><td>0</td><td>0</td><td>0</td><td>-39.4</td><td>-32.4</td><td>-22.4</td><td>16.5</td><td>16.4</td><td>15.6</td></tr><tr><td>cRIS</td><td>5.1</td><td>7.4</td><td>10.7</td><td>15.2</td><td>22.2</td><td>32.2</td><td>1.5</td><td>1.2</td><td>1.2</td></tr><tr><td>2qRIS</td><td>4.8</td><td>7.1</td><td>10.4</td><td>14.4</td><td>21.4</td><td>31.4</td><td>1.7</td><td>1.2</td><td>1.2</td></tr><tr><td>qRIS</td><td>3.9</td><td>6.1</td><td>9.4</td><td>11.4</td><td>18.4</td><td>28.4</td><td>2.3</td><td>1.3</td><td>1.2</td></tr><tr><td>s1-qRIS</td><td>2.8</td><td>5.0</td><td>8.3</td><td>7.9</td><td>14.9</td><td>24.9</td><td>3.1</td><td>1.6</td><td>1.2</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Fig. 2: (a) SNR, (b) ABR, and (c) BER determined at a transmission power of 10 dBm as a function of the number of UCs.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3: (a) SNR, (b) ABR and (c) BER in azimuth and elevation at 10 dBm for 64-UCs RIS.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4: (a) SNR, (b) ABR and (c) BER considering the capacitance value tolerances of the varactor diode with linear polarization.</figcaption></figure>

<footnote>[7] J. Rains, J. U. R. Kazim, A. Tukmanov, L. Zhang, Q. Abbasi, and M. Imran, "Fully-Addressable Varactor-Based Reflecting Metasurface with Dual-Linear Polarisation for Low Power Reconfigurable Intelligent Surfaces," in 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023, pp. 1–5.</footnote>

<footnote>[8] "GitHub - jimrains/OpenRIS: OpenRIS is an open-source reconfigurable intelligent surface (RIS) project — github.com," https://github.com/jimrains/OpenRIS, [Accessed 20-06-2024].</footnote>

<footnote>[9] E. Basar, I. Yildirim, and F. Kilinc, "Indoor and Outdoor Physical Channel Modeling and Efficient Positioning for Reconfigurable Intelligent Surfaces in mmWave Bands," *IEEE Transactions on Communications*, vol. 69, no. 12, pp. 8600–8611, 2021.</footnote>

<footnote>[10] "Skyworks SMV1408-Series — Products Details," https://www.skyworksinc.com/en/Products/Diodes/SMV1408-Series, [Accessed 20-06-2024].</footnote>