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.

Reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) is emerging as a key enabler for sixth-generation (6G) wireless systems, unifying communication, sensing, and control within a reconfigurable electromagnetic (EM) environment. This article presents a comprehensive review that integrates theoretical foundations, signal processing methodologies, and experimental implementations of RIS-ISAC. It first summarizes core principles of channel modeling, waveform design, and sensing parameter estimation across near-field, broadband, and multihop propagation regimes. Building on these foundations, the article surveys advances in hardware architectures, testbeds, and prototype demonstrations spanning sub-6 GHz to millimeter-wave and terahertz bands. Cross-cutting insights are drawn on system tradeoffs, performance bounds, and learning-aided adaptation. Finally, unified benchmarking metrics and open challenges are identified, covering synchronization, scalability, and standardization, to guide the evolution of RIS-ISAC from conceptual frameworks to practical 6G network deployments.

Reconfigurable intelligent surface-assisted integrated sensing and communication (RIS-ISAC) presents a promising system architecture to leverage the wide bandwidth available at millimeter-wave (mmWave) frequencies, while mitigating severe signal propagation losses and reducing infrastructure costs. To enhance ISAC functionalities in the future air-ground integrated network applications, RIS deployment must be carefully designed and evaluated, which forms the core motivation of this paper. To ensure practical relevance, a multi-RIS-ISAC system is established, with its signal model at mmWave frequencies demonstrated using ray-launching that calibrated to real-world environments. On this basis, an energy-efficiencydriven optimization problem is formulated to minimize the multi-RIS size-to-coverage sum ratio, comprehensively considering real-world RIS deployment constraints, positions, orientations, as well as ISAC beamforming strategies at both the base station and the RISs. To solve the resulting non-convex mixed-integer problem, a reformulation based on equivalent gain scaling method is introduced. A two-step iterative algorithm is then proposed, in which the deployment varaibles are determined under fixed RIS positions in the first step, and the RIS position set is updated in the second step to progressively approach the optimum solution. Simulation results based on realistic parameter benchmarks present that the optimized RISs deployment significantly enhances communication coverage and sensing accuracy with the minimum RIS sizes, outperforming existing approaches.

With the capability to configure wireless propagation environment, reconfigurable intelligent surface (RIS) has attracted wide attention from both academia and industry, in which measurement campaign of RIS in commercial networks is of great importance for performance evaluation. However, existing RIS configuration schemes in practical environments are generally angle-based which require accurate angle information and mainly consider single incident beam, or statistical methods with high sampling overhead. In this paper, we propose a phase shift design scheme for RIS referred to as multiple incident beam superposition (MIBS) scheme, which can be applied in scenarios with incident signals on the RIS from multiple directions. Instead of utilizing random sampling, the proposed scheme firstly employs an incident beam scanning process to extract the incident beam information by pre-calculated codebook to reduce sampling cost, and requires no complex channel estimation or prior channel information. Then the proposed scheme concentrates the incident beams toward the desired reflection direction through a phase shift superposition algorithm. Numerical simulations verify the excellent performance and a 90% sample reduction of the proposed scheme compared with existing methods under the condition of 1-bit RIS hardware for practical applications. Furthermore, a measurement campaign in 5G commercial networks is conducted to validate the advantages of the proposed MIBS scheme in optimizing crucial signal metrics, yielding a 6.76 dB RSRP gain, a 4.9 dB SINR gain and a 37% throughput improvement, showing great potential of RIS for coverage enhancement.

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.

Stacked intelligent metasurfaces (SIMs), composed of a multi-layered structure, have emerged as a powerful computing tool and analog signal processing platform for enabling task-oriented semantic communications (SemCom). However, SIMs lack nonlinear inference capabilities, thus motivating the emergence of the hybrid optical-electronic neural network (HOENN) that cascades a SIM and an electronic neural network (ENN). In this work, we investigate a disaster recognition taskoriented SemCom setting by leveraging the HOENN technology. Specifically, the HOENN is made of an optical neural network (ONN) using SIM mounted on an unmanned aerial vehicle (UAV) and a shallow ENN at the ground receiving station (GRS). The SIM automatically processes semantic information modulated on electromagnetic waves, with reduced energy consumption and ultrafast processing speed. At the GRS, the energy signals are processed by the shallow ENN to enhance the system's inference capability. The aim is to recognize the disaster according to the captured geomorphic images. To this end, we utilize a stochastic gradient descent algorithm to train the HOENN efficiently to minimize the cross-entropy between the recognized and actual semantics. Numerical results show that the HOENN surpasses the performance of using either ONN or ENN alone, achieving$\mathbf{9 7 \%}$recognition accuracy.

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of EM channel characteristics, degrees-of-freedom, and system capacity. However, these works do not clarify how to integrate EIT knowledge into the design and optimization of wireless systems. To fill in this gap, in this paper, we propose an EIT-based statistical channel model with simplified parameterization. Thanks to the simplified closed-form expression of the EMCF, it can be readily applied to various channel modeling and inference tasks. Specifically, by averaging the solutions of Maxwell’s equations over a tunable von Mises distribution, we obtain a spatio-temporal correlation function (STCF) model of the EM channel, which we name as the EMCF. Furthermore, by tuning the parameters of the EMCF, we propose an EIT-based covariance estimator (EIT-Cov) to accurately capture the channel covariance. Since classical MMSE estimators can exploit prior information contained in the channel covariance matrix, we further propose the EIT-MMSE channel estimator by substituting EMCF for the covariance matrix. Simulation results show that both the proposed EIT-Cov covariance estimator and the EIT-MMSE channel estimator outperform their baseline algorithms, thus proving that EIT is beneficial to wireless communication systems.

Integrated sensing and communications (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area. Specifically, we first estimate the sensing channel by using both the communications and the sensing signals echoed back from the target. Then we employ the diffusion model to generate the point cloud that represents the target and thus enables 3D visualization of the target’s EM property distribution. In order to minimize the mean Chamfer distance (MCD) between the ground truth and the estimated point clouds, we further design the communications and sensing beamforming matrices under the constraint of a maximum transmit power and a minimum communications achievable rate for each user equipment (UE). Simulation results demonstrate the efficacy of the proposed method in achieving high-quality reconstruction of the target’s shape, relative permittivity, and conductivity. Besides, the proposed method can sense the EM property of the target effectively in any position of the sensing area.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals to sense the electromagnetic (EM) property of the target and thus identify the materials of the target. Specifically, we first establish an EM wave propagation model with Maxwell equations, where the EM property of the target is captured by a closed-form expression of the channel. We then build the mathematical model for the relative permittivity and conductivity distribution (RPCD) within a predetermined region of interest shared by multiple base stations (BSs). By leveraging the Lippmann-Schwinger equation, we propose an EM property sensing method that reconstructs the RPCD using compressive sensing techniques. This approach exploits the joint sparsity of the EM property vector, which enables the proposed method to effectively handle the high dimensionality and ill-posed nature of the inverse scattering problem. We then develop a fusion algorithm to combine data from multiple BSs, which can enhance the reconstruction accuracy of EM property by efficiently integrating diverse measurements. Moreover, the fusion is performed at the feature level of RPCD and features low transmission overhead. We further design the pilot signals that can minimize the mutual coherence of the equivalent channels and enhance the diversity of incident EM wave patterns. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification.

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper’s imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper’s imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system.

Passive millimeter-wave (PMMW) imaging is extensively employed in public security industries due to its privacy-safe and non-hazardous nature. Nevertheless, the quality of PMMW images is typically poor given blur and noise. Although the end-to-end learning-based image deconvolution methods have demonstrated promising results, they are highly sensitive to minor variations during tests, making them fail to recover small yet crucial details. On the other hand, the blur kernel estimation from textureless PMMW images is also challenging, since the mainstream methods usually adopt convolutional neural networks (CNNs) with local receptive fields to estimate a blur kernel adapted to the entire image. To this end, we propose a dataset-free method (PMMWDeconv) for blind PMMW image deconvolution by integrating the physical generative model into the deconvolution process. The proposed method mainly contains two subnetworks, kernel generator and image generator, and we leverage data consistency and global context priors to help the network in learning from the blurry PMMW image, where the post-processing and transformer are employed to fulfill these goals. Comprehensive experiments are performed to validate the performance of PMMWDeconv, and the results demonstrate that the proposed method surpasses state-of-the-art methods in both robustness and generalization.

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify whether EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel example of how to improve channel estimators by integrating EM knowledge into the classical MMSE channel estimator. Specifically, the EM knowledge is first encoded into a spatial correlation function (SCF) of the channel, which we term the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. Furthermore, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that EIT benefits the channel estimator and enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.

Near field computational imaging has been recognized as a promising technique for non-destructive and highly accurate detection of the target. Meanwhile, reconfigurable intelligent surface (RIS) can flexibly control the scattered electro-magnetic (EM) fields for sensing the target and can thus help computational imaging in integrated sensing and communication (ISAC) systems. In this paper, we propose a near-field imaging scheme based on holograghic RIS. To mitigate the inherent ill conditioning of the inverse problem in the imaging system, we design the EM field patterns as masks that help translate the inverse problem into a forward problem. Next, we utilize RIS to generate different virtual EM masks on the target surface and calculate the cross-correlation between the mask patterns and the electric field strength at the receiver. We then provide a RIS design scheme for virtual EM masks by employing a regularization technique. Simulation results demonstrate that the proposed method can achieve high-quality imaging. Moreover, the imaging quality can be improved by generating more virtual EM masks, by increasing the signal-to-noise ratio (SNR) at the receiver, or by placing the target closer to the RIS.

Passive wireless human sensing has attracted increasing attention due to its non-contact nature and robustness under different lighting conditions. This paper proposes a system for multiperson detection and vital-sign (i.e., respiration and heartbeat) monitoring using space-time-coding (STC) reconfigurable intelligent surfaces (RISs). Specifically, the proposed system scans the area of interest (AoI) for human detection by using the STC RIS-generated harmonic beams. Simultaneously, specific beams are assigned to the detected persons for vital-sign sensing. Furthermore, we propose an improved variational mode decomposition (VMD) algorithm to process the reflected echo signal and estimate respiration and heartbeat rates of the detected persons. We build a prototype to validate the proposed system. Experimental results demonstrate that the system can simultaneously monitor respiration and heartbeat of up to four persons, with average errors below 1 RPM (respiration per minute) and 5 BPM (beats per minute). Further analysis reveals that the flexible beam control enabled by the STC RIS can reduce the noise reflected by irrelative objects, thus improving the signal-to-noise ratio of echoes from the human chest. This work offers a contact-free care to round-the-clock monitor vital signs of multiple persons, which are critical indicators for assessing general health state and identifying various diseases.

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).

Realizing the wireless environmental sensing is another desired function of reconfigurable intelligent surface (RIS), in addition to enhancing the performance of wireless communication systems. In this paper, we design a holographic RIS-aided computational imaging system, which consists of a transmitter, a holographic RIS, a rectangular target and a receiver. Here, the target is composed of a series of discrete segments, each of which possesses a constant scattering density. The sensing task of the proposed system is to estimate the scattering densities of the target, which corresponds to the termcomputational imaging. The termholographicmeans that the RIS is modeled as a physically continuous surface with a physically continuous phase shift pattern, which can be approximately considered as as having massive (possibly infinite) number of elements within a finite space. Both the RIS and the target are subject to the electromagnetic boundary conditions, whose scattered fields are computed by the equivalent current method and the physical equivalent. Based on the computed scattered fields of the target, we derive the pathloss of the proposed system. In order to perform the imaging, we alter the phase shift pattern of the RIS such that the main energy of its scattered fields is focused towards different segments of the target successively, which then produces multiple measurements of the scattering densities and simultaneously ensures a low pathloss. After all measurements are completed, the scattering densities of the target can be estimated with the observed measurement vector and the reconstructed sensing channel, i.e., the computational imaging is accomplished. Simulation results show that the proposed imaging strategy performs well if the system parameters are designed properly.

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.

In this article, joint communication and sensing (JCAS) is combined with reconfigurable intelligent surfaces (RISs), which validates a novel RIS-based JCAS (RIS-JCAS) system. The proposed system performs monostatic sensing to detect a potential user, after which communication with the user is established. The corresponding RIS-cascaded channel is formulated, based on which an algorithm is created, to handle radar target detection and communication channel estimation while multiple RISs operate simultaneously. A joint beam training process by utilizing the identical beamforming pattern on multiple RISs is created, which increases the time efficiency significantly. The detection accuracy is subsequently further improved through a novel fine-tuning method. To practically validate the aforementioned algorithm, a RIS-JCAS system is demonstrated using a mmWave testbed with a bandwidth of 1 GHz centralized at 26.2 GHz. Besides combining the widely adopted orthogonal frequency-division multiplexing (OFDM) scheme with high-directional RIS beams, the system also allows the use of variant RIS flat-top beam shaping combined with orthogonal chirp-division multiplexing radar (OCDM) waveforms. This idea keeps the sensitivity of the RIS-JCAS system while broader beams with wider coverage but lower gain are utilized. Overall, accurate 4-dimensional sensing results are derived, which strongly assists the communication channel estimation and realizes the core benefits of the proposed RIS-JCAS system.

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.

Passive millimeter-wave (PMMW) imaging is an ideal technique for concealed object detection in non-contact security inspection scenarios, and has been widely used in railway stations and airports. However, there are still several challenges that limit the accuracy of detection in PMMW images: low resolution, small objects and complex background interference. The existing deep learning-based methods mainly adopt two-stage architecture with convolutional neural networks (CNN) as the backbone for feature extraction, while the low speed of two-stage architecture and limited receptive field of CNN impede the further improvement of the intelligent inspection system. In this paper, we propose a one-stage anchor-free detector that combines the merits of CNN and transformer to solve these problems, and we avoid the tradeoff between accuracy and computational complexity that most hierarchical transformers make by constricting self-attention within pre-defined local windows. Specifically, we design a novel backbone to model low-level local and high-level global features at different scales via CNN and transformer. We also introduce object queries to guide the detector to perceive the concealed objects from the background noise, and these learnable queries are further utilized to form a full-size object-aware attention mechanism. Besides, to select the optimal positive samples for the anchor-free detector, we propose a novel label assignment strategy by employing Gaussian distribution to adaptively model the objects with various shapes. Experimental results on our self-developed PMMW imager demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed.

Electromagnetic controlled surface (ECS) which can regulate the amplitude and phase of electromagnetic wave reflection characteristics has attracted extensive attention in recent years because of its radar stealth effect by reducing the radar cross Section (RCS). However, radar detection cannot be avoided only by stealth in the energy field gradually. It is a novel pulse Doppler radar jamming mode proposed in this article to use the self-correlation of radar-transmitted waveforms to result in signal processing and radar detection failure through the rapid time-domain variation of phase coding ECS. In principle, phase modulation alters the intrapulse characteristics of the original signal and fundamentally interferes with the processing of the radar signal. In this article, random and periodic phase coding sequences are proposed according to different forms of radar jamming. And through the derivation formula and simulation experiment, the jamming effect under multiphase modulation is verified. Among them, the target energy is dispersed to the surroundings to form a wide envelope through random coding modulation, which leads to noise barrage jamming. While we use periodic sequence coding modulation, the target is shifted in the range–Doppler domain, causing misplaced coherence on the radar echo and deceptive jamming to the radar. Moreover, the technology can also be widely used in synthetic aperture radar (SAR) imaging, microwave measurement, and other remote sensing fields. To further confirm the accuracy and efficiency of the proposed method, multiparameter contrast experiments and parameter sensitivity analyses are conducted.

Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs’ inability to process signals and the attendant increased channel dimension have brought new challenges to RIS-assisted systems, including significantly increased pilot overhead required for channel estimation. To address these problems, several prior contributions that enhance the hardware architecture of RISs or develop algorithms to exploit the channels’ mathematical properties have been made, where the required pilot overhead is reduced to be proportional to the number of RIS elements. In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements. Specifically, in contrast to traditional signal transmission methods, where signals from the base station (BS) and the users are transmitted in different time slots, we propose a novel method in which signals are transmitted from the BS and the user simultaneously during CSI acquisition. With this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we propose the structure of sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, in which one of the proposed vM-EM algorithm is analyzed with the fixed-point perturbation method to obtain an asymptotic achievable bound. In addition, we also derive the Cramér-Rao lower bound (CRLB) and an asymptotic expression for characterizing the best possible performance of the proposed algorithms. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.

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) are 2-D metasurfaces, which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RIS is viewed as a potential key technology for the sixth-generation (6G) wireless communication systems mainly due to its advantages in tuning wireless signals, thus smartly controlling propagation environments. In this article, we aim at addressing channel characterization and modeling issues of RIS-assisted wireless communication systems. First, the concept, principle, and potential applications of RIS are given. An overview of RIS-based channel measurements and experiments is presented by classifying frequency bands, scenarios, system configurations, RIS constructions, experiment purposes, and channel observations. Then, RIS-based channel characteristics are studied, including reflection and transmission, the Doppler effect and multipath fading mitigation, channel reciprocity, channel hardening, rank improvement, far field, near field, and so on. RIS-based channel modeling works are investigated, including large-scale path loss models and small-scale multipath fading models. Finally, future research directions related to RIS-assisted channels are also discussed.

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.

Data augmentation is beneficial when the measured training data are insufficient to train a robust deep model. One of the promising techniques is to use simulated data generated by physics-based engines. For example, few-shot learning of synthetic aperture radar (SAR) targets could be benefited from simulated SAR images. However, the characteristics of the simulated training data significantly affect the performance of the trained model. Therefore, it is of great significance to evaluate the utility of simulated data objectively and effectively. A recognition rate versus substitution rate curve (RSC)-based assessment criterion is proposed, consisting of substitution rate (SR)-based dataset allocation stage and RSC-based evaluation stage. First, the differential dataset allocation is performed under a progressive SR to obtain paired reference and comparison training sets. Then, the reference and comparison classifiers are trained under different SRs using the same network and parameter configuration in the RSC criterion-based evaluation stage. AconvNet and AlexNet are selected as the backbones of the evaluation network. Especially, k-fold cross-validation is applied to alleviate selection bias. The difference between the integrals of RSCs is defined as the RSC score for the simulated dataset. Experiments conducted on the measured and simulated moving and stationary target acquisition and recognition (MSTAR) database demonstrate the rationality and validity of the proposed RSC criterion. Specifically, multisource simulated datasets are adopted, including the adversarial autoencoder-generated and electromagnetic simulation datasets. The proposed RSC criterion shows promising utility evaluation ability, flexibility, and extensibility compared with traditional full-reference image-quality assessment criteria.

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.

Metamaterials and metasurfaces have attracted much attention due to their powerful ability to control electromagnetic (EM) waves. In this paper, we review the recent developments in the field of EM metamaterials, starting from their exotic physics to their applications in novel information systems. First, we show the fundamental understanding on traditional metamaterials based on the effective medium theory and related applications, such as invisibility cloaks and meta-lenses. Second, we review the two-dimensional versions of metamaterials, i.e., metasurfaces, for controlling spatial waves and surface waves and thereafter present their typical designs. In particular, we briefly introduce spoof surface plasmon polaritons and their applications in microwave frequencies. Following the above approach, we emphatically present the concepts of digital coding metamaterials, programmable metamaterials, and information metamaterials. By extending the principles of information science to metamaterial designs, several functional devices and information systems are presented, which enable digital and EM-wave manipulations simultaneously. Finally, we give a brief summary of the development prospects for microwave metamaterials.

A zero-shot learning (ZSL) method of automatic target recognition (ATR) in synthetic aperture radar (SAR) image is proposed to address the scenario, where no SAR sample of a particular target is available for training. To learn features of the unseen target, physics-based electromagnetic (EM) simulated images of the target under different azimuth angles are used as the training data instead. The challenge lies in the fact that the simulated image has a distinct but nonessential texture that the real images do not have and, thus, can easily result in an overfitted discriminator network. To overcome this problem, all images are first preprocessed with a nonessential factor suppression step and then fed into a pretrained convolutional neural network for feature extraction. Finally, the feature vector is fed into a trainable fully-connected network for classification. The low-dimensional embedding of feature vectors suggests that the nonessential factor suppression can align the simulated samples with true samples effectively. We propose the max-tolerability principle and averaged margin index for ZSL, which is a useful indicator for selecting optimal classifier. We validated our method on ten-type target recognition task on MSTAR data sets and achieved 91.93% accuracy on nine known targets and 79.08% accuracy on zero-shot target.

Structured interfaces with subwavelength features enable modulations of phase, amplitude, and polarization on demand, leading to a plethora of flat-profile devices and metasurfaces. Plasmonic and dielectric metasurfaces have been intensively explored, building up the frameworks of flat optics for ultrathin and integrated nanophotonics. The in situ controllability and tunability of aforementioned family of metasurfaces, however, has been a grand challenge, due to the intrinsic limitations of the materials. Semiconductors with diversified catalogs of material candidates thus demonstrate promising potentials, owing to the mature and versatile technologies developed nowadays. The fuse of semiconductors and nanostructured metasurfaces has been witnessed more recently, paving a distinct avenue toward active, tunable, reconfigurable light manipulation for next-generation optical nanodevices. Judicious selection of the active materials for metasurfaces empowers the active functionality of the designer applications. This paper presents a review of this merging semiconductor paradigm for active metasurfaces across a wide range of spectrum and shows unprecedented potentials in the future interface-based optoelectronics, quantum optics, nano-optics, and surface engineering with full compatibility of semiconductor foundry.

Three-dimensional WebGIS has become an important research direction of GIS, and the increasing maturity of oblique photography measurement technology makes 3D-city model more efficient and provides a large number of data sources for the three-dimensional WebGIS platform. However, the amount of data obtained directly from oblique photography products is quite large and cannot be visualized on the Web side. In this paper, we establish a simplified algorithm of oblique photography products, reconstruct the LOD structure of products, establish an efficient spatial index model by using the quadtree algorithm, and improve the data scheduling mechanism.

The landing area of Chang'E-4 is initially selected as the Von Kármán crater inside the South Pole-Aitken basin on the lunar farside. The research would outline science opportunities based on the microwave radiometer data by the field theory. The SVD analyses between the brightness temperature fields of 3 GHz and 37 GHz were done with versus time series data which were calculated by hour angle correction in the Von Kármán crater. The results showed that the main factors affecting the spatiotemporal distribution of brightness temperature are roughness, but less affected by FTA. According to the spatial and temporal distribution of brightness temperature anomalies, the site A(Fig 8) was selected as the preselected landing area for Chang'E-4., which is located in the dense isoline region of the heterogeneous correlation map.

Due to the high spatial and spectral resolution, hyperspectral data have attracted many researchers' attention. Field Imaging Spectrometer System (FISS) is a newly hyperspectral sensor with high quality spectra in visible bands. But the spectral reflectance in near-infrared band is much lower than the normal spectra. In order to calibrate the FISS image data, the ASD spectra of locust leaves are applied to PROSAIL model for validation. By dividing the data into two parts, the visible band and near-infrared band, the parameters of PROSAIL model are obtained by finding the best match of visible band. Then, the parameters are applied to the near-infrared band for validation and thus applying the method to FISS image data for calibration. The results show that this method can calibrate the data effectively and make the data more practical.

Land surface albedo is one of the crucial parameters affecting the land surface energy. The radiation reflected by earth's surface is anisotropic, and it's one of the main factors that affect the accuracy of albedo retrieval from remotely sensed observations. Based on the MODIS BRDF/albedo product and the six archetypal BRDFs, this study learned the effect of reflectance anisotropy on albedo retrieval. First, the six archetypal BRDFs were used to fit the multi-angular observations or the nadir reflectance by the least square method, then the retrieved albedo were compared with the real surface albedo to describe the effect of reflectance anisotropy on albedo. Results show that the effect of reflectance anisotropy on albedo is closely related to the pattern of reflectance anisotropy and the observation geometric. When the multi-angular observations are sufficient and well distributed, the differences between the albedos retrieved from various BRDF archetypes are unremarkable. Meanwhile the intermediate BRDF archetype (No.3 or No.4) or the mean MODIS BRDF has an ability to improve the accuracy of albedo retrieval from insufficient observations.

Point cloud registration is an important procedure for terrestrial laser scanning data processing. Artificial targets are usually used in practice to guarantee a robust registration. However, installing and locating targets are labor-intensive for large surveying and mapping project. A methodology for semiautomatic registration of terrestrial point clouds using perspective intensity images is presented in this letter. To register two point clouds, perspective intensity image series of the scanning area are first generated from the point clouds. Then, corner points are extracted from the generated perspective intensity images and are interactively selected as tie points. The 3-D coordinates of the selected tie points are directly obtained or estimated using least squares method. Registration parameters are solved using singular value decomposition. To improve robustness and accuracy of the registration, random sample consensus is used to remove outliers in the tie points. The robustness and effectiveness of the presented methodology is demonstrated by experimental results.

Three-dimensional multistatic imaging is a powerful noninvasive examination tool for many military and civilian applications. Recently, the sparsity-regularized optimization has been used as a popular imaging technique to enhance the image quality. However, it suffers from the expensive computational cost, since its solution is obtained by a time-consuming iterative scheme, which is typically computationally prohibitive for large-scale imaging problems. To overcome this difficulty, this challenging imaging problem is converted into an image processing problem in this letter, which can be performed over small-scale overlapping patches and be efficiently solved in a parallel or distributed manner. In this way, the proposed qualitative scheme could be utilized to solve large-scale imaging problems. Exemplary simulation results are provided to demonstrate the efficiency of the proposed methodology.

The Bragg hypothesis provides a simplified representation of the complex real ripples that contribute to sea clutter returns. We propose to describe the microscopic profile of large-scale long waves by a set of sinusoidal ripples, which are assumed to be with the Bragg wavelength that satisfies the resonant condition, and travel along the local scattering lines of sight pointing towards and away from the radar. This simplification shows a reasonable way to obtain a rather simple representation of Bragg waves on sea surfaces. More importantly, it allows us to derive a simple asymptotic expression of integration in the far-field scattering amplitude formula, which facilitates the development on a compact model of the instantaneous complex reflective function of a time-deterministic sea surface. Good agreements shown in numerical examples validate the proposed model, which is very efficient to simulate the coherent sea clutters in 2D case up to X band.

Vegetation green up reflects climate impact on landscape, thus monitoring this phenomenon is of crucial importance. This paper develops a bottom-up model to derive green up date using satellite data and ground observations to rebuild green up pattern in Inner Mongolia and analyzes relationships between phenology and climatic factors. The results show that (1) the green up thresholds mostly are at the point where Vegetation Index (VI) begins to increase, and are highly positive correlate with the maxim VI (R2=0.73, Conversion Index=0.243), which means the satellite could effectively obtain green up signals; (2) The bottom-up model which coupled ground observation and satellite data has passed 0.001 significance test; (3) Grass green up spatial pattern changes each year, and temperature and precipitation both influence green up date (P<;0.05). Higher temperature and more precipitation in spring lead to earlier green up date, but the mechanism is uncertain in complex meteorological condition.

Inversion of FeO and TiO2by passive microwave radiometer not only provide support for high-precision quantitative inversion of lunar surface material component by optical remote sensing but also explore resources among the deep lunar regolith better. Using CE-2's passive microwave data, the daytime microwave map of the Moon is calculated based on the hour angle. The lunar regolith radioactive transfer model, integrated with a temperature model and the lunar regolith thickness model, has been established in this paper. On the basis of aforementioned models, the imaginary part of dielectric constant in the whole lunar was numerically simulated. Thus the FeO+TiO2content of the global lunar regolith layer can be obtained on the basis of the relationship between dielectric constant and FeO+TiO2content which was built from the measured data of the Apollo sample. The accuracy of the inversion based on CE-2 MRM data approach would be assessed by comparing our results with the Clementine data, Lunar Prospector gamma-ray data, and the laboratory data of lunar soils.

Synthetic aperture radar (SAR) images of the Earth's terrain surface contain the calculation of scattering coefficients and the simulation of backscattering echo, which are influenced by the terrain elevation and slope. In this study, we present a method of electromagnetic imaging for terrain surfaces based on airborne strip map synthetic aperture radar (SAR). First, the integral equation method was used to calculate the backscattering coefficients of terrain surfaces. Then the simulation method of backscattering echo signal is presented for the terrain surfaces from the principle of SAR imaging. Finally, the Range-Doppler Algorithm was used to obtain the imaging of terrain surface from the processing of backscattering echo signal.

In the terahertz (THz) band, materials that could be seen as smooth and homogeneous at microwave frequencies may display surface and volumetric scattering behavior. In this paper, we apply the integral equation method (IEM) and the vector radiative transfer (VRT) theory to simulate surface and volumetric scattering by a rough material boundary, respectively, in order to study the scattering characteristics of targets at THz frequencies. IEM was used to calculate the backscattering coefficient of a metal plate, in which the calculated results have good agreements with measured data, proving the applicability and superiority of IEM to the metal materials. Nevertheless, the simulation results of dielectric sample using IEM cannot fit the measured data. That is to say, for dielectric boundary, we should not only consider the contribution of surface roughness, but also the contribution of internal microstructures. We propose to use both VRT and IEM to simulate the scattering behavior of dielectric targets, which shows good agreement between the simulation and measurement. The results tell us that for the material which presents metal features, we only need to consider the effect of surface roughness; however,for the dielectric material, the micron inclusions of the material also need to be considered, i.e., carbon powder and silicon carbide particles. The established model can also be used to simulate the bi-static scattering coefficient, which can be regarded as a complement to the experimental data.

We present a genetic algorithm-based inversion method for retrievals of sand-covered surface roughness from the polarimetric radar observation. This inversion method is based on the integral equation method (IEM), which was developed rigorously to estimate backscattering coefficients in theory from the surface roughness parameter. The genetic algorithm is applied for the inversion method to retrieve both the rms height s and the correlation length l from multi-polarized radar observations of sand-covered surfaces. The cost function for each chromosome is evaluated using IEM. Good agreement of the backscattering coefficients is found between the numerical inversion and the measured data. We adopt the Gaussian fitting method to describe the inversion result, including s, l and fitness. We found that the genetic algorithm-based inversion method has the highest accuracy in the estimation of the surface roughness from the radar observations.

Many previous studies attempted to extract prior reflectance anisotropy knowledge from historical Bidirectional reflectance distribution function (BRDF) product based on normalized difference vegetation index (NDVI) data. However, the correlation between reflectance anisotropy and NDVI is still controversial. This study used BRDF archetypes to represent different reflectance anisotropy, and analyzed the correlation between reflectance anisotropy and NDVI based on five-year time series MODIS BRDF data. Nadir reflectance and NDVI retrieved from different BRDF archetypes and same multi-angular observations were also compared with each other to further study this correlation. Results show that the six BRDF archetype classes are all contained in any range of NDVI with one BRDF archetype class accounting for a maximum of 40%. Nadir reflectance and NDVI retrieved from different BRDF archetypes have little difference with each other at different solar zenith angles. NDVI is not a reliable clue to distinguish surface reflectance anisotropy.

This letter introduces the concept of regression structural similarity index measure (regression SSIM) and builds a supervised graph framework of automatic detection of small-size ultrawideband (UWB) radar targets. The twofold contribution made in this letter includes the following: 1) The regression SSIM is proposed to measure the similarity of the local pattern between a test image and a reference image; and 2) the framework of a supervised graph, together with the regression SSIM, has been developed to address the automatic detection of UWB radar objects. As opposed to other detection techniques reported in the literature, our methodology does not rely on statistical modeling or imposing typical shape parameters. Selected results of processing simulated and real UWB ground-penetrating radar data are provided, which verifies the state-of-the-art performance of the proposed methodology and provides important potential for a wide class of target detection.

The geographical world is an intricate system that comprises the interaction of the Earth's atmosphere, hydrosphere, biosphere, lithosphere, and pedosphere. Existing technologies and systems can only store, represent, and analyze crisp or type-I fuzzy spatial data and obtain spatial knowledge on several discrete scales. However, these technologies are limited to multi-scale and high-order vagueness spatial data representation and analysis, particularly regarding the representation and acquisition of multi-scale knowledge. In this paper, the uncertainty in geographic information systems (GISs) and existing problems in classical spatial analysis methods are summarized. Innovative concepts, such as the scale aggregation model and scale polymorphism, are proposed. A multi-scale fuzzy spatial analysis framework based on an interval type-II fuzzy set is introduced, and critical points are highlighted, such as an interval type-II fuzzy geographical object model (the boundary model and metric methods for geometric properties), direction relations, topological relations, and overlap methods. An actual case based on a multi-scale regional debris-flow hazard assessment is used to confirm the validity of the theory proposed in this paper.

A three-stage common-source low noise amplifier (LNA) is designed in a 65 nm LP CMOS process. Basically, at mm-wave frequency the Miller capacitance degrades the amplifier performance dramatically. To address these issues, a transformer based neutralization technique is used. With the proposed technique, simulations show that the circuit achieves a gain of 22 dB along with a 1-dB bandwidth of 4 GHz. The noise figure is 5.5 dB and the IIP3 is -10.7 dBm with a power consumption of 26 mW from a 1.2 V supply.

An efficient finite-element method is presented to analyze the scattering from complex bodies of revolution buried or partly buried in layered lossy media. In the proposed method, high-order edge-based vector basis functions are used to expand the transverse field components and high-order node-based scalar basis functions are used to expand the angular component. A locally conformal perfectly matched layer (PML) by complex coordinate stretching for inhomogeneous medium is presented and used to truncate the computational domain. Such a kind of PML is very easy to implement in the numerical process and is able to enclose an arbitrarily shaped convex object in the spatial domain. Numerical examples are presented to demonstrate the accuracy and efficiency of the presented method.

This paper considers a joint antenna selection method in amplify-and-forward (AF) relay networks where the source, relay and destination terminals are all equipped with multiple antennas. The fact that the system's full diversity can be maintained by antenna selection at each terminal makes it a promising solution to reduce the hardware complexity of multiple-input multiple-output (MIMO) terminals while realizing the diversity benefits of MIMO in relay networks. Since the exhaustive search for antenna subset selection is computationally prohibitive, we devise a low-complexity near-optimal joint antenna selection algorithm based on a constrained cross entropy optimization (CCEO) method to maximize the achievable rate and the convergence is guaranteed. Simulation results reveal both the effectiveness and the efficiency of the proposed algorithm and the significant performance improvement over other benchmark selection techniques. Finally, it is illustrated that the proposed CCEO algorithm can always achieve near-optimal results regardless of the number of selected antennas, outage probabilities and the signal-to-noise ratios (SNRs) at the terminals.

An efficient approach is proposed to analyze the electromagnetic scattering from objects above a 2-D perfectly electric conducting rough surface. A half-space Green's function with the rough-surface interface is first derived from the Kirchhoff approximation (KA). The method of moments is then applied to analyze the scattering problem of 3-D arbitrarily shaped objects above the rough surface. Since only the objects need to be discretized, the computational time and memory requirement are greatly reduced. The radar cross sections of typical objects above the rough surface have been computed using the proposed method. Numerical results show that the proposed method has good accuracy in the valid range of KA.

An efficient inversion method is proposed using Cui et al.'s high-order extended Born approximations to reconstruct the conductivity object function of three-dimensional dielectric objects buried in a lossy Earth. High-order solutions of the object function are obtained, which have closed-form relations to the linear inverse-scattering solution. Because such relations can be evaluated quickly using the fast Fourier transform, the high-order solutions have similar simplicity as the linear inversion. When the contrasts of buried objects are large, the high-order solutions are much more accurate due to the approximate consideration of multiple-scattering effects within the objects. Hence, good-resolution images can be obtained for large-contrast objects using the new method by only solving a linear inverse problem. Numerical experiments have shown the validity and efficiency of the proposed method.

The very early time electromagnetic system (VETEM) is an efficient tool for the detection of buried objects in very lossy earth, which allows a deeper penetration depth compared to the ground-penetrating radar. In this paper, the inversion of VETEM data is investigated using three-dimensional (3-D) inverse scattering techniques, where multiple frequencies are applied in the frequency range from 0-5 MHz. For small and moderately sized problems, the Born approximation and/or the Born iterative method have been used with the aid of the singular value decomposition and/or the conjugate gradient method in solving the linearized integral equations. For large-scale problems, a localized 3-D inversion method based on the Born approximation has been proposed for the inversion of VETEM data over a large measurement domain. Ways to process and to calibrate the experimental VETEM data are discussed to capture the real physics of buried objects. Reconstruction examples using synthesized VETEM data and real-world VETEM data are given to test the validity and efficiency of the proposed approach.

It is known that the extended Born approximation (ExBorn) is much faster than the method of moments (MoM) in the study of electromagnetic scattering by three-dimensional (3-D) dielectric objects, while it is much more accurate than the Born approximation at low frequencies. Hence, it is more applicable in the low-frequency numerical simulation tools. However, the conventional ExBorn is still too slow to solve large-scale problems because it requires O(N/sup 2/) computational load, where N is the number of unknowns. In this paper, a fast ExBorn algorithm is proposed for the numerical simulation of 3-D dielectric objects buried in a lossy Earth. When the buried objects are discretized with uniform rectangular mesh and the Green's functions are extended appropriately, the computational load can be reduced to O(N log N) using the cyclic convolution, cyclic correlation, and fast Fourier transform (FFT). Numerical analysis shows that the fast ExBorn provides good approximations if the buried target has a small or moderate contrast. If the contrast is large, however, ExBorn will be less accurate. In this case, a preconditioned conjugate-gradient FFT (CG-FFT) algorithm is developed, where the solution of the fast ExBorn is chosen as the initial guess and the preconditioner. Numerical results are given to test the validity and efficiency of the fast algorithms.

Very early time electromagnetic system (VETEM) is an efficient tool for the detection of buried targets in very lossy medium. In this paper, we will discuss the inversion of VETEM data using a combination of one-dimensional (1D) and three-dimensional (3D) inverse scattering techniques, where the Born iterative method and distorted Born iterative method have been applied. In order to expedite the 3D inversion, an extended Born-type approximation is made in the forward solver, together with FFT acceleration technique. The 1D inversion provides a gross image of the subsurface, but the 3D inversion helps to resolve the subsurface image better.

The electromagnetic field simulation of the target detection by high-resolution radar is performed by incorporating the asymptotic waveform evaluation (AWE) technique and the compression technique of a chirp signal. The electromagnetic field simulation is very helpful for revealing the multi-scattering mechanism of wide-band radar signal between targets or within a target. Our current simulation involves three major steps, in the first step, AWE technique is used to calculate the scattering far field over a wideband at which a radar is operated; in the second step, the frequency response of scattering field is multiplied by the spectrum of the chirp signal transmitted by radar to get the frequency response of radar system; in the third step, the system response is multiplied by the conjugate of the spectrum of the chirp signal and then transformed into time domain by inverse FFT. In this way the target detection by chirp radar can be accurately simulated using electromagnetic field. Numerical examples are presented.

A simple and efficient method for large scale three-dimensional (3D) subsurface imaging of inhomogeneous background is presented. One-dimensional (1D) multifrequency distorted Born iterative method (DBIM) is employed in the inversion. Simulation results utilizing synthetic scattering data are given. Calibration of the very early time electromagnetic (VETEM) experimental waveforms is detailed along with major problems encountered in practice and their solutions. This discussion is followed by the results of a large scale application of the method to the experimental data provided by the VETEM system of the U.S. Geological Survey. The method is shown to have a computational complexity that is promising for on-site inversion.

The paper by T. J. Cui and W. C. Chew, IEEE Trans. Geosci. Remote Sensing, vol. 38, p.2033-41 (2000) is corrected. The authors, in that paper, defined the branch cut of K/sub ay/ and k/sub by/ in an equation (7) as Re(k/sub ay/,/sub by/)/spl ges/0 and Im(k/sub ay/,/sub by/)/spl ges/0 to make the Sommerfeld-like integral in another equation(5) satisfy the radiation condition. In the inverse-Fourier expression of yet another equation (24), however, they had to choose the branch cut of k/sub by/ in a different way, where Re(k/sub by/)/spl ges/O and Im(k/sub ay/,/sub by/)/spl les/, so that the integral in this equation can be convergent. Hence, the k/sub by/ in equation (27) must be set in different branch cuts. Although such a procedure gave correct reconstruction results, the mathematical interpretation was confusing. In this correspondence, the authors now modify the mathematical interpretation so that it is clear and easily understood.

An efficient inverse-scattering algorithm is developed to reconstruct both the permittivity and conductivity profiles of two-dimensional (2D) dielectric objects buried in a lossy earth using the distorted Born iterative method (DBIM). In this algorithm, the measurement data are collected on (or over) the air-earth interface for multiple transmitter and receiver locations at single frequency. The nonlinearity due to the multiple scattering of pixels to pixels, and pixels to the air-earth interface has been taken into account in the iterative minimization scheme. At each iteration, a conjugate gradient (CG) method is chosen to solve the linearized problem, which takes the calling number of the forward solver to a minimum. To reduce the CPU time, the forward solver for buried dielectric objects is implemented by the CG method and fast Fourier transform (FFT). Numerous numerical examples are given to show the convergence, stability, and error tolerance of the algorithm.

In this paper, numerical simulations of a new enhanced very early time electromagnetic (VETEM) prototype system are presented, where a horizontal transmitting loop and two horizontal receiving loops are used to detect buried targets, in which three loops share the same axis and the transmitter is located at the center of receivers. In the new VETEM system, the difference of signals from two receivers is taken to eliminate strong direct-signals from the transmitter and background clutter and furthermore to obtain a better SNR for buried targets. Because strong coupling exists between the transmitter and receivers, accurate analysis of the three-loop antenna system is required, for which a loop-tree basis function method has been utilized to overcome the low-frequency breakdown problem. In the analysis of scattering problem from buried targets, a conjugate gradient (CG) method with fast Fourier transform (FFT) is applied to solve the electric field integral equation. However, the convergence of such CG-FFT algorithm is extremely slow at very low frequencies. In order to increase the convergence rate, a frequency-hopping approach has been used. Finally, the primary, coupling, reflected, and scattered magnetic fields are evaluated at receiving loops to calculate the output electric current. Numerous simulation results are given to interpret the new VETEM system. Comparing with other single-transmitter-receiver systems, the new VETEM has better SNR and ability to reduce the clutter.

An efficient method for modeling wire antennas of arbitrary shapes and orientations above a lossy ground is presented by using Galerkin method. As opposed to the traditional point matching method, careful treatment is required for the electric-field integral equation (EFIE) and its numerical implementation. Comparing with the point matching method, only three simple Sommerfeld integrals are needed in the new method, and the convergence is much faster. To test the validity of this method, numerous numerical examples are given. Numerical results show that, for equivalent-accuracy solutions, the cell number used in this method is much fewer than that in the point matching method.

A novel diffraction tomographic algorithm has been proposed to detect two-dimensional (2D) dielectric cylinders buried under a lossy Earth. In this algorithm, the air-Earth interface has been taken into account and the exact treatment of Sommerfeld-like integrals has been considered. Using the algorithm, all locations, shapes, and dielectric properties of buried cylinders can be accurately reconstructed under the low-contrast condition. For high-contrast targets, this algorithm can also be used to determine their locations and approximate their dielectric properties. Due to the use of fast Fourier transforms to implement the problem, this algorithm is very fast and quite tolerant to the error of measurement data. Numerical examples are given to show the validity of the algorithm.

This paper presents a fast method for electromagnetic scattering and radiation problems pertinent to three-dimensional (3D) buried objects. In this approach, a new symmetrical form of the Green's function is derived, which can reduce the number of Sommerfeld integrals involved in the buried objects problem. The integration along steepest descent paths and leading-order approximations are introduced to evaluate these Sommerfeld integrals, which can greatly accelerate the computation. Based on the fast evaluation of Sommerfeld integrals, the radiation of an arbitrarily oriented electric dipole buried in a half space is first analyzed and computed. Then, the scattering by buried dielectric objects and conducting objects is considered using the method of moments (MOM). Numerical results show that the fast method can save tremendous CPU time in radiation and scattering problems involving buried objects.

A fast algorithm for electromagnetic scattering by buried three dimensional (3-D) dielectric objects of large size is presented by using the conjugate gradient (CG) method and fast Fourier transform (FFT). In this algorithm, the Galerkin method is utilized to discretize the electric field integral equations, where rooftop functions are chosen as both basis and testing functions. Different from the 3-D objects in homogeneous space, the resulting matrix equation for the buried objects contains both cyclic convolution and correlation terms, either of which can be solved rapidly by the CG-FFT method. The near-scattered field on the observation plane in the upper space has been expressed by two-dimensional (2-D) discrete Fourier transforms (DFTs), which also can be rapidly computed. Because of the use of FFTs to handle the Toeplitz matrix, the Sommerfeld integrals' evaluation which is time consuming yet essential for the buried object problem, has been reduced to a minimum. The memory required in this algorithm is of order N (the number of unknowns), and the computational complexity is of order N/sub iter/N log N, in which N/sub iter/ is the iteration number, and N/sub iter//spl Lt/N is usually true for a large problem.

A general procedure is developed for the analysis of electromagnetic (EM) scattering by multiple three-dimensional (3D) dielectric and/or conducting objects buried under one-dimensional (1D) multilayered media. In this first part of a two-part paper, general closed-form formulations for the electric fields excited by an arbitrarily oriented electric dipole under the layered media are first presented, from which electric-field integral equations for the buried dielectric objects, pure conducting objects, and their combinations are then obtained, and the scattered electric fields in the upper space are formulated. Finally, the physical significance of the above formulations is discussed. In the second part, numerical implementations for these integral equations and the scattered fields are investigated.

For pt.I see ibid., vol.36, no.2, p.526-34 (1998). In part I of this paper, coupled electric-field integral equations for dielectric objects, conducting objects, and multiple dielectric and/or conducting objects were derived when they were buried under one-dimensional (1D) multilayered media. In part II of this paper, numerical implementations for these integral equations are developed by use of the method of moments, in which the "self-actions" in the method are treated special because of the presence of singularity. Sample numerical results are presented for several cases of interest, which show the validity of the scheme.

A nonlinear differential equation for the reflection coefficient of a pure conducting medium is derived by using a microwave networking technique, which can be approximately solved by a nonlinear renormalization method. From the renormalization solution of the reflection coefficient, a novel inverse scattering formula to the conductivity profile is further investigated in the closed form. Reconstruction examples show that the novel formula is more accurate than the results in D.B. Ge et al. (1991) and T.J. Cui et al. (1992).>