Wi-Fi computational imaging has emerged as a promising paradigm for non-intrusive sensing; however, its practical deployment is severely hindered by dense physical multipath clutter, hardware phase quantization errors, and the limited bandwidth of commercial Wi-Fi. Traditional beamforming-based algorithms often completely lose target focus in complex environments, resulting in severe ghosting artifacts. To overcome these fundamental limitations, we propose RISimg, a novel robust Wi-Fi imaging framework empowered by the spatiotemporal coding of Reconfigurable Intelligent Surface. We design a differential coding strategy to guarantee a well-conditioned and noise-robust sensing matrix, and propose a multi-frequency sparse reconstruction algorithm based on the Least Absolute Shrinkage and Selection Operator. By constructing a large-scale overdetermined system, this physics-driven approach effectively suppresses multipath interference and hardware errors, successfully recovering the basic morphological outlines of complex targets. To further enhance the imaging performance, we propose leveraging a Conditional Diffusion Model to refine the imaging results. By utilizing the reconstruction as a structural prior, a carefully designed Conditional U-Net progressively refines the image through a generative reverse sampling process, restoring high-fidelity continuous boundaries. Extensive evaluations using a hardware prototype built with commercial Wi-Fi devices and a low-cost metasurface demonstrate that the final cascaded CDM achieves an unprecedented Structural Similarity Index Measure of 0.9192 and a Spatial Correlation Coefficient of 0.7336, paving a robust new avenue for Wi-Fi imaging.

Broadcasting systems often suffer from coverage limitation and uneven service quality, particularly in non-line-of- sight (NLOS) environments. Reconfigurable intelligent surface (RIS) has emerged as a promising technology capable of reshaping electromagnetic propagation paths to enhance signal coverage in complex wireless environments. However, the inability for RIS to directly acquire channel state information (CSI) restricts its adaptability. In this paper, we propose a novel hybrid RIS (HRIS)-assisted broadcasting scheme that maximizes the minimum signal-to-noise ratio (SNR) of users, where HRIS enables simultaneous signal reflection and real-time CSI acquisition. We formulate the problem as the maximization of the worst-case SNR of users by optimizing the HRIS phase configuration based on directly acquired CSI. A gradient-based optimization algorithm is developed to iteratively update the phase matrix of HRIS while preserving the fairness among users in broadcasting system. Simulation results demonstrate that the proposed scheme achieves considerable improvements in SNR and sum rate of all users. Moreover, a prototype system is implemented with an 8×8 HRIS array. The measurement results show good consistency with the simulation results. The experimental validation further confirms the effectiveness of the proposed scheme, achieving up to 16.47 dB performance enhancement.

Future Internet-of-Things (IoT) networks demand scalable, energy-efficient, and intelligent wireless nodes with integrated sensing and communication (ISAC). Conventional transmitter architectures rely on complex and power-hungry radio-frequency (RF) chains, limiting their suitability for dense IoT deployment. This work presents a programmable millimeter-wave information metasurface (mmWave-InfoMeta) transmitter enabling compact, low-cost, and reconfigurable operation for IoT applications. The mmWave-InfoMeta directly modulates information onto multiple carrier frequencies using programmable modulation schemes, allowing adaptive operation across diverse IoT environments. With its array characteristics, the metasurface-based transmitter supports dynamic switching between SISO and MIMO modes, enabling space and frequency division multiplexing, while also integrating real-time user localization without the need for additional hardware. Experiments demonstrate a closed-loop ISAC platform capable of adaptive beamforming for signal enhancement. The proposed mmWave-InfoMeta offers a lightweight and multifunctional transmitter solution for large-scale and intelligent IoT systems.

As an emerging technology, WiFi sensing has garnered widespread attention in recent years. However, due to limitations in WiFi bandwidth and hardware capacity, multi-target sensing with WiFi remains an unresolved issue. In this paper, we presentSlingShot, a first-of-its-kind approach for multi-target WiFi sensing enabled by metasurface.SlingShotexploits a metasurface's ability of beam scanning to periodically scan among targets in a high frequency, so their sensing signals can be separated in the time domain. However, the WiFi-initiated and the metasurface-initiated signals will interfere with each other, which could significantly degrade the sensing accuracy. Moreover, the WiFi devices and the metasurface operate in a distributed manner, so the inherent clock offset and clock drift among them make it difficult to accurately separate the sensing signals of the targets. To address the above challenges,SlingShotleverages the metasurface's ability of phase shifting to cancel interfering signals and designs a passive clock synchronization scheme to synchronize the metasurface and WiFi devices. We implementSlingShotand evaluate it under various settings. Results show thatSlingShotcan sense up to 8 targets. Respectively compared to the state-of-the-art methods,SlingShothas 6.75 % higher mean accuracy in activity recognition and 58.34 % lower mean error in respiration monitoring.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for wireless communication systems due to its ability to manipulate electromagnetic waves. With advantages such as low hardware complexity and low power consumption, RIS shows significant potential in positioning applications. This paper presents an RIS-based wireless signal sensing method that operates under the constraint of passive reflection while leveraging the space-time coding capabilities of RIS. By applying a space-time coding matrix on the RIS, the beamspace domain and the angle of arrival (AoA) of signals incident on the RIS can be efficiently estimated, requiring only the processing of single-channel received signals at the access point. Building upon this, a positioning prototype system utilizing two 27 GHz millimeter-wave RIS panels is developed and implemented, supporting real-time user positioning. Experimental results demonstrate that the prototype system achieves centimeter-level positioning accuracy, with errors below 10 cm in 97.22% of measurement cases, thereby validating the effectiveness of the proposed sensing and positioning scheme. These findings may pave the way for further exploration of RIS-based integration of sensing and communication technologies.

To satisfy the increasing demands for wireless transmission rates, it is necessary to exploit spatial resources of electromagnetic (EM) waves. In this context, EM information theory (EIT) has emerged as a hot topic by integrating the theoretical framework of deterministic mathematics and stochastic statistics to explore the transmission mechanisms of continuous EM waves. However, the previous studies were primarily focused on the analysis of the spatial degrees of freedom, with limited exploration of a comprehensive understanding of the essential physical characteristics of EIT. In this paper, a three-dimensional (3D) line-of-sight channel capacity formula is proposed, which captures the vector EM physics and accommodates both near- and far-field scenes. A novel channel model is established based on the rigorous mathematical equation and the physical mechanism of fast multipole algorithm, and it is revealed that the scattered EM waves have finite angular spectral bandwidth, which determines the eigenvalue distributions of the communication system. Furthermore, a series of orthogonal basis are constructed for the currents on the transmitter and the fields on the receiving aperture, thus supporting the optimal design of the spatial precoder and combiner. Comprehensive analyses are made to investigate the relationship among the noise, transmitted power, and spatial degree of freedom, thereby establishing a rigorous upper bound of channel capacity. Finally, a series of simulations are conducted to validate the theoretical model and numerical method. This work offers a novel perspective and methodology for comprehending and leveraging spatial resources of wireless channels, and provides a theoretical foundation for the design and optimization of the holographic communications.

Wi-Fi human sensing has attracted numerous research studies over the past decade. The rapid advancement of machine learning technology further boosts the development of Wi-Fi human sensing. However, current Wi-Fi human sensing suffers from the “data scarcity” problem: all the existing proposals require collecting a large amount of human-based datasets to train the sensing models, which is laborintensive and may raise ethical concerns in certain scenarios. This obstacle seriously restricts the size, quality, and diversity of available datasets, thereby affecting the sensing performance in terms of accuracy and cross-domain applicability. In order to solve this problem, we in this paper propose Metasurface-Assisted Sample Synthesis (MASS), a novel approach to synthesize high-fidelity Wi-Fi sensing samples that effectively capture both the essential features of human motion and environment-specific multipath characteristics without requiring human involvement. The evaluation results show that MASS is effective in boosting machine learning performance, improving classification accuracy by 18%, and enhancing the cross-domain sensing accuracy by 22%. We further analyze inherent synthesis distortions stemming from hardware limitations and introduce a mitigation technique, which significantly enhances data fidelity, achieving 91.8% accuracy even when training exclusively on synthesized samples. These findings underscore the potential of MASS to facilitate the creation of high-quality, diverse datasets with minimal human involvement and associated labor costs.

Reconfigurable intelligent surfaces (RISs) is a two-dimensional metamaterial integrated with numerous reflective RIS elements, which manipulate electromagnetic (EM) waves digitally, offering great potential for internet of things (IoT) applications. However, conventional designs rely heavily on extensive full-wave EM simulations, which are extremely time-consuming. To address this challenge, we propose a machine-learning-assisted approach for efficient RIS design. First, an accurate and fast model for predicting the reflection coefficient of a RIS element is developed by integrating a multi-layer perceptron neural network with a dual-port network. This integration significantly reduces reliance on tedious EM simulations during training, enhancing computational efficiency. Once established, the model serves as a computationally efficient surrogate for resource-intensive EM simulations, enabling streamlined RIS element parameter optimization. Subsequently, an efficient framework for optimizing RIS element design parameters is proposed, advancing the design process. To validate the method, three RIS elements with distinct center frequencies were designed for IoT Applications. Among these, one RIS element was selected to construct a functional RIS. The performance of the RIS was evaluated through fabrication and experimental measurements. Notably, the experimental results demonstrate excellent agreement with EM simulations, confirming the effectiveness of the proposed method.

The rapid development of wireless sensing technology has opened new possibilities for non-contact health monitoring. Among them, respiration is crucial information for assessing vital signs. However, traditional methods face challenges of signal interference and overlap in multi-person environments. In this work, we propose a novel method for multi-person respiration detection using a digital programmable metasurface (DPM). This method takes advantage of the modulation characteristics of the DPM in the time and space domain. It divides the Channel State Information (CSI) from the Wi-Fi transmitter into multiple sub-signals in the time domain and modulates the radiation directions of the sub-signals for space redistribution. These signals are received by the Wi-Fi receiver and recombined to restore the CSI in each direction, thus enabling the accurate extraction of respiration signals from targets in different directions. Experimental results show that this method can directionally sense the human respiration information in a specific direction under the static working mode. Under dynamic scanning mode, it can effectively separate and detect the respiration information of four people from different directions. This system has great potential for applications in wireless communication, healthcare, and smart home environments.

In recent years, Radio Frequency Identification (RFID) technology has been applied in various fields, including warehouse management, smart transportation, passive sensors, and position tracking. This paper presents a novel application of RFID technology in the field of smart antennas, specifically for the wireless control of the pattern and working frequency of the Yagi-Uda antenna through the Ultra High Frequency (UHF)-RFID protocol. By leveraging RFID technology, our innovative approach controls the passive resonator’s length, enabling it to function as a director or reflector. This solution effectively addresses the challenges of pattern instability caused by metal control cables, which are commonly associated with conventional reconfigurable antennas. The proposed antenna operates at 2.49 GHz in directional mode and at 2.45 GHz and 2.51 GHz in bidirectional mode. In the directional mode, the measured gain is approximately 7.5 dBi, exhibiting a remarkable front-back ratio of 11 dB. Furthermore, the entire system demonstrates excellent energy efficiency, with a mere power consumption of 12 W, and the wireless control range reaches up to 33 m.

Existing Internet of Things (IoT) devices face a significant challenge in terms of power consumption due to their limited battery life. Capturing and utilizing ambient radio frequency (RF) energy emerges as a promising solution for powering low-power sensors and electronic devices, given its unique spatial and temporal distributions. However, the low level of ambient RF power severely hampers the rectenna’s RF-to-direct current (DC) conversion efficiency, making it incapable of generating sufficient DC power. To address this issue and enhance the conversion efficiency of a broadband rectenna at low environmental power levels, this study introduces a novel technique called the meta-lens assisted technique (MAT). This technique leads to a substantial increase in the rectenna’s received RF power by more than 10 dB. As a result, the total conversion efficiency improves by over 30% across a wide frequency band ranging from 2.9 GHz to 3.63 GHz (with a fractional bandwidth of 22.3%), even when the initial RF power received (without the MAT) was as low as -20 dBm, which approaches the real-life ambient RF power level. Notably, the proposed MAT achieves a 40% to 60% efficiency improvement compared to state-of-the-art approaches. These remarkable results demonstrate the promising potential of the MAT rectenna as an alternative for harvesting low-density wireless energy and supporting low-power-required industrial IoT applications.

Direction of Arrival (DOA) estimation is essential in building the wireless electromagnetic (EM) environment. However, the conventional DOA estimation methods have been criticized for their hardware complexity, high cost, and low energy efficiency. Recently, metasurface-based methods have emerged as promising alternatives that offer cost-effective solutions. Nevertheless, the existing studies have neglected angle sensitivity, an intrinsic physical property of metasurface, leading to errors in DOA estimations. To address this issue, we propose a generalized high-precision and wide-angle DOA estimation method based on the space-time-coding digital metasurfaces (STCDM). By carefully designing two elementary STC sequences, we realize two angle-independent harmonic states to construct a specified orthogonal matrix. Then, the harmonics, modulated by each meta-column in accordance with the matrix, are sampled by a single channel receiver at different time intervals. Upon the reception of the signal, the information on the metasurface can be comprehensively retrieved through the matrix operations, facilitating the high-accuracy DOA estimations using the array signal processing (ASP) algorithms. This work focuses on eliminating the influence of angle sensitivity on the metasurface-based DOA estimations, which guarantees high accuracy and substantially reduces hardware requirements. The proposed method holds potential for various applications, including Internet of Things (IoT) and integrated sensing and communication (ISAC).

A high-linearity down mixer with outstanding robust temperature tolerance for V-band applications is proposed in this paper. The mixer’s temperature robustness has been greatly enhanced by employing a negative temperature-compensation circuit (NTC) and a positive temperature-compensation circuit (PTC) in the transconductance ( g m ) stage and intermediate frequency (IF) output buffer, respectively. Benefiting from the active balun with enhanced g m and emitter negative feedback technique, the linearity of the mixer has been significantly improved. For verification, a double-balanced V-band mixer is designed and implemented in a 130 nm SiGe BiCMOS process. Measured over the local oscillator (LO) bandwidth from 57 GHz to 63 GHz, the mixer demonstrates a peak conversion gain (CG) of −0.5 dB, a minimal noise figure (NF) of 11.5 dB, and an input 1 dB compression point (IP 1 dB ) of 4.8 dBm under an LO power of −3 dBm. Furthermore, the measurements of CG, NF, and IP 1 dB exhibit commendable consistency within the temperature range of −55 °C to 85 °C, with fluctuations of less than 0.8 dB, 1 dB, and 1.2 dBm, respectively. From 57 GHz to 63 GHz, the measured LO-to-radio frequency (RF) isolation is better than 46 dB, the measured return loss at the RF port is > 29 dB, and at the LO port it exceeds 12 dB. With a 2.5 V supply voltage, the mixer power consumption is 15.75 mW, 18.5 mW, and 21 mW at temperatures of −55 °C, 25 °C, and 85 °C, respectively. Moreover, the mixer chip occupies a total silicon area of 0.56 mm 2 including all testing pads.

Reconfigurable intelligent surface (RIS) is a radical and cost-efficient technology to improve energy efficiency and mitigate interference in the heterogeneous network. In this paper, the resource allocation problem of sub-channels, transmit power and RIS coefficients is investigated in the RIS-aided heterogeneous network. To solve the formulated mixed integer nonlinear programming problem, a two-step centralized resource allocation algorithm and a two-step distributed resource allocation algorithm are proposed based on the alternating optimization method. In the centralized algorithm, the sub-channel allocation, transmit power and RIS coefficients are optimized by the macro base station solely, where the non-convex power optimization problem is transformed into a convex one based on the convex approximation method. In the distributed algorithm, which aims to alleviate the computational burden of the macro base station, the sub-channel allocation and transmit power are optimized by using the cooperation of all the small base stations and the macro base station. Finally, numerical results are presented to demonstrate the convergence of the proposed algorithms and the effectiveness of the sub-channel transfer. More importantly, it is shown that the centralized algorithm can achieve the higher total throughput, while the distributed algorithm greatly decreases the resource allocation time.

The reconfigurable intelligent surface (RIS) has received a lot of interest because of its capacity to reconfigure the wireless communication environment in a cost- and energy-efficient way. However, the realistic power consumption modeling and measurement validation of RIS has received far too little attention. Therefore, in this work, we model the power consumption of RIS and conduct measurement validations using various RISs to fill this vacancy. Firstly, we propose a practical power consumption model of RIS. The RIS hardware is divided into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The power consumption of the first two parts is modeled asPstaticand that of the last part is modeled asPunits. Expressions ofPstaticandPunitsvary amongst different types of RISs. Secondly, we conduct measurements on various RISs to validate the proposed model. Five different RISs including the PIN diode, varactor diode, and RF switch types are measured, and measurement results validate the generality and applicability of the proposed power consumption model of RIS. Finally, we summarize the measurement results and discuss the approaches to achieve the low-power-consumption design of RIS-assisted wireless communication systems.

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.

Reconfigurable intelligent surface (RIS)-empowered communication has recently drawn significant attention due to its superior capability in manipulating the wireless propagation environment. However, the channel modeling and measurement of RIS-assisted wireless communication systems in real environment has not been adequately studied. In this paper, we construct a channel measurement system using vector network analyzer (VNA) is used to investigate RIS-assisted wireless communication channel in outdoor scenarios at 2.6 GHz. New path loss (PL) models including angle domain information are proposed by refining the traditional close-in (CI) and floating-intercept (FI) models. In the proposed models, both influences of the distance from transmitter (TX) to RIS and the distance from receiver (RX) to RIS on the PL, are taken into account. In addition, the value of root mean square (RMS) delay spread of RIS-assisted wireless communication is found to be much smaller than that of the traditional non line-of-sight (NLOS) case, implying that RIS provides a virtual line-of-sight (LOS) link.

智能超表面(RIS)是一种具有可重构电磁特性的二维人工材料. 通过改变嵌入RIS元件中的可调谐器件的控制信号, 可独立地调整每个元件表面电磁波的相位、振幅、偏振和频率响应, 因此能够以可编程的方式重塑空间电磁波的波前. RIS提供了强大的能力来控制无线传播环境, 提高无线通信网络的性能, 同时具有低复杂性、 结构简单、 低成本的优点, 在无线覆盖扩展、 无线覆盖增强以及无线系统容量的提高方面具有很好的优势. 目前, RIS辅助无线通信技术的发展聚焦以下几个关键点: 目前, RIS在无线通信中的未来应用仍面临多重机遇与挑战. 为此, 中国工程院院刊《信息与电子工程前沿(英文)》组织了本期专题. 经严格评审, 选出12篇论文, 包括2篇综述和10篇研究, 涵盖了物理实现、 算法设计和标准化等热点话题.

Complex beams play important roles in wireless communications, radar, and satellites, and have attracted great interest in recent years. In light of this background, we present a fast and efficient approach to realize complex beams by using semidefinite relaxation (SDR) optimization and amplitude-phase digital coding metasurfaces. As the application examples of this approach, complex beam patterns with cosecant, flat-top, and double shapes are designed and verified using full-wave simulations and experimental measurements. The results show excellent main lobes and low-level side lobes and demonstrate the effectiveness of the approach. Compared with previous works, this approach can solve the complex beam-forming problem more rapidly and effectively. Therefore, the approach will be of great significance in the design of beam-forming systems in wireless applications.

Reconfigurable intelligent surface (RIS) and index modulation (IM) are two emerging technologies, which show great potentials to achieve green and clean wireless communications, attracting extensive attention in recent years. This paper designs and implements an RIS-based IM transmission scheme that effectively integrates the two techniques. By utilizing the characteristics of RIS to realize flexible control of electromagnetic waves in a reconfigurable manner, IM wireless transmission can be directly realized without conventional radio frequency chains. The proposed approach is validated through the prototype system which is set up based on a fabricated phase-programmable RIS operating in the sub-6GHz frequency band. The experimental results convincingly verify the feasibility of the proposed scheme and suggest that RISs offer a cost-effective hardware architecture to realize IM with massive transmitting antennas.

With the large-scale deployment and commercialization of 5G, the traditional coverage enhancement technology is facing severe challenges due to high hardware cost and power consumption. At the same time, the emerging reconfigurable intelligent surface (RIS) technology, which can flexibly regulate incident waves and optimize the transmission path of wireless signals, is considered to be one of the most potential 6G key enabling technologies. This paper presents a 5G commercial network coverage enhancement prototype system based on RIS, which directly uses the base station in China Mobile 5G commercial network as transmitter, 5G network signal test terminal as receiver, and RIS code optimization algorithm is employed. The whole system forms a closed optimization loop of “obtaining data-analyzing data-feedback code” and is tested in multiple scenarios. In selected scenarios, the prototype system achieves a received power gain of about 6 dB, which is significant for the research of RIS in 5G commercial network.

Reconfigurable intelligent surfaces (RISs) offer an entirely new route to alter the propagation properties of electromagnetic waves and thus control their reflection, refraction, and scattering features in arbitrary manners. Such physical attributes are perceived to bring about fundamental influence on the modern wireless communication system due to the possibilities to establish artificial and controllable propagation environments for radio signals, which no longer rely on the complex encoding, decoding, and other signal processing techniques. Recent studies reveal that the wave manipulation is not the only skill of the RISs. With the rapid developments of space–time digital metasurface and information metasurface, there has been increasing attention focused on the information manipulation via these artificial surfaces. In this article, we provide an overview of the theoretical models of the space–time digital metasurface and information metasurface, the mechanisms of wavefront shaping, and the signal modulations in space and time domains during the wave–matter interactions. We will also address some practical issues during implementations of the reconfigurable intelligent metasurfaces and the associated hardware architectures at microwave frequencies to realize simplified radio frequency transmitters. Several modulation schemes and the corresponding demonstration systems are introduced to illustrate the powerful abilities of the reconfigurable intelligent metasurfaces. Potential research directions of this technique are briefly discussed for their potential applications in future wireless networks.

Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted wireless communication systems. In this paper, we refine our previously proposed free-space path loss model for RISs to make it simpler, more applicable, and easier to use. The impact of the antenna’s directivity of the transmitter, receiver, and the unit cells of the RIS on the path loss is explicitly formulated as an angle-dependent loss factor. The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size. Based on the proposed model, the properties of a single unit cell are evaluated in terms of scattering performance, power consumption, and area, which allows us to unveil fundamental considerations for deploying RISs in high frequency bands. Two fabricated RISs operating in the millimeter-wave (mmWave) band are utilized to carry out a measurement campaign. The measurement results are shown to be in good agreement with the proposed path loss model. In addition, the experimental results suggest an effective form to characterize the power radiation pattern of the unit cell for path loss modeling.

Reconfigurable intelligent surface (RIS) has drawn worldwide attention because of its attractive capability to improve the electromagnetic propagation environment and surprising advantages of low cost and low power consumption. Recently, RIS is further advanced to realize the independent control of two orthogonal polarizations in real time. In this paper, we study the dual-polarized RIS-assisted mobile communication system and investigate its performance under practical polarization imperfections. Specifically, we provide a tight upper bound of the ergodic spectrum efficiency. Through the bound, we find that the performance is greatly dependent on the polarization imperfections and RIS phase shifts of the RIS-assisted link, when the number of base station antennas is large enough and the deterministic component exists in the RIS-assisted link. On basis of this tight bound, we propose an approximate optimal RIS phase shift design to maximize the ergodic spectrum efficiency upper bound and emphasize the necessity to make phase adjustment between two polarizations of the RIS. Theoretical analysis further gives guidance for the configuration of RIS. Numerical results demonstrate that the upper bound is very tight and the proposed phase shift design with adjustment is approximate optimal.

Future wireless communication networks are expected to fulfill the unprecedented performance requirements to support our highly digitized and globally data-driven society. Various technological challenges must be overcome to achieve our goal. Among many potential technologies, reconfigurable intelligent surface (RIS) and artificial intelligence (AI) have attracted extensive attention, thereby leading to a proliferation of studies for utilizing them in wireless communication systems. The RIS-based wireless communication frameworks and AI-enabled technologies, two of the promising technologies for the sixth-generation networks, interact and promote with each other, striving to collaboratively create a controllable, intelligent, reconfigurable, and programmable wireless propagation environment. This paper explores the road to implementing the combination of RIS and AI, more specifically, integrating AI-enabled technologies into RIS-based frameworks for maximizing the practicality of RIS to facilitate the realization of smart radio propagation environments, elaborated from shallow to deep insights. We begin with the basic concept and fundamental characteristics of RIS, followed by the overview of the research status of RIS. Then, we analyze the inevitable trend of RIS to be combined with AI. In particular, we focus on recent research about RIS-based architectures embedded with AI, elucidating from the intelligent structures and systems of metamaterials to the AI-embedded RIS-assisted wireless communication systems. Finally, the challenges and potential of the topic are discussed.

Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this article, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RISs, which may pave the way for further theoretical studies and practical applications in this field.

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface. Nevertheless, the electromagnetic responses of the RISs are usually only phase-adjustable, which considerably limits the achievable rate of RIS-based transmitters. In this paper, we propose an RIS architecture to achieve amplitude-and-phase-varying modulation, which facilitates the design of multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) transmission. The hardware constraints of the RIS and their impacts on the system design are discussed and analyzed. Furthermore, the proposed approach is evaluated using our prototype which implements the RIS-based MIMO-QAM transmission over the air in real time.