Reconfigurable intelligent surface (RIS)-empowered communication, has been considered widely as one of the revolutionary technologies for next generation networks. However, due to the novel propagation characteristics of RISs, underlying RIS channel modeling and measurement research is still in its infancy and not fully investigated. In this paper, we conduct multi-scenario broadband channel measurements and modeling for RIS-assisted communications at the sub-6 GHz band. The measurements are carried out in three scenarios covering outdoor, indoor, and outdoor-to-indoor (O2I) environments, which suffer from non-line-of-sight (NLOS) propagation inherently. Three propagation modes including intelligent reflection with RIS, specular reflection with RIS and the mode without RIS, are taken into account in each scenario. In addition, considering the cascaded characteristics of RIS-assisted channel by nature, two modified empirical models including floating-intercept (FI) and close-in (CI) are proposed, which cover distance and angle domains. The measurement results rooted in 2096 channel acquisitions verify the prediction accuracy of these proposed models. Moreover, the propagation characteristics for RIS-assisted channels, including path loss (PL) gain, PL exponent, spatial consistency, time dispersion, frequency stationarity, etc., are compared and analyzed comprehensively. These channel measurement and modeling results may lay the groundwork for future applications of RIS-assisted communication systems in practice.

pdf/multi-scenario-broadband-channel-measurement-and-modeling-bz16qh26.pdf

Multi-Scenario Broadband Channel Measurement and Modeling for Sub-6 GHz RIS-Assisted Wireless Communication Systems

The march towards 6G is accelerating and future wireless network architectures require enhanced performance along with significant coverage especially, to combat impairments on account of the wireless channel. Reconfigurable intelligent surface (RIS) technology is a promising solution, that has recently been considered as a research topic in standards, to help manipulate the channel in favor of users' needs. Generally, in experimental RIS systems, the RIS is either connected to the transmitter (Tx) or receiver (Rx) through a physical backhaul link and it is controlled by the network and requires significant computation at the RIS for codebook (CB) designs. In this paper, we propose a practical user-controlled RIS system that is isolated from the network to enhance communication performance and provide coverage to the user based on its location and preference. Furthermore, a low-complexity algorithm is proposed to aid in CB selection for the user, which is performed through the wireless cloud to enable a passive and energy efficient RIS. Extensive experimental test-bed measurements demonstrate the enhanced performance of the proposed system while both results match and validate each other.

Network-Independent and User-Controlled RIS: An Experimental Perspective

To the best of the authors' knowledge, this work presents the first large-scale indoor experimental assessment of an implementation of the emerging Smart ElectroMagnetic Environment (SEME) paradigm, which is based on the deployment of static-passive EM skins (SP-EMSs) to enhance the coverage in a 5 [GHz] Wi-Fi network. Unlike standard (laboratory-based) validations reported in the state-of-the-art (SoA) literature, the scenario at hand mimics a realistic indoor environment to replicate as close as possible the user experience when using commodity devices. Representative results from the experimental field trials are re-ported to confirm the performance predictions arising from the numerical studies and the tolerance analyses carried out with a commercial ray-tracing (RT) tool. Besides experimentally validating the SEME idea, this study is also aimed at (roughly) quantifying the economic advantage of a SEME implementation, relying on simple-manufacturing/low-cost field manipulating devices without any additional biasing circuitry, with respect to standard approaches that imply the densification of the active radiating sources.

pdf/towards-real-world-indoor-smart-electromagnetic-environments-26733o7j.pdf

Towards Real-World Indoor Smart Electromagnetic Environments - A Large-Scale Experimental Demonstration

The precision of link-level theoretical performance analysis for emerging wireless communication paradigms is critical. Recent studies have demonstrated the excellent fitting capabilities of the mixture gamma (MG) distribution in representing small-scale fading in outdoor terahertz (THz)-band scenarios. Our study establishes an in-depth performance analysis for outdoor point-to-point THz links under realistic configurations, incorporating MG small-scale fading combined with the misalignment effect. We derive closed-form expressions for the bit-error probability, outage probability, and ergodic capacity. Furthermore, we conduct an asymptotic analysis of these metrics at high signal-to-noise ratios and derive the necessary convergence conditions. Simulation results, leveraging precise measurement-based channel parameters in various configurations, closely align with the derived analytical equations.

Performance Analysis of Outdoor THz Links under Mixture Gamma Fading with Misalignment

Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with 40×40 elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance. 

Rapid CNN-Assisted Iterative RIS Element Configuration

Reconfigurable intelligent surface (RIS)-empowered communications represent exciting prospects as one of the promising technologies capable of meeting the requirements of the sixth generation networks such as low-latency, reliability, and dense connectivity. However, validation of test cases and real-world experiments of RISs are imperative to their practical viability. To this end, this paper presents a physical demonstration of an RIS-assisted communication system in an indoor environment in order to enhance the coverage by increasing the received signal power. We first analyze the performance of the RIS-assisted system for a set of different locations of the receiver and observe around 10 dB improvement in the received signal power by careful RIS phase adjustments. Then, we employ an efficient codebook design for RIS configurations to adjust the RIS states on the move without feedback channels. We also investigate the impact of an efficient grouping of RIS elements, whose objective is to reduce the training time needed to find the optimal RIS configuration. In our extensive experimental measurements, we demonstrate that with the proposed grouping scheme, training time is reduced from one-half to one-eighth by sacrificing only a few dBs in received signal power.

https://export.arxiv.org/pdf/2211.07188v2.pdf

Indoor Coverage Enhancement for RIS-Assisted Communication Systems: Practical Measurements and Efficient Grouping

There have been recently many studies demonstrating that the performance of wireless communication systems can be significantly improved by a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low power requirement and low complexity. This paper presents a measurement-based characterization of RISs for providing physical layer security, where the transmitter (Alice), the intended user (Bob), and the eavesdropper (Eve) are deployed in an indoor environment. Each user is equipped with a software-defined radio connected to a horn antenna. The phase shifts of reflecting elements are software controlled to collaboratively determine the amount of received signal power at the locations of Bob and Eve in such a way that the secrecy capacity is aimed to be maximized. An iterative method is utilized to configure a Greenerwave RIS prototype consisting of 76 passive reflecting elements. Computer simulation and measurement results demonstrate that an RIS can be an effective tool to significantly increase the secrecy capacity between Bob and Eve.

Measurement-based Characterization of Physical Layer Security for RIS-assisted Wireless Systems

In this work, extensive propagation characteristics of short range 240 to 300 GHz terahertz (THz) channels are mapped based on a measurement campaign conducted utilizing a novel, task specific measurement system. The measurement system allows collecting measurements from different distances and orientations in a very fine grained resolution, which is a particular issue in achieving realistic THz channel estimation. After the accurate measurement results are obtained, they are investigated in terms of channel impulse and channel frequency response. Furthermore, the fading channel amplitude histograms are modeled with the Gamma mixture model (GMM). The expectation-maximization (EM) algorithm is utilized to determine the corresponding mixture parameters. Also, to demonstrate the flexibility of the GMM, the Dirichlet process Gamma mixture model (DPGMM) is utilized in cases where the EM algorithm fails to represent histograms. Moreover, the suitability of the GMM is evaluated utilizing Kolmogorov Smirnov tests. Results verify that the GMMs can simulate the fading channel of micro-scale THz wireless communication in a realistic way, providing important implications regarding the achievable capacity in these channels. Finally, the average channel capacity of each link is evaluated using the probability density function of GMMs to gain deeper insight into the potential of micro-scale THz communications.

Measurement-Based Modeling of Short Range Terahertz Channels and Their Capacity Analysis

Terahertz (THz) frequencies are important for next generation wireless systems due to the advantages in terms of large available bandwidths. On the other hand, the limited range due to high attenuation in these frequencies can be overcome via densely installed heterogeneous networks also utilizing UAVs in a three-dimensional hyperspace. Yet, THz communications rely on precise beam alignment, if not handled properly results in low signal strength at the receiver which impacts THz signals more than conventional ones. This work focuses on the importance of precise alignment in THz communication systems and the significant effect of proper alignment is validated through comprehensive measurements conducted through a state-of-the-art measurement setup, which enables accurate data collection between 240 GHz to 300 GHz at varying angles and distances in an anechoic chamber eliminating reflections. By analyzing the channel frequency and impulse responses of these extensive and particular measurements, this study provides the first quantifiable results in terms of measuring the effects of beam misalignment in THz frequencies.

Experimental Assessment of Misalignment Effects in Terahertz Communications

The estimation of millimeter wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog to digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.

RIDNet Assisted cGAN Based Channel Estimation for One Bit ADC mmWave MIMO Systems

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