Passive Reflection Research Articles

Mindfulness Meditation and Placebo Modulate Distinct Multivariate Neural Signatures to Reduce Pain

BackgroundRather than a passive reflection of nociception, pain is shaped by the interplay between one’s experiences, current cognitive-affective states, and expectations. The placebo response, a paradoxical yet reliable phenomenon, is postulated to reduce pain by engaging mechanisms shared with active therapies. It has been assumed that mindfulness meditation, practiced by sustaining nonjudgmental awareness of arising sensory events, merely reflects mechanisms evoked by placebo. Recently, brain-based multivariate pattern analysis has been validated to successfully disentangle nociceptive-specific, negative affective, and placebo-based dimensions of the subjective pain experience. MethodsTo determine whether mindfulness meditation engages distinct brain mechanisms from placebo and sham mindfulness to reduce pain, multivariate pattern analysis pain signatures were applied across 2 randomized clinical trials that employed overlapping psychophysical pain testing procedures (49 °C noxious heat; visual analog pain scales) and distinct functional magnetic resonance imaging techniques (blood oxygen level–dependent; perfusion based). After baseline pain testing, 115 healthy participants were randomized into a 4-session mindfulness meditation (n = 37), placebo-cream conditioning (n = 19), sham mindfulness meditation (n = 20), or book-listening control (n = 39) intervention. After each intervention, noxious heat was administered during functional magnetic resonance imaging and each manipulation. ResultsA double dissociation in the multivariate pattern analysis signatures supporting pain regulation was revealed by mindfulness meditation compared with placebo cream. Mindfulness meditation produced significantly greater reductions in pain intensity and pain unpleasantness ratings and nociceptive-specific and negative affective pain signatures than placebo cream, sham mindfulness meditation, and control interventions. The placebo-cream group significantly lowered the placebo-based signature. ConclusionsMindfulness meditation and placebo engaged distinct and granular neural pain signatures to reduce pain.

Robust Beamforming Design for an IRS-Aided NOMA Communication System With CSI Uncertainty

Intelligent reflecting surface (IRS) is a promising technology that provides high throughput in future communication systems and is compatible with various communication techniques, such as non-orthogonal multiple-access (NOMA). This paper studies the downlink transmission of IRS-assisted NOMA communication, considering the practical case of imperfect channel state information (CSI). Aiming to maximize the system sum rate, a robust IRS-aided NOMA design is proposed to jointly find the optimal beamforming vector for the access point and the passive reflection matrix for the IRS. This robust design is realised using the penalty dual decomposition (PDD) scheme, and it is shown that the results have a close performance to their upper bound obtained from the corresponding perfect CSI scenario. The presented method is compatible with both continuous and discrete phase shift elements of the IRS. Our findings show that the proposed algorithms, for both continuous and discrete IRS, have low computational complexity compared to other schemes in the literature. Furthermore, we conduct a performance comparison between the IRS-aided NOMA and the IRS-aided orthogonal multiple access (OMA). This comparison shows that robust beamforming techniques are crucial for the system to reap the advantages of IRS-aided NOMA communication in the presence of CSI uncertainty.

Passive Reflection Codebook Design for IRS-Integrated Access Point

Passive Reflection Codebook Design for IRS-Integrated Access Point

Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging

Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress conditions (photoinhibition). In addition, strong photoinhibitory lighting itself can cause stress. Until now, it is unknown how light history influences the physiologically meaningful spectral indices of reflected light. In the present work, shifts in the spectral reflectance characteristics of Ficus elastica leaves caused by 10 h exposure to photoinhibitory white LED light, 200 μmol photons m−2 s−1 (light stress), and moderate natural light, 50 μmol photons m−2 s−1 (shade) are compared to dark-adapted plants. Measurements were performed with a Cubert UHD-185 hyperspectral camera in discrete spectral bands centred on wavelengths from 450 to 950 nm with a 4 nm step. It was shown that light stress leads to an increase in reflection in the range of 522–594 nm and a decrease in reflection at 666–682 nm. The physiological causes of the observed spectral shifts are discussed. Based on empirical data, the light-stress index (LSI) = mean(R666:682)/mean(R552:594) was calculated and tested. The data obtained suggest the possibility of identifying plant light stress using spectral sensors that remotely fix passive reflection with the need to take light history into account when analysing hyperspectral data.

Digital formation processes: A high-frequency, large-scale investigation

Large sources of digital trace data (i.e. “Big Data”) have become increasingly important in the study of material culture. However, akin to the offline material culture traditionally studied by archaeologists, digital trace data is rarely a passive reflection of human behavior – it is a complex palimpsest produced through a variety of erasure and accretion formation processes. To better understand how digital trace palimpsests are formed and how digital formation processes influence and inform our ability to interpret the offline material processes they index, we introduce a computational method – high-frequency archaeological survey – which allows us to observe digital formation processes at a high temporal resolution, as well as a large spatial scale. Using this method every hour for one month, we surveyed posts from across the United States in Craigslist's “Free Stuff” category (popularly called “Curb Alert”), a user-generated source of big digital trace data, indexing material things that have been placed on users' curbs for removal by scavengers or trash collectors. For each post, we observed its time-to-erasure and any edits that were made during the study period – finding that the posts that survive represent a biased sample of those that were posted over the course of the month, conditioned by how recently and on what day the post is posted, the material characteristics of things that are posted about, as well as regional variation. Far from only being evidence of biased end-of-month data, however, we show that further analysis of identified digital formation processes can be an important object of study in its own right – in this case, shedding new light on social scientific questions linking the exchange of “free stuff” with the process of social stratification and urban inequality in the United States. Overall, our findings suggest the importance of accounting for and explicitly analyzing digital formation processes in studies that utilize digital trace data.

Fundamental Limits of Intelligent Reflecting Surface Aided Multiuser Broadcast Channel

Intelligent reflecting surface (IRS) has recently received significant attention in wireless networks owing to its ability to smartly control the wireless propagation through passive reflection. Although prior works have employed the IRS to enhance the system performance under various setups, the fundamental capacity limits of an IRS aided multi-antenna multi-user system have not yet been characterized. Motivated by this, we investigate an IRS aided multiple-input single-output (MISO) broadcast channel by considering the capacity-achieving dirty paper coding (DPC) scheme and dynamic beamforming configurations. We first propose a bisection based framework to characterize its capacity region by optimally solving the sum-rate maximization problem under a set of rate constraints, which is also applicable to characterize the achievable rate region with the zero-forcing (ZF) scheme. Interestingly, it is rigorously proved that dynamic beamforming is able to enlarge the achievable rate region of ZF if the IRS phase-shifts cannot achieve fully orthogonal channels, whereas the attained gains become marginal due to the reduction of the channel correlations induced by smartly adjusting the IRS phase-shifts. The result implies that employing the IRS is able to reduce the demand for implementing dynamic beamforming. Finally, we analytically prove that the sum-rate achieved by the IRS aided ZF is capable of approaching that of the IRS aided DPC with a sufficiently large IRS in practice. Simulation results shed light on the impact of the IRS on transceiver designs and validate our theoretical findings, which provide useful guidelines to practical systems by indicating that replacing sophisticated schemes with easy-implementation schemes would only result in slight performance loss.

Passive Reflection Optimization for IRS-Aided Multicast Beamforming With Discrete Phase Shifts

Intelligent reflecting surface (IRS) can bring promising advantages to enhance the communication system performance by reconfiguring wireless channels dynamically. The design of passive IRS reflection has attracted high attention and most existing works have considered continuous phase shift for IRS reflection. However, this is difficult to realize in practice due to hardware limitations and discrete phase shifts are applied for practical IRSs instead. In this letter, the information multicast beamforming problem in an IRS-aided multi-user system is considered, where the minimum receive signal-to-noise-ratio among the users is maximized by optimizing the IRS reflections with discrete phase shifts. By leveraging semi-definite relaxation (SDR) and gradient descent/ascent (GDA), two solutions are presented with high/low computational complexity, respectively. Simulation results show the efficacy of the proposed GDA-based algorithm as compared to the SDR-based and other benchmark schemes.

Integrating Intelligent Reflecting Surface Into Base Station: Architecture, Channel Model, and Passive Reflection Design

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Knowledge and Learning Con-Validation Processes

Educational research on learning models highlights the need to activate awareness in the individual to assess knowledge and its function. This presupposes that the very knowledge does not have the character of a passive reflection of an already existing order of things. Rather, it is a question of recognizing in the individual the dynamic identity between learning and acting. In this context of investigation, it may be useful to revisit some fundamental lines of thinking expressed by classical American pragmatism, according to which human being is essentially action. Hence, everyone at the same time, is responsible to contributing to the production of a situational truth and to its con-validating. In this sense, the function of knowing as learning cannot be separated from its connection with a social praxis.

Robust Beamforming for IRS-Aided Multi-Cell mmWave Communication Systems

Intelligent reflecting surface (IRS) has recently been envisioned to enhance the power of the desired received signal or suppress the interference signal by deploying low-cost passive reflection elements. This paper investigates an IRS-aided multi-cell millimeter wave (mmWave) communication system for suppressing inter-cell interference (ICI) to assist the downlink transmission of cell-edge users. We aim for maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) through jointly optimizing the active beamforming vectors of mmWave base stations (MBSs), the phase shifts of the IRS, and the location of the IRS. Especially since it is challenging to obtain the perfect channel state information (CSI) related to the IRS links, we also study the performance of IRS-aided mmWave communication in the case of imperfect CSI. First, in the case of perfect CSI, to tackle the challenging and non-convex minimum weighted SINR maximization problem, we develop an alternating optimization (AO)-based beamforming algorithm via updating the active beamforming vectors at MBSs, the phase shifts at the IRS, and the location of the IRS alternately. Then, in the case of imperfect CSI, we propose a low-complexity majorization-minimization (MM)-based robust beamforming algorithm to obtain the benchmark performance. Moreover, the proposed algorithms are also extended to multi-IRS-aided multi-cell mmWave scenarios. Finally, the simulation results demonstrate the advantages in terms of the SINR, effective sum rate as well as energy efficiency after introducing the IRS to mitigate the ICI.

Adaptive resource allocation of secured access to intelligent surface enhanced satellite–terrestrial networks with two directional traffics

In this paper, we study the secured access to the satellite–terrestrial networks of 6G with two directional traffics. Both access point (AP) and the sensor node in the terrestrial networks simultaneously transmit messages to each other. The passive reflection surface technique is employed to assist both directional traffics. Meanwhile, the satellite as the eavesdropper is keeping on receiving both messages by equipping multiple antennas on this illegal receiver. Specifically, we formulate the adaptive power design problem for both AP and the sensor node, respectively, as the transmitter of the downlink and the uplink traffic in terrestrial networks. The objective function is the energy efficiency maximization and the constraint is the maximum overall data rate of the illegal eavesdropper over the satellite networks. The closed form expression is mathematically derived for the optimum solution. The obtained approach achieves the best performance for the terrestrial networks from the energy efficiency point of view. Moreover, it assures any quality of service requirement concerning the security of the whole satellite–terrestrial networks of 6G. Our approach is numerically verified by the Monte Carlo simulations.

Three-Dimensional Processing of Reflections for Passive-Source Seismology Based on Geometric Design

Passive-source exploration is a method of seismic exploration that has loose requirements on the conditions of the surface, is cheap, and does not require excitation by an active source. The ambient seismic signals collected from the field over an extended period of time can be used to generate virtual-shot seismic records similar to those obtained from the seismic exploration of an active source based on the relevant correlations, and this can in turn yield information on the underground structure through a series of conventional methods of processing seismic data. Three-dimensional (3D) processing can mitigate the influence of the azimuth of random noise to yield a more representative underground structure, but requires intensive computation. In this paper, we propose a 3D method to compute reflections of a passive source based on the geometry of seismic exploration. Assuming a high quality of imaging, we use information on the predesigned geometry to choose and correlate noisy synthetic data on the reflections by a seismic body to create virtual shot data, and subsequently capture images of the 3D data on passive reflection. The use of the predesigned geometry ensures that only the important and useful parts of the dataset are used for correlation and imaging, where this reduces the cost of computation. The proposed method can thus efficiently generate high-quality 3D synthetic data.

Security analysis and secured access design for networks of image remote sensing

The secured access is studied in this paper for the network of the image remote sensing. Each sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection point. In order to enhance the sensing quality for the remote uploading, the passive reflection surface technique is employed. If one eavesdropper that exists nearby this sensor is keeping on accessing the same networks, he may receive the same image from this sensor. Our goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing images. In order to achieve this goal, the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading coefficients. Based on this theoretical result, the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper sensor. Finally, the analytical expression is theoretically derived for the optimum uploading power. Numerical simulations verify the design approach.

Deep learning-based energy harvesting with intelligent deployment of RIS-assisted UAV-CFmMIMOs

The ever-evolving internet of things (IoT) has spawned hundreds of wireless sensors that communicate via the internet infrastructure. The lifetime and self-sustainability of these sensors are pivotal factors dictating the performance of respective application infrastructure. Radio frequency energy harvesting (RFEH) technology has exhibited the capability of effectively augmenting the battery lifetime of these sensors. In this work, we introduce a novel framework called CURe, which combines the advantages of cell-free massive multiple-input multiple-output (CFmMIMO) and reconfigurable intelligent surfaces (RISs) to provide uninterrupted energy harvesting for IoT devices through RFEH. CFmMIMO integrates the advantages of distributed systems and massive MIMO, while RIS improves the signal strength of the information transfer and RFEH via its passive reflection capabilities. Moreover, we consider unmanned aerial vehicles (UAVs) equipped with CFmMIMO as mobile access points (APs) to better serve the moving sensory devices. To further enhance RFEH, we propose DeNCE, a channel estimation technique based on deep learning (DL) that eliminates the need for traditional closed-form equation-based channel estimation methods. Through evaluation, we first validate the performance of CURe by comparing it with the modified bisection search for max–min fairness (MBS-MMF) algorithm and later corroborate that DeNCE significantly improves the performances of both models. Finally, to optimize the UAV deployment and ensure continuous RFEH coverage, we propose dARL, a deep reinforcement learning (DRL)-based scheduling framework that enables UAV-CFmMIMO swarms to perform continuous energy harvesting in the coverage area collaboratively.

Intelligent Reflecting Surfaces for Sum-Rate Maximization in Cognitive Radio Enabled Wireless Powered Communication Network

In this paper, we study a cognitive radio (CR)-enabled wireless powered communication network (WPCN) with an intelligent reflecting surface (IRS). A cognitive wireless powered communication network (CWPCN) consisting of one secondary wireless powered communication network, in which a hybrid access point broadcasts wireless energy to multiple users on downlink and receives information signals on uplink, shares the same spectrum with the existing primary wireless communication network. In this paper, we consider an underlay CWPCN in which the secondary network is regulated by a given interference temperature constraint (ITC) such that interference with the primary network is kept no greater than a predefined threshold. To enhance the performance of the CWPCN, we consider an IRS-assisted network in which multiple passive reflection elements are configured to reflect the signals in any desired phase/direction. Our main goal is to maximize the sum throughput of secondary users while managing the interference constraint. For this, we jointly optimize the uplink and downlink phase shift matrices of the IRS elements with optimal time slots for wireless energy transfer (WET) on downlink and wireless information transfer on uplink. In finding the optimal solution, the formulated optimization problem is non-convex, complex, and intractable. In this paper, we propose an alternating optimization (AO)-based solution with a successive convex approximation (SCA) technique. We show through simulation results that the proposed IRS-assisted network provides a significant enhancement in performance over the conventional CWPCN.

Over-the-air beamforming with reconfigurable intelligent surfaces

Reconfigurable intelligent surface (RIS)-empowered communication is a revolutionary technology that enables to manipulate wireless propagation environment via smartly controllable low-cost reflecting surfaces. However, in order to outperform conventional communication systems, an RIS-aided system with solely passive reflection requires an extremely large surface. To meet this challenge, the concept of active RIS, which performs simultaneous amplification and reflection on the incident signal at the expense of additional power consumption, has been recently introduced. In this paper, deploying an active RIS, we propose a novel beamforming concept, over-the-air beamforming, for RIS-aided multi-user multiple-input single-output (MISO) transmission schemes without requiring any pre/post signal processing hardware designs at the transmitter and receiver sides. In the proposed over-the-air beamforming-based transmission scheme, the reflection coefficients of the active RIS elements are customized to maximize the sum-rate gain. To tackle this issue, first, a non-convex quadratically constrained quadratic programming (QCQP) problem is formulated. Then, using semidefinite relaxation (SDR) approach, this optimization problem is converted to a convex feasibility problem, which is efficiently solved using the CVX optimization toolbox. Moreover, taking inspiration from this beamforming technique, a novel high-rate receive index modulation (IM) scheme with a low-complexity sub-optimal detector is developed. Through comprehensive simulation results, the sum-rate and bit error rate (BER) performance of the proposed designs are investigated.

Reconfigurable Intelligent Surface-Assisted Backscatter Communication: A New Frontier for Enabling 6G IoT Networks

Backscatter Communication (BackCom), which is based on passive reflection and modulation of an incident radio-frequency (RF) wave, has emerged as a cutting-edge technological paradigm for self-sustainable Internet-of-things (IoT). Nevertheless, contemporary BackCom systems are limited to short-range and low data rate applications only, rendering them insufficient on their own to support pervasive connectivity among the massive number of IoT devices. Meanwhile, wireless networks are rapidly evolving toward the smart radio paradigm. In this regard, reconfigurable intelligent surfaces (RISs) have come to the forefront to transform the wireless propagation environment into a fully controllable and customizable space in a cost-effective and energy-efficient manner. Targeting the sixth-generation (6G) horizon, we anticipate the integration of RISs into BackCom systems as a new frontier for enabling 6G IoT networks. In this article, for the first time in the open literature, we provide a tutorial overview of RIS-assisted BackCom (RIS-BackCom) systems. Specifically, we introduce the three different variants of RIS-BackCom and identify the potential improvements that can be achieved by incorporating RISs into BackCom systems. In addition, owing to the unrivaled effectiveness of non-orthogonal multiple access (NOMA), we present a case study on a RIS-assisted NOMA-enhanced BackCom system. Finally, we outline the way forward for translating this disruptive concept into real-world applications.

Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning

This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic traffic devices. First of all, this paper proposes a novel joint active device separation (the message recovery of active device) and channel estimation architecture for the RIS-aided URA. Specifically, the RIS passive reflection is optimized before the successful device separation. Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences. However, the inherent coupling among multiple sparse device-RIS channels, together with the unknown number of active devices make the detection problem at hand deviate from the widely-used coupled tensor decomposition format. To overcome this challenge, this paper judiciously devises a probabilistic model that captures both the element-wise sparsity from the angular channel model and the low-rank property due to the sporadic nature of URA. Then, based on such a probabilistic model, a iterative detection algorithm is developed under the framework of sparse variational inference, where each update iteration is obtained in a closed-form and the number of active devices can be automatically estimated for effectively avoiding the overfitting of noise. Extensive simulation results confirm the excellence of the proposed URA algorithm, especially for the case of a large number of reflecting elements for accommodating a significantly large number of devices.

Microfluidic-Blow-Spinning fabricated sandwiched structural fabrics for All-Season personal thermal management

The concept of personal thermal management (PTM) has successfully been proven in heating and cooling fabrics, which can not only improve human comfort, but also reduce energy consumption. However, it remains a huge challenge to integrate the two opposite functions of heating and cooling in the same fabric by adaptive thermal management. Herein, a sandwiched structural fabric featuring both passive reflection cooling derived from photonic crystals (PCs) layer and active heating rooted in nano-Ag layer are generated by adjusting photonic band gap position and applied voltage changes. The passive reflection cooling of sandwiched structural fabric offers improved effect over the normal cotton, and the high absolute temperature difference reaches 7.9 °C. Besides, when a low voltage of 3 V is applied, the extra heating temperature difference of Joule heating realizes 18.1 °C due to the good electrical conductivity of nano-Ag. Moreover, we have successfully constructed a wearable PTM device to adjust temperature of human body by using this sandwiched structural fabric with excellent flexibility, breathability and high mechanical strength. Therefore, this strategy presents an easy-to-manipulate platform for the fabrication of sandwiched structural fabrics and provides new insights for PTM applications.

Multi-IRS-Assisted mmWave MIMO Communication Using Twin-Timescale Channel State Information

To reduce the computational complexity and channel estimation overhead for multi-intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication, we consider a joint design of the hybrid precoders at the base station and the passive precoders at the IRSs to maximize the ergodic spectral efficiency by exploiting the twin-timescale channel state information (CSI). Specifically, the digital precoder is designed according to the instantaneous CSI of a reduced-dimensional assist channel matrix, while the IRS passive reflection coefficient matrices and the analog precoder are optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the twin timescale. This work proposes efficient algorithms to jointly design the precoders, where the update of the IRS reflection coefficient matrices is independent of the hybrid precoders and the design of the analog precoder is independent of the digital precoder. Simulation results demonstrate the effectiveness of the proposed algorithms and provide the application scenes of the fully-connected and subarray-connected architectures. The results also show that the ergodic spectral efficiency for the fully-connected architecture using the twin-timescale CSI can approach that using the existing CSI schemes with less channel estimation overhead and computational complexity.