Reconfigurable intelligent surface (RIS)-enabled systems have been widely considered as one of the revolutionary technologies for the new generation of communications. In particular, RIS-assisted multiple-input multiple-output (MIMO) communications have come at the forefront of research, yet there is still lack of supportive channel measurements and modeling in real environments. Against this background, this paper conducts multi-frequency and multi-scenario channel measurements and channel modeling for RIS-assisted MIMO communication systems. Utilizing a temporal autocorrelation-based channel sounder and the fabricated RISs, the virtual RIS-assisted MIMO channels are realized by successively moving the transceiver antennas on movable rotary tables. The channel realizations are collected in indoor hotspot (InH) and urban microcellular (UMi) scenarios at sub-6 GHz and millimeter-wave (mmWave) frequency bands, respectively, where various communication states, different coding schemes of the RIS, as well as with and without RIS deployment are fully considered. Based on the measured channel realizations, critical channel metrics including the signal power, effective rank, spectral efficiency (SE), root-mean-square delay-spread (RMS DS), RiceanK-factor (KF), spatial correlation, etc., are illustrated and compared under different coding schemes, communication states, deployment scenarios, and frequency bands. The measurement results indicate that the coding scheme of the RIS significantly impacts the channel performance, while the capability of RIS to customize the channel is strongly related to the power intensity it provides. Deploying an RIS with energy-focused coding can provide significant signal power gains for non-line-of-sight (NLoS) links and spatial multiplexing gains for line-of-sight (LoS) and obstructed-line-of-sight (OLoS) links, thereby greatly improving the SE. Moreover, it is found that under different communication states, such as RIS-assisted NLoS/LoS/OLoS links, the influence imposed by RIS on the channel metrics could be completely opposite, especially for the KF, RMS DS, and effective rank. In addition, after the RIS deployment with a beamforming mode, the spatial correlation in the NLoS and LoS MIMO channels significantly increases and decreases, respectively. Furthermore, it is verified that the Weichselberger model can provide a satisfactory prediction accuracy on the SE of RIS-assisted MIMO channels, while the Kronecker model underestimates it.

This paper presents channel measurement campaigns and spatial channel characterization of reconfigurable intelligent surface (RIS)-assisted millimeter-wave multiple-input multiple-output (MIMO) systems. Utilizing a channel sounder and an RIS, the RIS-assisted MIMO channels are constructed in an indoor non-line-of-sight scenario. By rotating a narrow-beam directional antenna (DA) in the azimuth angle domain, the spatial signal distributions are captured. Meanwhile, multiple comparative experiments, including: wideband vs narrowband (NB) and DA vs omnidirectional antenna, are conducted. The influence of RIS deployment and different coding schemes is considered. Based on such channel realizations, spatial channel metrics, including the power azimuth spectrum (PAS), root mean square angular spread (RMS AS), root mean square delay spread (RMS DS), spatial correlation coefficient (SCC), effective rank, and etc., are thoroughly investigated. Measurement results show that, using the DA, NB signal, and beamforming provided by the RIS contributes to a lower RMS DS, a higher SCC, and a lower effective rank. A truncated Laplacian function is used to describe the PAS, indicating prominent signal strength improvements in both the primary direction facing RIS and the opposite direction to RIS. The RMS ASs are well-fitted by a generalized extreme value distribution, which manifest a distance-dependent variation in the space domain.

Reconfigurable intelligent surface (RIS) has garnered significant attention in wireless communications due to its electromagnetic wave manipulation capabilities. In order to ensure the effectiveness of various transmission designs in practical environment, the channel characteristics of RIS-assisted wireless communication systems warrant thorough investigation. While substantial research has been conducted on large-scale channel characteristics, there remains a lack of measurement-based studies on channel small-scale characteristics. This paper conducts channel measurement campaigns, analyzing delay characteristics and multi-cluster properties in multiple indoor and outdoor environments, including square, corridor, and classroom scenarios. The measurement results indicate that the RIS-assisted path (i.e. the virtual line-of-sight (VLOS) path) makes the channels exhibit two-cluster characteristics analogous to conventional line-of-sight channels. Moreover, in classroom scenario, VLOS path generates reflected echoes that degrade the channel delay characteristics. Building on these findings, this paper proposes a small-scale channel modeling framework comprising a two-cluster-based channel impulse response (CIR) model and a K-factor-based CIR model. The simulation and measurement results demonstrate satisfactory agreement, validating the effectiveness and practicality of the proposed framework. Two types of CIR models that are unified with the traditional CIR model form, along with an optional echo component, make this framework flexible and easy to use. The proposed small-scale modeling framework can effectively characterize the power delay profiles of RIS-assisted channels, providing References for future research on RIS-assisted wireless communications.

Reconfigurable intelligent surfaces (RISs) are widely recognized as a cutting-edge technology for the sixth-generation mobile communication systems (6G). In this paper, we investigate the effective channel gain and spatial multiplexing gain of multi-RIS-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) channels. A channel model for multi-RIS-assisted MIMO system is introduced. We propose two discrete phase design methods for multi-RIS-assisted MIMO systems. The factors influencing spatial multiplexing gain, including the power difference and spatial similarity between different virtual line-of-sight (VLOS) paths, are thoroughly analyzed. Numerical simulations and measurement experiments are conducted in three scenarios, analyzing the impact of the RIS unit number as well as the positions of the RISs and antennas. The results indicate that jointly considering the position information of multiple RISs in discrete phase design leads to a higher effective channel gain, whereas relying only on the position of each single RIS exhibits instability. These findings highlight the positive role of sufficient location information and joint phase design in multi-RIS-assisted systems. Moreover, the results demonstrate that reduced power difference and spatial similarity among VLOS paths enhance the effective rank of the cascaded mmWave MIMO channel.

Reconfigurable intelligent surface (RIS) is regarded as one of the promising technologies for the sixth-generation mobile communication systems (6G) due to its ability to reshape the wireless channel and improve the communication transmission rate. In this paper, we investigate the spatial characteristic of an RIS-assisted millimeter wave (mmWave) multiple-input multipleoutput (MIMO) channel through channel measurement in practical environment. The channel measurement campaigns are carried out in an indoor hotspot (InH) non-line-of-sight (NLOS) scenario at 35 GHz. We compare and analyze the channel parameters in three propagation modes, including intelligent reflection with RIS (IRWR), specular reflection with RIS (SRWR), and without RIS (WR). Different types of receiver (RX) antenna are compared, including the horn antenna and the omni-directional antenna. Signals with bandwidths of 300 MHz and 10 MHz are transmitted to compare the spatial characteristics of wideband and narrowband signals. The measured channel frequency response (CFR) reveals that the RIS coding scheme, antenna type, and signal bandwidth are significant factors influencing the spatial correlation of the wireless channel. The measured power azimuth spectrum (PAS), fitted by truncated Laplacian functions, demonstrates that the deployment of RIS with energy-focused phase configuration results in an amplification of the power in the primary signal propagation direction. Moreover, the analysis of the root mean square (RMS) angular spread shows that it increases with the growing distance between the RX and RIS.

Reconfigurable intelligent surface (RIS) technology has gained wide recognition for its ability of enhancing the performance of wireless communication systems. This paper investigates the energy efficiency (EE) of a discrete phase shift RIS-assisted multi-cell communication system. Assuming that only statistical channel state information is available, we maximize the EE under a practical measured-based RIS power consumption model. Approximating the discrete measured-based RIS power consumption model by a continuous differentiable function, the problem is addressed by jointly optimizing the transmit beamforming at the base stations and the RIS phase shift matrix, by using fractional programming and complex circle manifold techniques. Simulation results demonstrate that the proposed algorithm can effectively achieve near-optimal EE performance and low RIS power consumption.

In recent years, reconfigurable intelligent surfaces (RISs) have attracted significant attention from both academia and industry due to their ability to manipulate electromagnetic waves in wireless communication. In particular, the characteristics of wireless channels incorporating RISs offer substantial research value. Several previous studies have investigated the path loss characteristics of RIS-assisted wireless channels in practical scenarios. However, these pioneering studies ignore the effect of RIS array gain on path loss. This paper proposes a floating-intercept (FI) path loss model that includes the array gain of the RIS, the distance between the transmitter and the RIS, and the distance between the receiver and the RIS. In this RIS-FI model, the RIS array gain component is related to the reflection coefficients of all the unit cells of RIS. The proposed model can effectively characterize path loss in both the far-field and near-field regions of the RIS. A fabricated 512-element RIS and a vector network analyzer (VNA) are utilized to set up the channel measurement system. The proposed RIS-FI path loss model is validated by using measurement data collected from multiple scenarios, including outdoor square, indoor corridor, and classroom. Compared to other RIS path loss models, the experimental results show that the proposed model offers better accuracy in describing path loss in the near-field region of the RIS. The RIS-FI path loss model presented in this paper may pave the way for further research on RIS-assisted wireless channels.

Reconfigurable intelligent surface (RIS)-assisted communications have received widespread attention recently, whose channel modeling is the cornerstone for its future practical application. Nevertheless, the RIS-related channel modeling stays at the level of either the theoretical free-space derivation or the end-to-end channel model, yet its general segment channel model applicable to real environment is still rarely reported. In this paper, we formulate a temporal segment channel model for RIS-assisted non-line-of-sight (NLOS) communications, which separates the global channel into three parts, i.e., transmitter (Tx)-RIS, RIS-receiver (Rx), and Tx-Rx. Then we bridge the relationship between the proposed model and the existing models. In addition, we illustrate the Ricean K–factor characteristics of the global channel and the sub-channel in the proposed model. The lower boundary for the Ricean K–factor of sub-channel is also deduced. Two low-complexity estimation algorithms for the Ricean K–factor of sub-channel are proposed, whose accuracy is verified by the measured channel data.

In mobile crowd sensing (MCS), complex tasks often require collaboration among multiple workers with diverse expertise and sensors. However, few studies consider the sensing time redundancy of multiple workers to complete a task collaboratively, and the subjective and objective collaboration willingness of participating workers in forming collaboration groups for different tasks. If solely focusing on enhancing workers’ willingness to collaborate, it cannot guarantee the minimum time redundancy within the collaboration group, resulting in a decrease in the group’s efficiency. Similarly, if only aiming to reduce sensing time redundancy among the workers in the collaboration group, it may lead to a loss of workers’ willingness to collaborate, and the diminished motivation among workers will consequently reduce the group’s efficiency. To address these challenges, this paper proposes EGC-STRO, a method for forming efficient collaboration groups in MCS that optimizes sensing time redundancy while balancing the workers’ cooperation willingness as constraints. First, this method proposes an evaluation indicator to select workers who meet their reward expectations, i.e., objective collaboration willingness, and uses an incentive mechanism based on bargaining game to maximize the overall interests. Furthermore, subjective collaboration willingness is defined and a collaboration worker selection algorithm is designed. The algorithm adds workers who meet both subjective and objective willingness requirements to the candidate set and selects workers with the smallest sensing redundancy time in the worker candidate set to join the final collaboration group. Simulation results demonstrate that compared with the baseline methods, our proposed EGC-STRO increases the worker engagement by about 5%-20%, increases the task coverage by 6%-25%, increases the platform utility by 17%-50%, and increases the worker utility by 20%-60%.

Mobile crowd sensing (MCS) has attracted extensive attention as a promising method for environmental sensing and data collection. However, due to the increasing computational tasks, bandwidth, and computing pressure, traditional “Cloud-Edge–End” MCS is insufficient to efficiently offload sensing data in real-time for data aggregation. Therefore, we consider a novel MCS framework based on the “Cloud-Enhanced-Edge–End,” the framework uses MCS idle users as edge nodes (ENs) to assist edge servers to enhance computing power and reduce delay and energy consumption. To achieve efficient data aggregation in the “Cloud-Enhanced-Edge–End” MCS framework, we first consider the multiobjective optimization of delay and energy consumption to establish a utility function. Second, addressing the shortcomings of traditional optimization algorithms, which often struggle with complex decision spaces and dynamic environments, we propose a MCS offloading (MCSOL) algorithm based on deep Q-network (DQN). The algorithm uses reinforcement learning to adaptively offload computing tasks to the optimal ENs or servers to maximize utility function to reduce delay and energy consumption. Our experimental results reveal that, in comparison with the other five strategies like only offloading data to base stations and ENs, MCSOL enhances data aggregation performance within the range of 30%–60%.

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.