Ensuring the precision of channel modeling plays a pivotal role in the development of wireless communication systems, and this requirement remains a persistent challenge within the realm of networks supported by Reconfigurable Intelligent Surfaces (RIS). Achieving a comprehensive and reliable understanding of channel behavior in RIS-aided networks is an ongoing and complex issue that demands further exploration. In this paper, we empirically validate a recently-proposed impedance-based RIS channel model that accounts for the mutual coupling at the antenna array and precisely models the presence of scattering objects within the environment as a discrete array of loaded dipoles. To this end, we exploit real-life channel measurements collected in an office environment to demonstrate the validity of such a model and its applicability in a practical scenario. Finally, we provide numerical results demonstrating that designing the RIS configuration based upon such model leads to superior performance as compared to reference schemes.
Reconfigurable intelligent surface (RIS) devices have emerged as an effective way to control the propagation channels for enhancing the end users' performance. However, RIS optimization involves configuring the radio frequency (RF) response of a large number of radiating elements, which is challenging in real-world applications due to high computational complexity. In this paper, a model-free cross-entropy (CE) algorithm is proposed to optimize the binary RIS configuration for improving the signal-to-noise ratio (SNR) at the receiver. One key advantage of the proposed method is that it only needs system performance parameters, e.g., the received SNR, without the need for channel models or channel estimation. Both simulations and experiments are conducted to evaluate the performance of the proposed CE algorithm. The results demonstrate that the CE algorithm outperforms benchmark algorithms, and shows stronger channel hardening with increasing numbers of RIS elements.
The widespread adoption of Reconfigurable Intelligent Surfaces (RISs) in future practical wireless systems is critically dependent on the design and implementation of efficient access protocols, an issue that has received less attention in the research literature. In this paper, we propose a grant-free random access (RA) protocol for a RIS-assisted wireless communication setting, where a massive number of users' equipment (UEs) try to access an access point (AP). The proposed protocol relies on a channel oracle, which enables the UEs to infer the best RIS configurations that provide opportunistic access to UEs. The inference is based on a model created during a training phase with a greatly reduced set of RIS configurations. Specifically, we consider a system whose operation is divided into three blocks: i) a downlink training block, which trains the model used by the oracle, ii) an uplink access block, where the oracle infers the best access slots, and iii) a downlink acknowledgment block, which provides feedback to the UEs that were successfully decoded by the AP during access. Numerical results show that the proper integration of the RIS into the protocol design is able to increase the expected end-to-end throughput by approximately 40% regarding the regular repetition slotted ALOHA protocol.
In this work, we present a 2x2 near-field multi-input multiple-output (MIMO) prototype for bit-error-rate (BER) and error vector magnitude (EVM) measurements in a metal enclosure. The near-field MIMO prototype is developed using software-defined-radios (SDRs) for over-the-air transmission of QPSK modulated baseband waveforms. We check the near-field MIMO BER and EVM measurements in three different scenarios in a highly reflecting metal enclosure environment. In the first scenario, the line-of-sight (LOS) communication link is investigated when the mode-stirrer is stationary. In stationary channel conditions near-field MIMO BER and EVM measurements are performed. In the second scenario, BER and EVM measurements are performed in dynamic channel conditions when the mode-stirrer is set to move continuously. In the third scenario, LOS communication near-field MIMO BER and EVM measurements are performed in stationary channel conditions but now in the presence of MIMO interference. In three different scenarios, near-field MIMO BER and EVM measurements are investigated at different Tx USRP gain values and in the presence of varying levels of MIMO interference.
The fifth generating (5G) of wireless networks will be more adaptive and heterogeneous. Reconfigurable intelligent surface technology enables the 5G to work on multistrand waveforms. However, in such a dynamic network, the identification of specific modulation types is of paramount importance. We present a RIS-assisted digital classification method based on artificial intelligence. We train a convolutional neural network to classify digital modulations. The proposed method operates and learns features directly on the received signal without feature extraction. The features learned by the convolutional neural network are presented and analyzed. Furthermore, the robust features of the received signals at a specific SNR range are studied. The accuracy of the proposed classification method is found to be remarkable, particularly for low levels of SNR.
We exploit multi-path fading propagation to improve both the signal-to-interference-plus-noise-ratio and the stability of wireless communications within electromagnetic environments that support rich multipath propagation. Quasi-passive propagation control with multiple binary reconfigurable intelligent surfaces is adopted to control the stationary waves supported by a metallic cavity hosting a software-defined radio link. Results are demonstrated in terms of the error vector magnitude minimization of a quadrature phase-shift modulation scheme under no-line-of-sight conditions. It is found that the magnitude of fluctuation of received symbols is reduced to a stable constellation by increasing the number of individual surfaces, or elements, thus demonstrating channel hardening. By using a second software-defined radio device as a jammer, we demonstrate the ability of the RIS to mitigate the co-channel interference by channel hardening. Results are of particular interest in smart radio environments for mobile network architectures beyond 5G.
In this work, we present reconfigurable intelligent surface (RIS)-assisted optimization of the multiple links in the same indoor environment. Multiple RISs from different operators can co-exists and handle independent robust communication links in the same indoor environment. We investigated the key performance metrics with the help of two simultaneously operating RIS-empowered robust communication links at different center frequencies in the same indoor environment. We found with the help of bit error rate (BER) and error vector magnitude (EVM) measurements that two operators can co-exist in the same RF environment without seriously impacting quality of service of users.
Reconfigurable intelligent surface (RIS) is an emerging technology that is under investigation for different applications in wireless communications. RISs are often analyzed and optimized by considering simplified electromagnetic reradiation models. In this chapter, we aim to study the impact of realistic reradiation models for RISs as a function of the sub-wavelength inter-distance between nearby elements of the RIS, the quantization levels of the reflection coefficients, the interplay between the amplitude and phase of the reflection coefficients, and the presence of electromagnetic interference. We consider both case studies in which the users may be located in the far-field and near-field regions of an RIS. Our study shows that, due to design constraints, such as the need of using quantized reflection coefficients or the inherent interplay between the phase and the amplitude of the reflection coefficients, an RIS may reradiate power towards unwanted directions that depend on the intended and interfering electromagnetic waves. Therefore, it is in general important to optimize an RIS by considering the entire reradiation pattern by design to maximize the reradiated power towards the desired directions of reradiation while keeping the power reradiated towards other unwanted directions at a low level. Our study shows that a 2-bit digitally controllable RIS with an almost constant reflection amplitude as a function of the applied phase shift, and whose scattering elements have a size and an inter-distance between (1/8)th and (1/4)th of the signal wavelength may be a good tradeoff between performance, implementation complexity and cost. However, the presented results are preliminary and pave the way for further research into the performance of RISs based on accurate and realistic electromagnetic reradiation models.
Reconfigurable intelligent surfaces (RISs) are arrays of passive elements that can control the reflection of the incident electromagnetic waves. While RIS are particularly useful to avoid blockages, the protocol aspects for their implementation have been largely overlooked. In this paper, we devise a random access protocol for a RIS-assisted wireless communication setting. Rather than tailoring RIS reflections to meet the positions of users equipment (UEs), our protocol relies on a finite set of RIS configurations designed to cover the area of interest. The protocol is comprised of a downlink training phase followed by an uplink access phase. During these phases, a base station (BS) controls the RIS to sweep over its configurations. The UEs then receive training signals to measure the channel quality with the different RIS configurations and refine their access policies. Numerical results show that our protocol increases the average number of successful access attempts; however, at the expense of increased access delay due to the realization of a training period. Promising results are further observed in scenarios with a high access load.