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In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered for feedback evaluation at the UE. CSILaBS reduces over-the-air feedback overhead, improves CSI quality, and lowers the computation cost of UE. Besides, in a multiuser environment, we propose various mechanisms to select the feedback by exploiting PF while aiming to improve CSI accuracy. We also address various ML-based CPs, such as NeuralProphet (NP), an ML-inspired statistical algorithm. Furthermore, inspired to use a statistical model and ML together, we propose a novel hybrid framework composed of a recurrent neural network and NP, which yields better prediction accuracy than individual models. The performance of CSILaBS is evaluated through an empirical dataset recorded at Nokia Bell-Labs. The outcomes show that ML elimination at UE can retain performance gains, for example, precoding quality., Comment: 14 pages, 11 figures

We present a cross-comparison of three stop-flow configurations--such as low-pressure (LSF), high-pressure open-circuit (OC-HSF), and high-pressure short-circuit (SC-HSF) stop-flow--to rapidly bring a high flow velocity within a microchannel to a standstill. The average velocities inside the microchannels were reduced from > 1 m/s to < 10 um/s within 2s of initiating the stop-flow. The performance of the three stop-flow configurations was assessed by measuring the residual flow velocities within microchannels having three orders-of-magnitude different flow resistances. The LSF configuration outperformed the OC-HSF and SC-HSF configurations within the high flow resistance microchannel, and resulted in a residual velocity of < 10 um/s. The OC-HSF configuration resulted in a residual velocity of < 150 um/s within a low flow resistance microchannel. The SC-HSF configuration resulted in a residual velocity of < 200 um/s across the three orders-of-magnitude different flow resistance microchannels, and < 100 um/s for the low flow resistance channel. We hypothesized that the residual velocity resulted from the compliance in the fluidic circuit, which was further investigated by varying the elasticity of the microchannel walls and the connecting tubing. A numerical model was developed to estimate the expanded volumes of the compliant microchannel and connecting tubings under a pressure gradient and to calculate the distance traveled by the sample fluid. A comparison of the numerically and experimentally obtained traveling distances confirmed our hypothesis that the residual velocities were an outcome of the compliance in the fluidic circuit. Therefore, a configuration with minimal fluidic circuit compliance resulted in the least residual velocity.

In future solar cell technologies, the thermodynamic Shockley-Queisser limit for solar-to-current conversion in traditional p-n junctions could potentially be overcome with a bulk photovoltaic effect by creating an inversion broken symmetry in piezoelectric or ferroelectric materials. Here, we unveiled mechanical distortion-induced bulk photovoltaic behavior in a two-dimensional material (2D), MoTe2, caused by phase transition and broken inversion symmetry in MoTe2. The phase transition from single-crystalline semiconducting 2H-MoTe2 to semi-metallic 1T-MoTe2 was confirmed using X-ray photoelectron spectroscopy (XPS). We used a micrometer-scale system to measure the absorption of energy, which reduced from 800 meV to 63 meV when phase transformation from hexagonal to distorted octahedral and revealed a smaller bandgap semi-metallic behavior. Experimentally, a large bulk photovoltaic response is anticipated with the maximum photovoltage VOC = 16 mV and a positive signal of the ISC = 60 uA (400 nm, 90.4 Wcm-2) in the absence of an external electric field. The maximum values of both R and EQE were found to be 98 mAW-1 and 30 %, respectively. Our findings unveil distinctive features of the photocurrent responses caused by in-plane polarity and its potential from a wide pool of established TMD-based nanomaterials, and a novel approach to reach high efficiency in converting photons-to-electricity for power harvesting optoelectronics devices.

This paper report on the design of an experiment to establish an experimental self-similar adverse pressure gradient (APG) turbulent boundary layer (TBL) flow at the verge of separation in order to characterise its statistical structure and the contribution of the Reynolds stresses to the wall shear stress via quadrant analysis decomposition in conjunction with the Renard & Deck (RD) decomposition of the mean skin friction. In this study high-spatial-resolution (HSR) two-component - two-dimensional (2C-2D) particle image velocimetry (PIV) is employed to measure the self-similar APG-TBL flow. The measurements of the self-similar APG-TBL at the verge of separation, which is referred to as 'strong APG-TBL' is compared with similar HSR 2C-2D PIV measurements of a TBL under zero and mild APG to characterise the effect of the APG on the TBL. In the strong APG-TBL, the outer scaled profiles of the first- and second-order statistics, as well as the defect velocity exhibit self-similarity over the measured streamwise domain of 3.3{\delta}. The quadrant decomposition of the velocity fluctuations shows that the formation of the outer peak in the Reynolds shear stress profile under APG is due to the energisation of sweep motions of high-momentum fluid in the outer region of the boundary layer. The RD decomposition of the mean skin friction shows that the skin friction has larger contributions from ejections than sweeps. This is due to the emergence of the outer peaks with a correspondingly diminishing of the inner peaks in the turbulence production profiles. The skewness and flatness profiles show that the zero skewness and minimum flatness locations of the streamwise and wall-normal velocity fluctuations collapse with the location of the maximum Reynolds streamwise stress in the TBLs irrespective of the pressure gradient imposed on the TBL.

Massive multiple-input multiple-output (mMIMO) regime reaps the benefits of spatial diversity and multiplexing gains, subject to precise channel state information (CSI) acquisition. In the current communication architecture, the downlink CSI is estimated by the user equipment (UE) via dedicated pilots and then fed back to the gNodeB (gNB). The feedback information is compressed with the goal of reducing over-the-air overhead. This compression increases the inaccuracy of acquired CSI, thus degrading the overall spectral efficiency. This paper proposes a computationally inexpensive machine learning (ML)-based CSI feedback algorithm, which exploits twin channel predictors. The proposed approach can work for both time-division duplex (TDD) and frequency-division duplex (FDD) systems, and it allows to reduce feedback overhead and improves the acquired CSI accuracy. To observe real benefits, we demonstrate the performance of the proposed approach using the empirical data recorded at the Nokia campus in Stuttgart, Germany. Numerical results show the effectiveness of the proposed approach in terms of reducing overhead, minimizing quantization errors, increasing spectral efficiency, cosine similarity, and precoding gain compared to the traditional CSI feedback mechanism.

Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance. However, it becomes challenging to acquire accurate CSI in a highly dynamic environment, mainly due to multi-path fading. Inaccurate CSI can deteriorate the performance, particularly of a massive multiple-input multiple-output (mMIMO) system. This paper adapts machine learning (ML) for CSI prediction. Specifically, we exploit time-series models of deep learning (DL) such as recurrent neural network (RNN) and Bidirectional long-short term memory (BiLSTM). Further, we use NeuralProphet (NP), a recently introduced time-series model, composed of statistical components, e.g., auto-regression (AR) and Fourier terms, for CSI prediction. Inspired by statistical models, we also develop a novel hybrid framework comprising RNN and NP to achieve better prediction accuracy. The proposed channel predictors (CPs) performance is evaluated on a real-time dataset recorded at the Nokia Bell-Labs campus in Stuttgart, Germany. Numerical results show that DL brings performance gain when used with statistical models and showcases robustness., Comment: 6

While the literature on RF fingerprinting-based authentication and key distillation is vast, the two topics have customarily been studied separately. In this paper, starting from the observation that the wireless channel is a composite, deterministic / stochastic process, we propose a power domain decomposition that allows performing the two tasks simultaneously. We devise intelligent pre-processing schemes to decompose channel state information (CSI) observation vectors into "predictable" and "unpredictable" components. The former, primarily due to large-scale fading, can be used for node authentication through RF fingerprinting. The latter, primarily due to small-scale fading, could be used for semantically secure secret key generation (SKG). To perform the decomposition, we propose: (i) a fingerprint "separability" criterion, expressed through the maximisation of the total variation distance between the empirical fingerprint measures; (ii) a statistical independence metric for observations collected at different users, expressed through a normalised version of the $d$-dimensional Hilbert Schmidt independence criterion (dHSIC) test statistic. We propose both explicit implementations, using principal component analysis (PCA) and kernel PCA and black-box, unsupervised learning, using autoencoders. Our experiments on synthetic and real CSI datasets showcase that the incorporation of RF fingerprinting and SKG, with explicit security guarantees, is tangible in future generations of wireless.

With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks. 6G networks will be immensely complex, requiring more deployment time, cost and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical (PHY) and link layers in cellular networks where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and future timeline on readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in the wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks., Comment: 6

Liquid crystals alignment on 2D materials is known due to their intrinsic van der Waals interaction. Here, we demonstrate that direction of electric field can tune the alignment of liquid crystal adsorbed on 2D surface. Due to degeneracy of alignment liquid crystal on hexagonal surface, it prefers to align in three multiple states. We utilized this concept and control the alignment of grains of chemical vapor deposition grown boron nitride (h-BN) to control the incident light. It was demonstrated that electric field can reorient the direction of liquid crystal alignment on Zigzag and armchair edges of h-BN. The ab initio calculations confirmed the favorable adsorption configurations of liquid crystal molecule on hexagonal boron nitride surface. This concept provides a pathway towards dynamic high-quality pixels with low power consumption and could define a new avenue in near-eye displays., Comment: arXiv admin note: text overlap with arXiv:1806.00531

The mushroom growth of cellular users requires novel advancements in the existing cellular infrastructure. One way to handle such a tremendous increase is to densely deploy terrestrial small-cell base stations (TSBSs) with careful management of smart backhaul/fronthaul networks. Nevertheless, terrestrial backhaul hubs significantly suffer from the dense fading environment and are difficult to install in a typical urban environment. Therefore, this paper considers the idea of replacing terrestrial backhaul network with an aerial network consisting of unmanned aerial vehicles (UAVs) to provide the fronthaul connectivity between the TSBSs and the ground core-network (GCN). To this end, we focus on the joint positioning of UAVs and the association of TSBSs such that the sum-rate of the overall system is maximized. In particular, the association problem of TSBSs with UAVs is formulated under communication-related constraints, i.e., bandwidth, number of connections to a UAV, power limit, interference threshold, UAV heights, and backhaul data rate. To meet this joint objective, we take advantage of the genetic algorithm (GA) due to the offline nature of our optimization problem. The performance of the proposed approach is evaluated using the unsupervised learning-based k-means clustering algorithm. We observe that the proposed approach is highly effective to satisfy the requirements of smart fronthaul networks., Comment: 14 pages, 9 figures, 2 tables, IEEE Transactions on Network Science and Engineering, 2021

Several calibration techniques have been proposed in the literature for the calibration of two-component two-dimensional (2C-2D) particle image velocimetry (PIV) and three-component two-dimensional (3C-2D) stereoscopic PIV (SPIV) systems. These techniques generally involve the use of a calibration target that is assumed to be at the exact centre of the laser sheet within the field of view (FOV), which in practice is very difficult to achieve. In 3C-2D SPIV, several methods offer different correction schemes based on the computation of a disparity map, which are aimed at correcting errors produced due to this misalignment. These techniques adjust the calibration of individual cameras to reduce the disparity error, but in doing so can create unintended errors in the measurement position and/or the velocity measurements, such as introducing a bias in the measured three-component (3-C) displacements. This paper introduces a novel method to ensure accurate alignment of the laser sheet with the calibration target so that the uncertainty in displacement measurements is less than or equal to the uncertainty inherent to the PIV and hence, no correction scheme is required. The proposed method has been validated with a simple experiment in which true displacements are given to a particle container (illuminated by an aligned laser sheet) and the measured 3C displacements are compared with the given true displacements. An uncertainty of less than 7.6 micrometres (equivalent to 0.114 pixels) in the measured 3C displacements demonstrates the effectiveness of the new alignment method and eliminates the need for any ad hoc post-correction scheme., Comment: This paper is under review in the journal "Experiments in Fluids"

High-spatial-resolution (HSR) two-component, two-dimensional particle-image-velocimetry (2C-2D PIV) measurements of a zero-pressure-gradient (ZPG) turbulent boundary layer (TBL) and an adverse-pressure-gradient (APG)-TBL were taken in the LMFL High Reynolds number Boundary Layer Wind Tunnel. The ZPG-TBL has a momentum-thickness based Reynolds number $Re_{\delta_2} = \delta_2 U_e/\nu = 7,750$ while the APG-TBL has a $Re_{\delta_2} = 16,240$ and a Clauser's pressure gradient parameter $\beta = \delta_1 P_x/\tau_w = 2.27$ After analysing the single-exposed PIV image data using a multigrid/multipass digital PIV (Soria, 1996) with in-house software, proper orthogonal decomposition (POD) was performed on the data to separate flow-fields into large- and small-scale motions (LSMs and SSMs), with the LSMs further categorized into high- and low-momentum events. Profiles of the conditionally averaged Reynolds stresses show that the high-momentum events contribute more to the Reynolds stresses than the low-momentum between wall to the end of the log-layer and the opposite is the case in the wake region. The cross-over point of the profiles of the Reynolds stresses from the high- and low-momentum LSMs always has a higher value than the corresponding Reynolds stress from the original ensemble at the same wall-normal location. Furthermore, the cross-over point in the APG-TBL moves further from the wall than in the ZPG-TBL. By removing the velocity fields with LSMs, the estimate of the Reynolds streamwise stress and Reynolds shear stress from the remaining velocity fields is reduced by up to $42 \%$ in the ZPG-TBL. The reduction effect is observed to be even larger (up to $50\%$) in the APG-TBL. However, the removal of these LSMs has a minimal effect on the Reynolds wall-normal stress in both the ZPG- and APG-TBL., Comment: This paper is under consideration for publication in Experimental Thermal and Fluid Sciences

Motivated by the issue of inaccurate channel state information (CSI) at the base station (BS), which is commonly due to feedback/processing delays and compression problems, in this paper, we introduce a scalable idea of adopting artificial intelligence (AI) aided CSI acquisition. The proposed scheme enhances the CSI compression, which is done at the mobile terminal (MT), along with accurate recovery of estimated CSI at the BS. Simulation-based results corroborate the validity of the proposed scheme. Numerically, nearly 100\% recovery of the estimated CSI is observed with relatively lower overhead than the benchmark scheme. The proposed idea can bring potential benefits in the wireless communication environment, e.g., ultra-reliable and low latency communication (URLLC), where imperfect CSI and overhead is intolerable.

In wireless communication, accurate channel state information (CSI) is of pivotal importance. In practice, due to processing and feedback delays, estimated CSI can be outdated, which can severely deteriorate the performance of the communication system. Besides, to feedback estimated CSI, a strong compression of the CSI, evaluated at the user equipment (UE), is performed to reduce the over-the-air (OTA) overhead. Such compression strongly reduces the precision of the estimated CSI, which ultimately impacts the performance of multiple-input multiple-output (MIMO) precoding. Motivated by such issues, we present a novel scalable idea of reporting CSI in wireless networks, which is applicable to both time-division duplex (TDD) and frequency-division duplex (FDD) systems. In particular, the novel approach introduces the use of a channel predictor function, e.g., Kalman filter (KF), at both ends of the communication system to predict CSI. Simulation-based results demonstrate that the novel approach reduces not only the channel mean-squared-error (MSE) but also the OTA overhead to feedback the estimated CSI when there is immense variation in the mobile radio channel. Besides, in the immobile radio channel, feedback can be eliminated, which brings the benefit of further reducing the OTA overhead. Additionally, the proposed method provides a significant signal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly mobile and immobile.

In the past decade, advances in electronics technology have made larger imaging sensors available to the experimental fluid mechanics community. These advancements have enabled the measurement of 2-component 2-dimensional (2C-2D) velocity fields using particle image velocimetry (PIV) with much higher spatial resolution than previously possible. However, due to the large size of the sensor, the lens distortion needs to be taken into account as it will now have a more significant effect on the measurement quality that must be corrected to ensure accurate high-fidelity 2C-2D velocity field measurements. In this paper, two dewarping models, a second-order rational function (R2) and a bicubic polynomial (P3) are investigated with regards to 2C-2D PIV measurements of a turbulent boundary layer (TBL) using a large imaging sensor. Two approaches are considered and compared: (i) dewarping the images prior to the PIV cross-correlation analysis and (ii) undertaking the PIV cross-correlation analysis using the original recorded distorted images then followed by using the mapping functions derived for image dewarping to provide the correct spatial location of the velocity measurement point. The results demonstrate that the use of P3 dewarping model to correct lens distortion yields better results than the R2 dewarping model. Furthermore, both approaches for the P3 dewarping model yield results which are statistically indistinguishable.

Database replication is an important component of reliable, disaster tolerant and highly available distributed systems. However, data replication also causes communication and processing overhead. Quantification of these overheads is crucial in choosing a suitable DBMS form several available options and capacity planning. In this paper, we present results from a comparative empirical analysis of replication activities of three commonly used DBMSs - MySQL, PostgreSQL and Cassandra under text as well as image traffic. In our experiments, the total traffic with two replicas (which is the norm) was as much as $300$\% higher than the total traffic with no replica. Furthermore, activation of the compression option for replication traffic, built into MySQL, reduced the total network traffic by as much as $20$\%. We also found that average CPU utilization and memory utilization were not impacted by the number of replicas or the dataset.

The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a smart vehicular system as it is beneficial to drive safely. Present vehicular system lacks data reliability, security, and easy deployment. Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. Nevertheless, an increase in the number of operating drones in the communication networks makes them more vulnerable towards the cyber-attacks, which can completely sabotage the communication infrastructure. To tackle these problems, we propose a blockchain-based registration and authentication system for the entities such as drones, smart vehicles (SVs) and roadside units (RSUs). This paper is mainly focused on the blockchain-based secure system design and the optimal placement of drones to improve the spectral efficiency of the overall network. In particular, we investigate the association of RSUs with the drones by considering multiple communication-related factors such as available bandwidth, maximum number of links a drone can support, and backhaul limitations. We show that the proposed model can easily be overlaid on the current vehicular network reaping benefits of secure and reliable communications.